Artificial Intelligence Essay for Students and Children

500+ words essay on artificial intelligence.

Artificial Intelligence refers to the intelligence of machines. This is in contrast to the natural intelligence of humans and animals. With Artificial Intelligence, machines perform functions such as learning, planning, reasoning and problem-solving. Most noteworthy, Artificial Intelligence is the simulation of human intelligence by machines. It is probably the fastest-growing development in the World of technology and innovation . Furthermore, many experts believe AI could solve major challenges and crisis situations.

Artificial Intelligence Essay

Types of Artificial Intelligence

First of all, the categorization of Artificial Intelligence is into four types. Arend Hintze came up with this categorization. The categories are as follows:

Type 1: Reactive machines – These machines can react to situations. A famous example can be Deep Blue, the IBM chess program. Most noteworthy, the chess program won against Garry Kasparov , the popular chess legend. Furthermore, such machines lack memory. These machines certainly cannot use past experiences to inform future ones. It analyses all possible alternatives and chooses the best one.

Type 2: Limited memory – These AI systems are capable of using past experiences to inform future ones. A good example can be self-driving cars. Such cars have decision making systems . The car makes actions like changing lanes. Most noteworthy, these actions come from observations. There is no permanent storage of these observations.

Type 3: Theory of mind – This refers to understand others. Above all, this means to understand that others have their beliefs, intentions, desires, and opinions. However, this type of AI does not exist yet.

Type 4: Self-awareness – This is the highest and most sophisticated level of Artificial Intelligence. Such systems have a sense of self. Furthermore, they have awareness, consciousness, and emotions. Obviously, such type of technology does not yet exist. This technology would certainly be a revolution .

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Applications of Artificial Intelligence

First of all, AI has significant use in healthcare. Companies are trying to develop technologies for quick diagnosis. Artificial Intelligence would efficiently operate on patients without human supervision. Such technological surgeries are already taking place. Another excellent healthcare technology is IBM Watson.

Artificial Intelligence in business would significantly save time and effort. There is an application of robotic automation to human business tasks. Furthermore, Machine learning algorithms help in better serving customers. Chatbots provide immediate response and service to customers.

introduction of artificial intelligence essay

AI can greatly increase the rate of work in manufacturing. Manufacture of a huge number of products can take place with AI. Furthermore, the entire production process can take place without human intervention. Hence, a lot of time and effort is saved.

Artificial Intelligence has applications in various other fields. These fields can be military , law , video games , government, finance, automotive, audit, art, etc. Hence, it’s clear that AI has a massive amount of different applications.

To sum it up, Artificial Intelligence looks all set to be the future of the World. Experts believe AI would certainly become a part and parcel of human life soon. AI would completely change the way we view our World. With Artificial Intelligence, the future seems intriguing and exciting.

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Artificial Intelligence Essay

500+ words essay on artificial intelligence.

Artificial intelligence (AI) has come into our daily lives through mobile devices and the Internet. Governments and businesses are increasingly making use of AI tools and techniques to solve business problems and improve many business processes, especially online ones. Such developments bring about new realities to social life that may not have been experienced before. This essay on Artificial Intelligence will help students to know the various advantages of using AI and how it has made our lives easier and simpler. Also, in the end, we have described the future scope of AI and the harmful effects of using it. To get a good command of essay writing, students must practise CBSE Essays on different topics.

Artificial Intelligence is the science and engineering of making intelligent machines, especially intelligent computer programs. It is concerned with getting computers to do tasks that would normally require human intelligence. AI systems are basically software systems (or controllers for robots) that use techniques such as machine learning and deep learning to solve problems in particular domains without hard coding all possibilities (i.e. algorithmic steps) in software. Due to this, AI started showing promising solutions for industry and businesses as well as our daily lives.

Importance and Advantages of Artificial Intelligence

Advances in computing and digital technologies have a direct influence on our lives, businesses and social life. This has influenced our daily routines, such as using mobile devices and active involvement on social media. AI systems are the most influential digital technologies. With AI systems, businesses are able to handle large data sets and provide speedy essential input to operations. Moreover, businesses are able to adapt to constant changes and are becoming more flexible.

By introducing Artificial Intelligence systems into devices, new business processes are opting for the automated process. A new paradigm emerges as a result of such intelligent automation, which now dictates not only how businesses operate but also who does the job. Many manufacturing sites can now operate fully automated with robots and without any human workers. Artificial Intelligence now brings unheard and unexpected innovations to the business world that many organizations will need to integrate to remain competitive and move further to lead the competitors.

Artificial Intelligence shapes our lives and social interactions through technological advancement. There are many AI applications which are specifically developed for providing better services to individuals, such as mobile phones, electronic gadgets, social media platforms etc. We are delegating our activities through intelligent applications, such as personal assistants, intelligent wearable devices and other applications. AI systems that operate household apparatus help us at home with cooking or cleaning.

Future Scope of Artificial Intelligence

In the future, intelligent machines will replace or enhance human capabilities in many areas. Artificial intelligence is becoming a popular field in computer science as it has enhanced humans. Application areas of artificial intelligence are having a huge impact on various fields of life to solve complex problems in various areas such as education, engineering, business, medicine, weather forecasting etc. Many labourers’ work can be done by a single machine. But Artificial Intelligence has another aspect: it can be dangerous for us. If we become completely dependent on machines, then it can ruin our life. We will not be able to do any work by ourselves and get lazy. Another disadvantage is that it cannot give a human-like feeling. So machines should be used only where they are actually required.

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How artificial intelligence is transforming the world

Subscribe to techstream, darrell m. west and darrell m. west senior fellow - center for technology innovation , douglas dillon chair in governmental studies john r. allen john r. allen.

April 24, 2018

Artificial intelligence (AI) is a wide-ranging tool that enables people to rethink how we integrate information, analyze data, and use the resulting insights to improve decision making—and already it is transforming every walk of life. In this report, Darrell West and John Allen discuss AI’s application across a variety of sectors, address issues in its development, and offer recommendations for getting the most out of AI while still protecting important human values.

Table of Contents I. Qualities of artificial intelligence II. Applications in diverse sectors III. Policy, regulatory, and ethical issues IV. Recommendations V. Conclusion

  • 49 min read

Most people are not very familiar with the concept of artificial intelligence (AI). As an illustration, when 1,500 senior business leaders in the United States in 2017 were asked about AI, only 17 percent said they were familiar with it. 1 A number of them were not sure what it was or how it would affect their particular companies. They understood there was considerable potential for altering business processes, but were not clear how AI could be deployed within their own organizations.

Despite its widespread lack of familiarity, AI is a technology that is transforming every walk of life. It is a wide-ranging tool that enables people to rethink how we integrate information, analyze data, and use the resulting insights to improve decisionmaking. Our hope through this comprehensive overview is to explain AI to an audience of policymakers, opinion leaders, and interested observers, and demonstrate how AI already is altering the world and raising important questions for society, the economy, and governance.

In this paper, we discuss novel applications in finance, national security, health care, criminal justice, transportation, and smart cities, and address issues such as data access problems, algorithmic bias, AI ethics and transparency, and legal liability for AI decisions. We contrast the regulatory approaches of the U.S. and European Union, and close by making a number of recommendations for getting the most out of AI while still protecting important human values. 2

In order to maximize AI benefits, we recommend nine steps for going forward:

  • Encourage greater data access for researchers without compromising users’ personal privacy,
  • invest more government funding in unclassified AI research,
  • promote new models of digital education and AI workforce development so employees have the skills needed in the 21 st -century economy,
  • create a federal AI advisory committee to make policy recommendations,
  • engage with state and local officials so they enact effective policies,
  • regulate broad AI principles rather than specific algorithms,
  • take bias complaints seriously so AI does not replicate historic injustice, unfairness, or discrimination in data or algorithms,
  • maintain mechanisms for human oversight and control, and
  • penalize malicious AI behavior and promote cybersecurity.

Qualities of artificial intelligence

Although there is no uniformly agreed upon definition, AI generally is thought to refer to “machines that respond to stimulation consistent with traditional responses from humans, given the human capacity for contemplation, judgment and intention.” 3  According to researchers Shubhendu and Vijay, these software systems “make decisions which normally require [a] human level of expertise” and help people anticipate problems or deal with issues as they come up. 4 As such, they operate in an intentional, intelligent, and adaptive manner.

Intentionality

Artificial intelligence algorithms are designed to make decisions, often using real-time data. They are unlike passive machines that are capable only of mechanical or predetermined responses. Using sensors, digital data, or remote inputs, they combine information from a variety of different sources, analyze the material instantly, and act on the insights derived from those data. With massive improvements in storage systems, processing speeds, and analytic techniques, they are capable of tremendous sophistication in analysis and decisionmaking.

Artificial intelligence is already altering the world and raising important questions for society, the economy, and governance.

Intelligence

AI generally is undertaken in conjunction with machine learning and data analytics. 5 Machine learning takes data and looks for underlying trends. If it spots something that is relevant for a practical problem, software designers can take that knowledge and use it to analyze specific issues. All that is required are data that are sufficiently robust that algorithms can discern useful patterns. Data can come in the form of digital information, satellite imagery, visual information, text, or unstructured data.

Adaptability

AI systems have the ability to learn and adapt as they make decisions. In the transportation area, for example, semi-autonomous vehicles have tools that let drivers and vehicles know about upcoming congestion, potholes, highway construction, or other possible traffic impediments. Vehicles can take advantage of the experience of other vehicles on the road, without human involvement, and the entire corpus of their achieved “experience” is immediately and fully transferable to other similarly configured vehicles. Their advanced algorithms, sensors, and cameras incorporate experience in current operations, and use dashboards and visual displays to present information in real time so human drivers are able to make sense of ongoing traffic and vehicular conditions. And in the case of fully autonomous vehicles, advanced systems can completely control the car or truck, and make all the navigational decisions.

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Applications in diverse sectors

AI is not a futuristic vision, but rather something that is here today and being integrated with and deployed into a variety of sectors. This includes fields such as finance, national security, health care, criminal justice, transportation, and smart cities. There are numerous examples where AI already is making an impact on the world and augmenting human capabilities in significant ways. 6

One of the reasons for the growing role of AI is the tremendous opportunities for economic development that it presents. A project undertaken by PriceWaterhouseCoopers estimated that “artificial intelligence technologies could increase global GDP by $15.7 trillion, a full 14%, by 2030.” 7 That includes advances of $7 trillion in China, $3.7 trillion in North America, $1.8 trillion in Northern Europe, $1.2 trillion for Africa and Oceania, $0.9 trillion in the rest of Asia outside of China, $0.7 trillion in Southern Europe, and $0.5 trillion in Latin America. China is making rapid strides because it has set a national goal of investing $150 billion in AI and becoming the global leader in this area by 2030.

Meanwhile, a McKinsey Global Institute study of China found that “AI-led automation can give the Chinese economy a productivity injection that would add 0.8 to 1.4 percentage points to GDP growth annually, depending on the speed of adoption.” 8 Although its authors found that China currently lags the United States and the United Kingdom in AI deployment, the sheer size of its AI market gives that country tremendous opportunities for pilot testing and future development.

Investments in financial AI in the United States tripled between 2013 and 2014 to a total of $12.2 billion. 9 According to observers in that sector, “Decisions about loans are now being made by software that can take into account a variety of finely parsed data about a borrower, rather than just a credit score and a background check.” 10 In addition, there are so-called robo-advisers that “create personalized investment portfolios, obviating the need for stockbrokers and financial advisers.” 11 These advances are designed to take the emotion out of investing and undertake decisions based on analytical considerations, and make these choices in a matter of minutes.

A prominent example of this is taking place in stock exchanges, where high-frequency trading by machines has replaced much of human decisionmaking. People submit buy and sell orders, and computers match them in the blink of an eye without human intervention. Machines can spot trading inefficiencies or market differentials on a very small scale and execute trades that make money according to investor instructions. 12 Powered in some places by advanced computing, these tools have much greater capacities for storing information because of their emphasis not on a zero or a one, but on “quantum bits” that can store multiple values in each location. 13 That dramatically increases storage capacity and decreases processing times.

Fraud detection represents another way AI is helpful in financial systems. It sometimes is difficult to discern fraudulent activities in large organizations, but AI can identify abnormalities, outliers, or deviant cases requiring additional investigation. That helps managers find problems early in the cycle, before they reach dangerous levels. 14

National security

AI plays a substantial role in national defense. Through its Project Maven, the American military is deploying AI “to sift through the massive troves of data and video captured by surveillance and then alert human analysts of patterns or when there is abnormal or suspicious activity.” 15 According to Deputy Secretary of Defense Patrick Shanahan, the goal of emerging technologies in this area is “to meet our warfighters’ needs and to increase [the] speed and agility [of] technology development and procurement.” 16

Artificial intelligence will accelerate the traditional process of warfare so rapidly that a new term has been coined: hyperwar.

The big data analytics associated with AI will profoundly affect intelligence analysis, as massive amounts of data are sifted in near real time—if not eventually in real time—thereby providing commanders and their staffs a level of intelligence analysis and productivity heretofore unseen. Command and control will similarly be affected as human commanders delegate certain routine, and in special circumstances, key decisions to AI platforms, reducing dramatically the time associated with the decision and subsequent action. In the end, warfare is a time competitive process, where the side able to decide the fastest and move most quickly to execution will generally prevail. Indeed, artificially intelligent intelligence systems, tied to AI-assisted command and control systems, can move decision support and decisionmaking to a speed vastly superior to the speeds of the traditional means of waging war. So fast will be this process, especially if coupled to automatic decisions to launch artificially intelligent autonomous weapons systems capable of lethal outcomes, that a new term has been coined specifically to embrace the speed at which war will be waged: hyperwar.

While the ethical and legal debate is raging over whether America will ever wage war with artificially intelligent autonomous lethal systems, the Chinese and Russians are not nearly so mired in this debate, and we should anticipate our need to defend against these systems operating at hyperwar speeds. The challenge in the West of where to position “humans in the loop” in a hyperwar scenario will ultimately dictate the West’s capacity to be competitive in this new form of conflict. 17

Just as AI will profoundly affect the speed of warfare, the proliferation of zero day or zero second cyber threats as well as polymorphic malware will challenge even the most sophisticated signature-based cyber protection. This forces significant improvement to existing cyber defenses. Increasingly, vulnerable systems are migrating, and will need to shift to a layered approach to cybersecurity with cloud-based, cognitive AI platforms. This approach moves the community toward a “thinking” defensive capability that can defend networks through constant training on known threats. This capability includes DNA-level analysis of heretofore unknown code, with the possibility of recognizing and stopping inbound malicious code by recognizing a string component of the file. This is how certain key U.S.-based systems stopped the debilitating “WannaCry” and “Petya” viruses.

Preparing for hyperwar and defending critical cyber networks must become a high priority because China, Russia, North Korea, and other countries are putting substantial resources into AI. In 2017, China’s State Council issued a plan for the country to “build a domestic industry worth almost $150 billion” by 2030. 18 As an example of the possibilities, the Chinese search firm Baidu has pioneered a facial recognition application that finds missing people. In addition, cities such as Shenzhen are providing up to $1 million to support AI labs. That country hopes AI will provide security, combat terrorism, and improve speech recognition programs. 19 The dual-use nature of many AI algorithms will mean AI research focused on one sector of society can be rapidly modified for use in the security sector as well. 20

Health care

AI tools are helping designers improve computational sophistication in health care. For example, Merantix is a German company that applies deep learning to medical issues. It has an application in medical imaging that “detects lymph nodes in the human body in Computer Tomography (CT) images.” 21 According to its developers, the key is labeling the nodes and identifying small lesions or growths that could be problematic. Humans can do this, but radiologists charge $100 per hour and may be able to carefully read only four images an hour. If there were 10,000 images, the cost of this process would be $250,000, which is prohibitively expensive if done by humans.

What deep learning can do in this situation is train computers on data sets to learn what a normal-looking versus an irregular-appearing lymph node is. After doing that through imaging exercises and honing the accuracy of the labeling, radiological imaging specialists can apply this knowledge to actual patients and determine the extent to which someone is at risk of cancerous lymph nodes. Since only a few are likely to test positive, it is a matter of identifying the unhealthy versus healthy node.

AI has been applied to congestive heart failure as well, an illness that afflicts 10 percent of senior citizens and costs $35 billion each year in the United States. AI tools are helpful because they “predict in advance potential challenges ahead and allocate resources to patient education, sensing, and proactive interventions that keep patients out of the hospital.” 22

Criminal justice

AI is being deployed in the criminal justice area. The city of Chicago has developed an AI-driven “Strategic Subject List” that analyzes people who have been arrested for their risk of becoming future perpetrators. It ranks 400,000 people on a scale of 0 to 500, using items such as age, criminal activity, victimization, drug arrest records, and gang affiliation. In looking at the data, analysts found that youth is a strong predictor of violence, being a shooting victim is associated with becoming a future perpetrator, gang affiliation has little predictive value, and drug arrests are not significantly associated with future criminal activity. 23

Judicial experts claim AI programs reduce human bias in law enforcement and leads to a fairer sentencing system. R Street Institute Associate Caleb Watney writes:

Empirically grounded questions of predictive risk analysis play to the strengths of machine learning, automated reasoning and other forms of AI. One machine-learning policy simulation concluded that such programs could be used to cut crime up to 24.8 percent with no change in jailing rates, or reduce jail populations by up to 42 percent with no increase in crime rates. 24

However, critics worry that AI algorithms represent “a secret system to punish citizens for crimes they haven’t yet committed. The risk scores have been used numerous times to guide large-scale roundups.” 25 The fear is that such tools target people of color unfairly and have not helped Chicago reduce the murder wave that has plagued it in recent years.

Despite these concerns, other countries are moving ahead with rapid deployment in this area. In China, for example, companies already have “considerable resources and access to voices, faces and other biometric data in vast quantities, which would help them develop their technologies.” 26 New technologies make it possible to match images and voices with other types of information, and to use AI on these combined data sets to improve law enforcement and national security. Through its “Sharp Eyes” program, Chinese law enforcement is matching video images, social media activity, online purchases, travel records, and personal identity into a “police cloud.” This integrated database enables authorities to keep track of criminals, potential law-breakers, and terrorists. 27 Put differently, China has become the world’s leading AI-powered surveillance state.

Transportation

Transportation represents an area where AI and machine learning are producing major innovations. Research by Cameron Kerry and Jack Karsten of the Brookings Institution has found that over $80 billion was invested in autonomous vehicle technology between August 2014 and June 2017. Those investments include applications both for autonomous driving and the core technologies vital to that sector. 28

Autonomous vehicles—cars, trucks, buses, and drone delivery systems—use advanced technological capabilities. Those features include automated vehicle guidance and braking, lane-changing systems, the use of cameras and sensors for collision avoidance, the use of AI to analyze information in real time, and the use of high-performance computing and deep learning systems to adapt to new circumstances through detailed maps. 29

Light detection and ranging systems (LIDARs) and AI are key to navigation and collision avoidance. LIDAR systems combine light and radar instruments. They are mounted on the top of vehicles that use imaging in a 360-degree environment from a radar and light beams to measure the speed and distance of surrounding objects. Along with sensors placed on the front, sides, and back of the vehicle, these instruments provide information that keeps fast-moving cars and trucks in their own lane, helps them avoid other vehicles, applies brakes and steering when needed, and does so instantly so as to avoid accidents.

Advanced software enables cars to learn from the experiences of other vehicles on the road and adjust their guidance systems as weather, driving, or road conditions change. This means that software is the key—not the physical car or truck itself.

Since these cameras and sensors compile a huge amount of information and need to process it instantly to avoid the car in the next lane, autonomous vehicles require high-performance computing, advanced algorithms, and deep learning systems to adapt to new scenarios. This means that software is the key, not the physical car or truck itself. 30 Advanced software enables cars to learn from the experiences of other vehicles on the road and adjust their guidance systems as weather, driving, or road conditions change. 31

Ride-sharing companies are very interested in autonomous vehicles. They see advantages in terms of customer service and labor productivity. All of the major ride-sharing companies are exploring driverless cars. The surge of car-sharing and taxi services—such as Uber and Lyft in the United States, Daimler’s Mytaxi and Hailo service in Great Britain, and Didi Chuxing in China—demonstrate the opportunities of this transportation option. Uber recently signed an agreement to purchase 24,000 autonomous cars from Volvo for its ride-sharing service. 32

However, the ride-sharing firm suffered a setback in March 2018 when one of its autonomous vehicles in Arizona hit and killed a pedestrian. Uber and several auto manufacturers immediately suspended testing and launched investigations into what went wrong and how the fatality could have occurred. 33 Both industry and consumers want reassurance that the technology is safe and able to deliver on its stated promises. Unless there are persuasive answers, this accident could slow AI advancements in the transportation sector.

Smart cities

Metropolitan governments are using AI to improve urban service delivery. For example, according to Kevin Desouza, Rashmi Krishnamurthy, and Gregory Dawson:

The Cincinnati Fire Department is using data analytics to optimize medical emergency responses. The new analytics system recommends to the dispatcher an appropriate response to a medical emergency call—whether a patient can be treated on-site or needs to be taken to the hospital—by taking into account several factors, such as the type of call, location, weather, and similar calls. 34

Since it fields 80,000 requests each year, Cincinnati officials are deploying this technology to prioritize responses and determine the best ways to handle emergencies. They see AI as a way to deal with large volumes of data and figure out efficient ways of responding to public requests. Rather than address service issues in an ad hoc manner, authorities are trying to be proactive in how they provide urban services.

Cincinnati is not alone. A number of metropolitan areas are adopting smart city applications that use AI to improve service delivery, environmental planning, resource management, energy utilization, and crime prevention, among other things. For its smart cities index, the magazine Fast Company ranked American locales and found Seattle, Boston, San Francisco, Washington, D.C., and New York City as the top adopters. Seattle, for example, has embraced sustainability and is using AI to manage energy usage and resource management. Boston has launched a “City Hall To Go” that makes sure underserved communities receive needed public services. It also has deployed “cameras and inductive loops to manage traffic and acoustic sensors to identify gun shots.” San Francisco has certified 203 buildings as meeting LEED sustainability standards. 35

Through these and other means, metropolitan areas are leading the country in the deployment of AI solutions. Indeed, according to a National League of Cities report, 66 percent of American cities are investing in smart city technology. Among the top applications noted in the report are “smart meters for utilities, intelligent traffic signals, e-governance applications, Wi-Fi kiosks, and radio frequency identification sensors in pavement.” 36

Policy, regulatory, and ethical issues

These examples from a variety of sectors demonstrate how AI is transforming many walks of human existence. The increasing penetration of AI and autonomous devices into many aspects of life is altering basic operations and decisionmaking within organizations, and improving efficiency and response times.

At the same time, though, these developments raise important policy, regulatory, and ethical issues. For example, how should we promote data access? How do we guard against biased or unfair data used in algorithms? What types of ethical principles are introduced through software programming, and how transparent should designers be about their choices? What about questions of legal liability in cases where algorithms cause harm? 37

The increasing penetration of AI into many aspects of life is altering decisionmaking within organizations and improving efficiency. At the same time, though, these developments raise important policy, regulatory, and ethical issues.

Data access problems

The key to getting the most out of AI is having a “data-friendly ecosystem with unified standards and cross-platform sharing.” AI depends on data that can be analyzed in real time and brought to bear on concrete problems. Having data that are “accessible for exploration” in the research community is a prerequisite for successful AI development. 38

According to a McKinsey Global Institute study, nations that promote open data sources and data sharing are the ones most likely to see AI advances. In this regard, the United States has a substantial advantage over China. Global ratings on data openness show that U.S. ranks eighth overall in the world, compared to 93 for China. 39

But right now, the United States does not have a coherent national data strategy. There are few protocols for promoting research access or platforms that make it possible to gain new insights from proprietary data. It is not always clear who owns data or how much belongs in the public sphere. These uncertainties limit the innovation economy and act as a drag on academic research. In the following section, we outline ways to improve data access for researchers.

Biases in data and algorithms

In some instances, certain AI systems are thought to have enabled discriminatory or biased practices. 40 For example, Airbnb has been accused of having homeowners on its platform who discriminate against racial minorities. A research project undertaken by the Harvard Business School found that “Airbnb users with distinctly African American names were roughly 16 percent less likely to be accepted as guests than those with distinctly white names.” 41

Racial issues also come up with facial recognition software. Most such systems operate by comparing a person’s face to a range of faces in a large database. As pointed out by Joy Buolamwini of the Algorithmic Justice League, “If your facial recognition data contains mostly Caucasian faces, that’s what your program will learn to recognize.” 42 Unless the databases have access to diverse data, these programs perform poorly when attempting to recognize African-American or Asian-American features.

Many historical data sets reflect traditional values, which may or may not represent the preferences wanted in a current system. As Buolamwini notes, such an approach risks repeating inequities of the past:

The rise of automation and the increased reliance on algorithms for high-stakes decisions such as whether someone get insurance or not, your likelihood to default on a loan or somebody’s risk of recidivism means this is something that needs to be addressed. Even admissions decisions are increasingly automated—what school our children go to and what opportunities they have. We don’t have to bring the structural inequalities of the past into the future we create. 43

AI ethics and transparency

Algorithms embed ethical considerations and value choices into program decisions. As such, these systems raise questions concerning the criteria used in automated decisionmaking. Some people want to have a better understanding of how algorithms function and what choices are being made. 44

In the United States, many urban schools use algorithms for enrollment decisions based on a variety of considerations, such as parent preferences, neighborhood qualities, income level, and demographic background. According to Brookings researcher Jon Valant, the New Orleans–based Bricolage Academy “gives priority to economically disadvantaged applicants for up to 33 percent of available seats. In practice, though, most cities have opted for categories that prioritize siblings of current students, children of school employees, and families that live in school’s broad geographic area.” 45 Enrollment choices can be expected to be very different when considerations of this sort come into play.

Depending on how AI systems are set up, they can facilitate the redlining of mortgage applications, help people discriminate against individuals they don’t like, or help screen or build rosters of individuals based on unfair criteria. The types of considerations that go into programming decisions matter a lot in terms of how the systems operate and how they affect customers. 46

For these reasons, the EU is implementing the General Data Protection Regulation (GDPR) in May 2018. The rules specify that people have “the right to opt out of personally tailored ads” and “can contest ‘legal or similarly significant’ decisions made by algorithms and appeal for human intervention” in the form of an explanation of how the algorithm generated a particular outcome. Each guideline is designed to ensure the protection of personal data and provide individuals with information on how the “black box” operates. 47

Legal liability

There are questions concerning the legal liability of AI systems. If there are harms or infractions (or fatalities in the case of driverless cars), the operators of the algorithm likely will fall under product liability rules. A body of case law has shown that the situation’s facts and circumstances determine liability and influence the kind of penalties that are imposed. Those can range from civil fines to imprisonment for major harms. 48 The Uber-related fatality in Arizona will be an important test case for legal liability. The state actively recruited Uber to test its autonomous vehicles and gave the company considerable latitude in terms of road testing. It remains to be seen if there will be lawsuits in this case and who is sued: the human backup driver, the state of Arizona, the Phoenix suburb where the accident took place, Uber, software developers, or the auto manufacturer. Given the multiple people and organizations involved in the road testing, there are many legal questions to be resolved.

In non-transportation areas, digital platforms often have limited liability for what happens on their sites. For example, in the case of Airbnb, the firm “requires that people agree to waive their right to sue, or to join in any class-action lawsuit or class-action arbitration, to use the service.” By demanding that its users sacrifice basic rights, the company limits consumer protections and therefore curtails the ability of people to fight discrimination arising from unfair algorithms. 49 But whether the principle of neutral networks holds up in many sectors is yet to be determined on a widespread basis.

Recommendations

In order to balance innovation with basic human values, we propose a number of recommendations for moving forward with AI. This includes improving data access, increasing government investment in AI, promoting AI workforce development, creating a federal advisory committee, engaging with state and local officials to ensure they enact effective policies, regulating broad objectives as opposed to specific algorithms, taking bias seriously as an AI issue, maintaining mechanisms for human control and oversight, and penalizing malicious behavior and promoting cybersecurity.

Improving data access

The United States should develop a data strategy that promotes innovation and consumer protection. Right now, there are no uniform standards in terms of data access, data sharing, or data protection. Almost all the data are proprietary in nature and not shared very broadly with the research community, and this limits innovation and system design. AI requires data to test and improve its learning capacity. 50 Without structured and unstructured data sets, it will be nearly impossible to gain the full benefits of artificial intelligence.

In general, the research community needs better access to government and business data, although with appropriate safeguards to make sure researchers do not misuse data in the way Cambridge Analytica did with Facebook information. There is a variety of ways researchers could gain data access. One is through voluntary agreements with companies holding proprietary data. Facebook, for example, recently announced a partnership with Stanford economist Raj Chetty to use its social media data to explore inequality. 51 As part of the arrangement, researchers were required to undergo background checks and could only access data from secured sites in order to protect user privacy and security.

In the U.S., there are no uniform standards in terms of data access, data sharing, or data protection. Almost all the data are proprietary in nature and not shared very broadly with the research community, and this limits innovation and system design.

Google long has made available search results in aggregated form for researchers and the general public. Through its “Trends” site, scholars can analyze topics such as interest in Trump, views about democracy, and perspectives on the overall economy. 52 That helps people track movements in public interest and identify topics that galvanize the general public.

Twitter makes much of its tweets available to researchers through application programming interfaces, commonly referred to as APIs. These tools help people outside the company build application software and make use of data from its social media platform. They can study patterns of social media communications and see how people are commenting on or reacting to current events.

In some sectors where there is a discernible public benefit, governments can facilitate collaboration by building infrastructure that shares data. For example, the National Cancer Institute has pioneered a data-sharing protocol where certified researchers can query health data it has using de-identified information drawn from clinical data, claims information, and drug therapies. That enables researchers to evaluate efficacy and effectiveness, and make recommendations regarding the best medical approaches, without compromising the privacy of individual patients.

There could be public-private data partnerships that combine government and business data sets to improve system performance. For example, cities could integrate information from ride-sharing services with its own material on social service locations, bus lines, mass transit, and highway congestion to improve transportation. That would help metropolitan areas deal with traffic tie-ups and assist in highway and mass transit planning.

Some combination of these approaches would improve data access for researchers, the government, and the business community, without impinging on personal privacy. As noted by Ian Buck, the vice president of NVIDIA, “Data is the fuel that drives the AI engine. The federal government has access to vast sources of information. Opening access to that data will help us get insights that will transform the U.S. economy.” 53 Through its Data.gov portal, the federal government already has put over 230,000 data sets into the public domain, and this has propelled innovation and aided improvements in AI and data analytic technologies. 54 The private sector also needs to facilitate research data access so that society can achieve the full benefits of artificial intelligence.

Increase government investment in AI

According to Greg Brockman, the co-founder of OpenAI, the U.S. federal government invests only $1.1 billion in non-classified AI technology. 55 That is far lower than the amount being spent by China or other leading nations in this area of research. That shortfall is noteworthy because the economic payoffs of AI are substantial. In order to boost economic development and social innovation, federal officials need to increase investment in artificial intelligence and data analytics. Higher investment is likely to pay for itself many times over in economic and social benefits. 56

Promote digital education and workforce development

As AI applications accelerate across many sectors, it is vital that we reimagine our educational institutions for a world where AI will be ubiquitous and students need a different kind of training than they currently receive. Right now, many students do not receive instruction in the kinds of skills that will be needed in an AI-dominated landscape. For example, there currently are shortages of data scientists, computer scientists, engineers, coders, and platform developers. These are skills that are in short supply; unless our educational system generates more people with these capabilities, it will limit AI development.

For these reasons, both state and federal governments have been investing in AI human capital. For example, in 2017, the National Science Foundation funded over 6,500 graduate students in computer-related fields and has launched several new initiatives designed to encourage data and computer science at all levels from pre-K to higher and continuing education. 57 The goal is to build a larger pipeline of AI and data analytic personnel so that the United States can reap the full advantages of the knowledge revolution.

But there also needs to be substantial changes in the process of learning itself. It is not just technical skills that are needed in an AI world but skills of critical reasoning, collaboration, design, visual display of information, and independent thinking, among others. AI will reconfigure how society and the economy operate, and there needs to be “big picture” thinking on what this will mean for ethics, governance, and societal impact. People will need the ability to think broadly about many questions and integrate knowledge from a number of different areas.

One example of new ways to prepare students for a digital future is IBM’s Teacher Advisor program, utilizing Watson’s free online tools to help teachers bring the latest knowledge into the classroom. They enable instructors to develop new lesson plans in STEM and non-STEM fields, find relevant instructional videos, and help students get the most out of the classroom. 58 As such, they are precursors of new educational environments that need to be created.

Create a federal AI advisory committee

Federal officials need to think about how they deal with artificial intelligence. As noted previously, there are many issues ranging from the need for improved data access to addressing issues of bias and discrimination. It is vital that these and other concerns be considered so we gain the full benefits of this emerging technology.

In order to move forward in this area, several members of Congress have introduced the “Future of Artificial Intelligence Act,” a bill designed to establish broad policy and legal principles for AI. It proposes the secretary of commerce create a federal advisory committee on the development and implementation of artificial intelligence. The legislation provides a mechanism for the federal government to get advice on ways to promote a “climate of investment and innovation to ensure the global competitiveness of the United States,” “optimize the development of artificial intelligence to address the potential growth, restructuring, or other changes in the United States workforce,” “support the unbiased development and application of artificial intelligence,” and “protect the privacy rights of individuals.” 59

Among the specific questions the committee is asked to address include the following: competitiveness, workforce impact, education, ethics training, data sharing, international cooperation, accountability, machine learning bias, rural impact, government efficiency, investment climate, job impact, bias, and consumer impact. The committee is directed to submit a report to Congress and the administration 540 days after enactment regarding any legislative or administrative action needed on AI.

This legislation is a step in the right direction, although the field is moving so rapidly that we would recommend shortening the reporting timeline from 540 days to 180 days. Waiting nearly two years for a committee report will certainly result in missed opportunities and a lack of action on important issues. Given rapid advances in the field, having a much quicker turnaround time on the committee analysis would be quite beneficial.

Engage with state and local officials

States and localities also are taking action on AI. For example, the New York City Council unanimously passed a bill that directed the mayor to form a taskforce that would “monitor the fairness and validity of algorithms used by municipal agencies.” 60 The city employs algorithms to “determine if a lower bail will be assigned to an indigent defendant, where firehouses are established, student placement for public schools, assessing teacher performance, identifying Medicaid fraud and determine where crime will happen next.” 61

According to the legislation’s developers, city officials want to know how these algorithms work and make sure there is sufficient AI transparency and accountability. In addition, there is concern regarding the fairness and biases of AI algorithms, so the taskforce has been directed to analyze these issues and make recommendations regarding future usage. It is scheduled to report back to the mayor on a range of AI policy, legal, and regulatory issues by late 2019.

Some observers already are worrying that the taskforce won’t go far enough in holding algorithms accountable. For example, Julia Powles of Cornell Tech and New York University argues that the bill originally required companies to make the AI source code available to the public for inspection, and that there be simulations of its decisionmaking using actual data. After criticism of those provisions, however, former Councilman James Vacca dropped the requirements in favor of a task force studying these issues. He and other city officials were concerned that publication of proprietary information on algorithms would slow innovation and make it difficult to find AI vendors who would work with the city. 62 It remains to be seen how this local task force will balance issues of innovation, privacy, and transparency.

Regulate broad objectives more than specific algorithms

The European Union has taken a restrictive stance on these issues of data collection and analysis. 63 It has rules limiting the ability of companies from collecting data on road conditions and mapping street views. Because many of these countries worry that people’s personal information in unencrypted Wi-Fi networks are swept up in overall data collection, the EU has fined technology firms, demanded copies of data, and placed limits on the material collected. 64 This has made it more difficult for technology companies operating there to develop the high-definition maps required for autonomous vehicles.

The GDPR being implemented in Europe place severe restrictions on the use of artificial intelligence and machine learning. According to published guidelines, “Regulations prohibit any automated decision that ‘significantly affects’ EU citizens. This includes techniques that evaluates a person’s ‘performance at work, economic situation, health, personal preferences, interests, reliability, behavior, location, or movements.’” 65 In addition, these new rules give citizens the right to review how digital services made specific algorithmic choices affecting people.

By taking a restrictive stance on issues of data collection and analysis, the European Union is putting its manufacturers and software designers at a significant disadvantage to the rest of the world.

If interpreted stringently, these rules will make it difficult for European software designers (and American designers who work with European counterparts) to incorporate artificial intelligence and high-definition mapping in autonomous vehicles. Central to navigation in these cars and trucks is tracking location and movements. Without high-definition maps containing geo-coded data and the deep learning that makes use of this information, fully autonomous driving will stagnate in Europe. Through this and other data protection actions, the European Union is putting its manufacturers and software designers at a significant disadvantage to the rest of the world.

It makes more sense to think about the broad objectives desired in AI and enact policies that advance them, as opposed to governments trying to crack open the “black boxes” and see exactly how specific algorithms operate. Regulating individual algorithms will limit innovation and make it difficult for companies to make use of artificial intelligence.

Take biases seriously

Bias and discrimination are serious issues for AI. There already have been a number of cases of unfair treatment linked to historic data, and steps need to be undertaken to make sure that does not become prevalent in artificial intelligence. Existing statutes governing discrimination in the physical economy need to be extended to digital platforms. That will help protect consumers and build confidence in these systems as a whole.

For these advances to be widely adopted, more transparency is needed in how AI systems operate. Andrew Burt of Immuta argues, “The key problem confronting predictive analytics is really transparency. We’re in a world where data science operations are taking on increasingly important tasks, and the only thing holding them back is going to be how well the data scientists who train the models can explain what it is their models are doing.” 66

Maintaining mechanisms for human oversight and control

Some individuals have argued that there needs to be avenues for humans to exercise oversight and control of AI systems. For example, Allen Institute for Artificial Intelligence CEO Oren Etzioni argues there should be rules for regulating these systems. First, he says, AI must be governed by all the laws that already have been developed for human behavior, including regulations concerning “cyberbullying, stock manipulation or terrorist threats,” as well as “entrap[ping] people into committing crimes.” Second, he believes that these systems should disclose they are automated systems and not human beings. Third, he states, “An A.I. system cannot retain or disclose confidential information without explicit approval from the source of that information.” 67 His rationale is that these tools store so much data that people have to be cognizant of the privacy risks posed by AI.

In the same vein, the IEEE Global Initiative has ethical guidelines for AI and autonomous systems. Its experts suggest that these models be programmed with consideration for widely accepted human norms and rules for behavior. AI algorithms need to take into effect the importance of these norms, how norm conflict can be resolved, and ways these systems can be transparent about norm resolution. Software designs should be programmed for “nondeception” and “honesty,” according to ethics experts. When failures occur, there must be mitigation mechanisms to deal with the consequences. In particular, AI must be sensitive to problems such as bias, discrimination, and fairness. 68

A group of machine learning experts claim it is possible to automate ethical decisionmaking. Using the trolley problem as a moral dilemma, they ask the following question: If an autonomous car goes out of control, should it be programmed to kill its own passengers or the pedestrians who are crossing the street? They devised a “voting-based system” that asked 1.3 million people to assess alternative scenarios, summarized the overall choices, and applied the overall perspective of these individuals to a range of vehicular possibilities. That allowed them to automate ethical decisionmaking in AI algorithms, taking public preferences into account. 69 This procedure, of course, does not reduce the tragedy involved in any kind of fatality, such as seen in the Uber case, but it provides a mechanism to help AI developers incorporate ethical considerations in their planning.

Penalize malicious behavior and promote cybersecurity

As with any emerging technology, it is important to discourage malicious treatment designed to trick software or use it for undesirable ends. 70 This is especially important given the dual-use aspects of AI, where the same tool can be used for beneficial or malicious purposes. The malevolent use of AI exposes individuals and organizations to unnecessary risks and undermines the virtues of the emerging technology. This includes behaviors such as hacking, manipulating algorithms, compromising privacy and confidentiality, or stealing identities. Efforts to hijack AI in order to solicit confidential information should be seriously penalized as a way to deter such actions. 71

In a rapidly changing world with many entities having advanced computing capabilities, there needs to be serious attention devoted to cybersecurity. Countries have to be careful to safeguard their own systems and keep other nations from damaging their security. 72 According to the U.S. Department of Homeland Security, a major American bank receives around 11 million calls a week at its service center. In order to protect its telephony from denial of service attacks, it uses a “machine learning-based policy engine [that] blocks more than 120,000 calls per month based on voice firewall policies including harassing callers, robocalls and potential fraudulent calls.” 73 This represents a way in which machine learning can help defend technology systems from malevolent attacks.

To summarize, the world is on the cusp of revolutionizing many sectors through artificial intelligence and data analytics. There already are significant deployments in finance, national security, health care, criminal justice, transportation, and smart cities that have altered decisionmaking, business models, risk mitigation, and system performance. These developments are generating substantial economic and social benefits.

The world is on the cusp of revolutionizing many sectors through artificial intelligence, but the way AI systems are developed need to be better understood due to the major implications these technologies will have for society as a whole.

Yet the manner in which AI systems unfold has major implications for society as a whole. It matters how policy issues are addressed, ethical conflicts are reconciled, legal realities are resolved, and how much transparency is required in AI and data analytic solutions. 74 Human choices about software development affect the way in which decisions are made and the manner in which they are integrated into organizational routines. Exactly how these processes are executed need to be better understood because they will have substantial impact on the general public soon, and for the foreseeable future. AI may well be a revolution in human affairs, and become the single most influential human innovation in history.

Note: We appreciate the research assistance of Grace Gilberg, Jack Karsten, Hillary Schaub, and Kristjan Tomasson on this project.

The Brookings Institution is a nonprofit organization devoted to independent research and policy solutions. Its mission is to conduct high-quality, independent research and, based on that research, to provide innovative, practical recommendations for policymakers and the public. The conclusions and recommendations of any Brookings publication are solely those of its author(s), and do not reflect the views of the Institution, its management, or its other scholars.

Support for this publication was generously provided by Amazon. Brookings recognizes that the value it provides is in its absolute commitment to quality, independence, and impact. Activities supported by its donors reflect this commitment. 

John R. Allen is a member of the Board of Advisors of Amida Technology and on the Board of Directors of Spark Cognition. Both companies work in fields discussed in this piece.

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  • Ibid., p. 7.
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Essay on Artificial Intelligence

Artificial Intelligence is the intelligence possessed by the machines under which they can perform various functions with human help. With the help of A.I, machines will be able to learn, solve problems, plan things, think, etc. Artificial Intelligence, for example, is the simulation of human intelligence by machines. In the field of technology, Artificial Intelligence is evolving rapidly day by day and it is believed that in the near future, artificial intelligence is going to change human life very drastically and will most probably end all the crises of the world by sorting out the major problems. 

Our life in this modern age depends largely on computers. It is almost impossible to think about life without computers. We need computers in everything that we use in our daily lives. So it becomes very important to make computers intelligent so that our lives become easy. Artificial Intelligence is the theory and development of computers, which imitates the human intelligence and senses, such as visual perception, speech recognition, decision-making, and translation between languages. Artificial Intelligence has brought a revolution in the world of technology. 

Artificial Intelligence Applications

AI is widely used in the field of healthcare. Companies are attempting to develop technologies that will allow for rapid diagnosis. Artificial Intelligence would be able to operate on patients without the need for human oversight. Surgical procedures based on technology are already being performed.

Artificial Intelligence would save a lot of our time. The use of robots would decrease human labour. For example, in industries robots are used which have saved a lot of human effort and time. 

In the field of education, AI has the potential to be very effective. It can bring innovative ways of teaching students with the help of which students will be able to learn the concepts better. 

Artificial intelligence is the future of innovative technology as we can use it in many fields. For example, it can be used in the Military sector, Industrial sector, Automobiles, etc. In the coming years, we will be able to see more applications of AI as this technology is evolving day by day. 

Marketing: Artificial Intelligence provides a deep knowledge of consumers and potential clients to the marketers by enabling them to deliver information at the right time. Through AI solutions, the marketers can refine their campaigns and strategies.

Agriculture: AI technology can be used to detect diseases in plants, pests, and poor plant nutrition. With the help of AI, farmers can analyze the weather conditions, temperature, water usage, and condition of the soil.

Banking: Fraudulent activities can be detected through AI solutions. AI bots, digital payment advisers can create a high quality of service.

Health Care: Artificial Intelligence can surpass human cognition in the analysis, diagnosis, and complication of complicated medical data.

History of Artificial Intelligence

Artificial Intelligence may seem to be a new technology but if we do a bit of research, we will find that it has roots deep in the past. In Greek Mythology, it is said that the concepts of AI were used. 

The model of Artificial neurons was first brought forward in 1943 by Warren McCulloch and Walter Pits. After seven years, in 1950, a research paper related to AI was published by Alan Turing which was titled 'Computer Machinery and Intelligence. The term Artificial Intelligence was first coined in 1956 by John McCarthy, who is known as the father of Artificial Intelligence. 

To conclude, we can say that Artificial Intelligence will be the future of the world. As per the experts, we won't be able to separate ourselves from this technology as it would become an integral part of our lives shortly. AI would change the way we live in this world. This technology would prove to be revolutionary because it will change our lives for good. 

Branches of Artificial Intelligence:

Knowledge Engineering

Machines Learning

Natural Language Processing

Types of Artificial Intelligence

Artificial Intelligence is categorized in two types based on capabilities and functionalities. 

Artificial Intelligence Type-1

Artificial intelligence type-2.

Narrow AI (weak AI): This is designed to perform a specific task with intelligence. It is termed as weak AI because it cannot perform beyond its limitations. It is trained to do a specific task. Some examples of Narrow AI are facial recognition (Siri in Apple phones), speech, and image recognition. IBM’s Watson supercomputer, self-driving cars, playing chess, and solving equations are also some of the examples of weak AI.

General AI (AGI or strong AI): This system can perform nearly every cognitive task as efficiently as humans can do. The main characteristic of general AI is to make a system that can think like a human on its own. This is a long-term goal of many researchers to create such machines.

Super AI: Super AI is a type of intelligence of systems in which machines can surpass human intelligence and can perform any cognitive task better than humans. The main features of strong AI would be the ability to think, reason, solve puzzles, make judgments, plan and communicate on its own. The creation of strong AI might be the biggest revolution in human history.

Reactive Machines: These machines are the basic types of AI. Such AI systems focus only on current situations and react as per the best possible action. They do not store memories for future actions. IBM’s deep blue system and Google’s Alpha go are the examples of reactive machines.

Limited Memory: These machines can store data or past memories for a short period of time. Examples are self-driving cars. They can store information to navigate the road, speed, and distance of nearby cars.

Theory of Mind: These systems understand emotions, beliefs, and requirements like humans. These kinds of machines are still not invented and it’s a long-term goal for the researchers to create one. 

Self-Awareness: Self-awareness AI is the future of artificial intelligence. These machines can outsmart the humans. If these machines are invented then it can bring a revolution in human society. 

Artificial Intelligence will bring a huge revolution in the history of mankind. Human civilization will flourish by amplifying human intelligence with artificial intelligence, as long as we manage to keep the technology beneficial.

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FAQs on Artificial Intelligence Essay

1. What is Artificial Intelligence?

Artificial Intelligence is a branch of computer science that emphasizes the development of intelligent machines that would think and work like humans.

2. How is Artificial Intelligence Categorised?

Artificial Intelligence is categorized in two types based on capabilities and functionalities. Based on capabilities, AI includes Narrow AI (weak AI), General AI, and super AI. Based on functionalities, AI includes Relative Machines, limited memory, theory of mind, self-awareness.

3. How Does AI Help in Marketing?

AI helps marketers to strategize their marketing campaigns and keep data of their prospective clients and consumers.

4. Give an Example of a Relative Machine?

IBM’s deep blue system and Google’s Alpha go are examples of reactive machines.

5. How can Artificial Intelligence help us?

Artificial Intelligence can help us in many ways. It is already helping us in some cases. For example, if we think about the robots used in a factory, they all run on the principle of Artificial Intelligence. In the automobile sector, some vehicles have been invented that don't need any humans to drive them, they are self-driving. The search engines these days are also AI-powered. There are many other uses of Artificial Intelligence as well.

introduction of artificial intelligence essay

10 May 2022

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Introduction to artificial intelligence

Artificial intelligence applications are all around us, but what does it really mean? In this article, Kumar Abhishek explains the history and progress of artificial intelligence.

Artificial intelligence (AI) is the ability of machines to replicate or enhance human intellect, such as reasoning and learning from experience. Artificial intelligence has been used in computer programs for years, but it is now applied to many other products and services. For example, some digital cameras can determine what objects are present in an image using artificial intelligence software. In addition, experts predict many more innovative uses for artificial intelligence in the future, including smart electric grids.

AI uses techniques from probability theory, economics, and algorithm design to solve practical problems. In addition, the AI field draws upon computer science, mathematics, psychology, and linguistics. Computer science provides tools for designing and building algorithms, while mathematics offers tools for modeling and solving the resulting optimization problems.

Although the concept of AI has been around since the 19th century, when Alan Turing first proposed an “imitation game” to assess machine intelligence, it only became feasible to achieve in recent decades due to the increased availability of computing power and data to train AI systems.

To understand the idea behind AI, you should think about what distinguishes human intelligence from that of other creatures – our ability to learn from experiences and apply these lessons to new situations. We can do this because of our advanced brainpower; we have more neurons than any animal species.

Today’s computers don’t match the human biological neural network – not even close. But they have one significant advantage over us: their ability to analyze vast amounts of data and experiences much faster than humans could ever hope.

AI lets you focus on the most critical tasks and make better decisions based on acquired data related to a use case. It can be used for complex tasks, such as predicting maintenance requirements, detecting credit card fraud, and finding the best route for a delivery truck. In other words, AI can automate many business processes leaving you to concentrate on your core business.

Research in the field is concerned with producing machines to automate tasks requiring intelligent behavior. Examples include control, planning and scheduling, the ability to answer diagnostic and consumer questions, handwriting, natural language processing and perception, speech recognition, and the ability to move and manipulate objects.

History of AI and how it has progressed over the years

With so much attention on modern artificial intelligence, it is easy to forget that the field is not brand new. AI has had a number of different periods, distinguished by whether the focus was on proving logical theorems or trying to mimic human thought via neurology.

Artificial intelligence dates back to the late 1940s when computer pioneers like Alan Turing and John von Neumann first started examining how machines could “think.” However, a significant milestone in AI occurred in 1956 when researchers proved that a machine could solve any problem if it were allowed to use an unlimited amount of memory. The result was a program called the General Problem Solver (GPS).

Over the next two decades, research efforts focused on applying artificial intelligence to real-world problems. This development led to expert systems, which allow machines to learn from experience and make predictions based on gathered data. Expert systems aren’t as complex as human brains, but they can be trained to identify patterns and make decisions based on that data. They’re commonly used in medicine and manufacturing today.

A second major milestone came in 1965 with the development of programs like Shakey the robot and ELIZA, which automated simple conversations between humans and machines. These early programs paved the way for more advanced speech recognition technology, eventually leading to Siri and Alexa.

The initial surge of excitement around artificial intelligence lasted about ten years. It led to significant advances in programming language design, theorem proving, and robotics. But it also provoked a backlash against over-hyped claims that had been made for the field, and funding was cut back sharply around 1974.

After a decade without much progress, interest revived in the late 1980s. This revival was primarily driven by reports that machines were becoming better than humans at “narrow” tasks like playing checkers or chess and advances in computer vision and speech recognition. This time, the emphasis was on building systems that could understand and learn from real-world data with less human intervention.

These developments continued slowly until 1992, when interest began to increase again. First, technological advances in computing power and information storage helped boost interest in research on artificial intelligence. Then, in the mid-1990s, another major boom was driven by considerable advances in computer hardware that had taken place since the early 1980s. The result has been dramatic improvements in performance on several significant benchmark problems, such as image recognition, where machines are now almost as good as humans at some tasks.

The early years of the 21st century were a period of significant progress in artificial intelligence. The first major advance was the development of the self-learning neural network. By 2001, its performance had already surpassed human beings in many specific areas, such as object classification and machine translation. Over the next few years, researchers improved its performance across a range of tasks, thanks to improvements in the underlying technologies.

The second significant advancement in this period was the development of generative model-based reinforcement learning algorithms. Generative models can generate novel examples from a given class, which helps learn complex behaviors from very little data. For example, they can be used to learn how to control a car from only 20 minutes of driving experience.

In addition to these two advances, there have been several other significant developments in AI over the past decade. There has been an increasing emphasis on using deep neural networks for computer vision tasks, such as object recognition and scene understanding. There has also been an increased focus on using machine learning tools for natural language processing tasks such as information extraction and question answering. Finally, there has been a growing interest in using these same tools for speech recognition tasks like automatic speech recognition (ASR) and speaker identification (SID).

Different fields under AI to clear common misconceptions

Artificial Intelligence is the most trending field of computer science. However, with all the new technology and research, it’s growing so fast that it can be confusing to understand what is what. Furthermore, there are many different fields within AI, each one having its specific algorithms. Therefore, it’s essential to know that AI is not a single field but a combination of various fields.

Artificial Intelligence (AI) is the general term for being able to make computers do things that require intelligence if done by humans. AI can be broken down into two major fields, Machine Learning (ML) and Neural Networks (NN). Both are subfields under Artificial Intelligence, and each one has its methods and algorithms to help solve problems.

An image showing 3 circles. Deep Learning is the innermost circle. Outside of that is Machine Learning., and the largest circle encompassing the others is Artificial Intelligence t

Machine learning

Machine Learning (ML) makes computers learn from data and experience to improve their performance on some tasks or decision-making processes. ML uses statistics and probability theory for this purpose. Machine learning uses algorithms to parse data, learn from it, and make determinations without explicit programming. Machine learning algorithms are often categorized as supervised or unsupervised. Supervised algorithms can apply what has been learned in the past to new data sets; unsupervised algorithms can draw inferences from datasets. Machine learning algorithms are designed to strive to establish linear and non-linear relationships in a given set of data. This feat is achieved by statistical methods used to train the algorithm to classify or predict from a dataset.

Deep learning

Deep learning is a subset of machine learning that uses multi-layered artificial neural networks to deliver state-of-the-art accuracy in object detection, speech recognition and language translation. Deep learning is a crucial technology behind driverless cars and enables the machine analysis of large amounts of complex data — for example, recognizing the faces of people who appear in an image or video.

Neural networks

Neural networks are inspired by biological neurons in the human brain and are composed of layers of connected nodes called “neurons” that contain mathematical functions to process incoming data and predict an output value. Artificial neural network learns by example, similarly to how humans learn from our parents, teachers, and peers. They consist of at least three layers: an input layer, hidden layers, and an output layer. Each layer contains nodes (also known as neurons) which have weighted inputs that compute the output.

A image showing a graph. Y axis is performance, X axis is the amount of data. The Deep Learning curve continues to go up with more data which Traditional machine learning plateaus.

The performance of traditional machine learning models plateau and throwing any more data doesn’t help improve the performance. Deep learning models continue to improve in performance with more data.

These fields have different algorithms, depending on the use case. For example, we have decision trees, random forests, boosting, support vector machines (SVM), k-nearest neighbors (kNN), and others for machine learning. For neural networks, we have convolutional neural networks (CNNs), recurrent neural networks (RNNs), long short-term memory networks (LSTMs), and more.

However, classifying AI according to its strength and capabilities would mean further subdividing it into “narrow AI” and “general AI.” Narrow AI is about getting machines to do one task really well, like image recognition or playing chess. General AI means devices that can do everything humans can do and more. Today’s research focuses on narrow AI, but many researchers would like machine learning to eventually achieve general AI.

How AI stands out in different industries.

AI is a booming technology that the global community has accepted. It has been revolutionizing the industry from various sectors for quite some time. It is a comprehensive technology that is being applied in almost every industry. This section discusses how AI is impacting service delivery in various sectors.

Fully self-driving cars are now a reality. Tesla is the first company to make a car with all of the sensors, cameras, and software needed for a computer to drive itself from start to finish. Trucks may be the next primary target for autonomy: self-driving trucks will enormously impact road safety and infrastructure and save companies money by reducing labor costs.

A few other industries are also implementing AI. For example, in finance, AI helps with forecasting and supports hedge-fund investment decisions. Predictive analytics (or forecasting) applies artificial intelligence using machine learning and statistical techniques to make predictions about future events based on previous data. For example, you can use forecasting to predict product sales, customer demand, or even stock prices. One popular example of predictive analytics is Amazon’s product recommendations engine (also known as “Customers who bought this item also bought”). It uses past purchase data from millions of customers to recommend products based on the users’ preferences.

In healthcare, AI is helping doctors to diagnose diseases by gathering data from health records, scanning reports, and medical images. This helps doctors to make faster diagnoses and guide the patient for further tests or prescribe medications. In addition, AI can be used in the treatment process by monitoring patients and alerting their doctors when something goes wrong. According to Forbes, AI will save over 7 million lives in 2035.

In retail, AI does everything from stock management to customer service chatbots. As a result, many businesses are taking advantage of AI to improve productivity, efficiency, and accuracy. In addition, companies find new ways to use AI to make life easier for their customers and employees, from product design to customer service.

The current state of AI-based software systems.

The recent advancements in AI have led to the emergence of a new type of system called Generative Adversarial Networks (GANs), which generate realistic images, text, or audio. Due to their remarkable capabilities, some people are concerned that this technology could replace humans in the future.

GANs are just one example of how AI is changing our lives. This section explores more current AI examples and its applications in software systems such as GPT3 , DALL.E , and virtual reality/augmented reality (VR/AR).

AI-based software systems are comprised of many layers such as foundational models, advanced algorithms, and automated reasoning tools. Some of the most popular AI-based systems that use these layers include GPT3, DALL.E, AlphaGo, RoBERTa, and many others.

DALL.E and GPT3 are large-scale models that have achieved remarkable results in computer vision and natural language processing (NLP).

The GPT3 model is an NLP model based on a deep learning algorithm called transformers. It was trained on a corpus of text from Common Crawl and published in 2020. GPT3 uses a large dataset trained in the English language to produce outputs based on the inputted information. The model can be trained to perform any task imaginable, from generating text to solving math problems. Also, we can use GPT3 to generate text, translate between languages, answer questions about images, and more.

The DALL.E model is an image generator based on a deep learning algorithm called variational autoencoders (VAEs). Similarly, DALL.E can be trained using an image dataset to produce images based on the inputted text descriptions. It was trained on datasets such as ImageNet and published in 2021. We can use DALL.E to generate images that match captions or URLs given by users. These models have been developed by OpenAI, which has close ties to the US government and military-industrial complex (MIC).

DeepMind created AlphaGo as a program that would play the ancient game Go without anyone’s help. The game is similar to chess but much more complex due to its simple rules and many possible moves per turn. AlphaGo used reinforcement learning to learn how to play the game better over time by playing against itself repeatedly until it mastered every possible situation that could occur in a game of Go with 100% accuracy.

RoBERTa is an algorithm from Facebook AI Research (FAIR) that uses deep learning techniques to solve problems in natural language processing (NLP), such as sentence classification or machine translation.

The Future of AI. What to expect from AI in the next few years or decades

Artificial intelligence has come a long way, but it’s about to make a huge leap. Artificial general intelligence (AGI), the kind of AI capable of doing any intellectual task that a human being can do, is still a ways off, but we’re already starting to see plenty of progress in other areas of AI. Here’s what you can expect soon:

Artificial Intelligence will make more jobs obsolete as it takes over more and more tasks

The reason why is simple: if you can replace one person with an AGI system, you don’t need one computer to do the work – you can spread it out across thousands or millions of computers. That’s only possible because a general AI system can learn from past experiences and improve itself, meaning that it doesn’t have to be reprogrammed for every new task. In fact, there’s no reason why an AGI system would need humans at all – once it learns enough, it could design its own machines or find ways to automate entire industries.

The advent of AI is transforming the business landscape and changing people’s lives for the better. In the coming years, most industries will see a significant transformation due to new-age technologies like cloud computing, Internet of Things (IoT), and Big Data Analytics. All these factors profoundly influence how businesses operate today and are also finding applications in other areas like military, healthcare, and infrastructure development.

To build an engaging metaverse that appeals to millions of users who want to learn, create, and inhabit virtual worlds, AI must be used to enable realistic simulations of the real world. People need to feel immersed in the environments they participate in. AI is helping to achieve this reality by making objects look more realistic and enabling computer vision so users can interact with simulated objects using their body movements.

Concerns surrounding the advancement and usage of AI

AI is a very powerful idea, but it’s not magic. The key thing to remember about AI is that it learns from data. The model and algorithm underneath are only as good as the data put into them. This means that data availability, bias, improper labeling, and privacy issues can all significantly impact the performance of an AI model.

Data availability and quality are critical for training an AI system. Some of the biggest concerns surrounding AI today relate to potentially biased datasets that may produce unsatisfactory results or exacerbate gender/racial biases within AI systems. When we research different types of machine learning models, we find that certain models are more susceptible to bias than others. For example, when using deep learning models (e.g., neural networks), the training process can introduce bias into the model if a biased dataset is used during training.

However, other machine learning models (e.g., random forests) can be less sensitive to the bias in the data during training. For example, if a dataset contains information about many different variables but only one variable is used to make decisions (e.g., gender), this model will tend to be more biased toward that variable than random forests that consider all variables equally weighted by default.

Other concerns need to be taken into account with the advancement and usage of AI. These include data availability, computational power, and privacy, such as health data. People’s data is needed to develop models, but how do we get such data given how protected health data needs to be.

As artificial intelligence becomes more common, it’s only natural that there are increasing requirements for processing power. As a result, AI researchers use supercomputers to develop algorithms and models on a massive and complex scale.

This is especially true of deep learning, a type of machine learning that uses algorithms to recognize patterns in large data sets like images or sound. The main issue with DL is that it requires enormous computational power. To train a neural network using DL, you need to feed vast amounts of data into the system — for example, thousands or millions of pictures — and then let it figure out how to tell one from another on its own. This training process is complex and laborious, but it is also computationally expensive. Model development can take days or even weeks on a single high-end GPU or CPU capable of delivering lots of computational power. To make matters worse, once you train the model, you need a supercomputer to execute the model at full capacity. Google’s investments in TPUs (Tensor Processing Units) attempt to solve this problem using state-of-the-art hardware technology.

Another source of concern in the development of AI is how automated systems will ultimately be used. For example, should we consider holding corporations responsible for the actions of intelligent machines they develop? Or should we consider holding machine developers accountable for their work?

Artificial intelligence (AI) is the intelligence of machines and the branch of computer science that aims to create it. AI is today’s dominant technology and will continue to be a significant factor in various industries for years to come. As AI systems become more advanced, they are not only poised to disrupt multiple industries with their impact but also raise concerns about how we should handle such incredible power.

This field has evolved a great deal over the years. It has gone from being a subject of popular science fiction to a significant part of our lives today. By examining AI from its past, it is possible to better understand its present and predict its future, as we have done in this article.

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Kumar Abhishek

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This guide provides an introduction to artificial intelligence (AI), with a focus on generative AI. You’ll find explanations of the benefits and limitations as well as support to use, cite, research, and teach with artificial intelligence.

AI tools and the legal and ethical landscape surrounding their use are changing rapidly. We will periodically update this guide and provide the date of last update to inform your use.

Updated: February 2024

Glossary of AI Terms

These definitions were generated using the Llama 2 large language model and reviewed for accuracy by a Libraries staff member. Generating content like this can be done efficiently using a large language model, but it is important to remember to review the output carefully and acknowledge the source.

Artificial intelligence refers to the development of computer systems that can perform tasks that typically require human intelligence, such as visual perception, speech recognition, decision-making, and natural language understanding.

Types of Artificial Intelligence

A chatbot is a software application that uses natural language processing (NLP) and machine learning to simulate conversation with humans, either via text or voice interfaces.

  • Generative AI

Generative artificial intelligence refers to algorithms and models that can generate new content or data, such as images, videos, music, or text, based on patterns learned from existing information.

  • Machine Learning (ML)

Machine learning is a subset of artificial intelligence that involves training computer systems to learn from data and improve their performance over time through experience.

  • Natural Language Processing (NLP)

NLP is a subfield of artificial intelligence that deals with the interaction between computers and human language, including text and speech processing, sentiment analysis, machine translation, and dialogue systems.

  • Large Language Model (LLM)

A large language model is a type of machine learning model that is trained on vast amounts of text data to generate language outputs that are coherent and contextually appropriate.

Using Large Language Models (LLMs)

  • Hallucination

In the context of AI, hallucination refers to the phenomenon where a model generates inaccurate or imaginary output that cannot be explained by its training data, often due to overfitting or underfitting .

A prompt is a specific task or question that is given to an AI system to elicit a response or output.

  • Prompt Engineering

Prompt engineering is the process of designing and refining prompts to elicit desired responses or behaviors from AI systems, in order to improve their performance and versatility.

Understanding Large Language Models (LLMs)

Parameters are settings or values that are adjusted during the training process to optimize the performance of an AI model, such as the learning rate, regularization strength, or number of hidden layers.

  •  Temperature

In the context of generative AI, temperature refers to a parameter that controls the "randomness" or "diversity" of generated samples, with higher temperatures resulting in more diverse and less predictable outputs.

In Natural Language Processing and machine learning, tokens refer to individual words or phrases in a text dataset, which are used as input features for models to analyze and understand the meaning of the text.

  • Training Data

Training data is the set of examples or inputs used to train an AI system, which helps the model learn patterns and relationships in the data and make predictions or decisions.

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Essays on Artificial Intelligence

Writing an essay on artificial intelligence is not just an academic exercise; it's a chance to explore the cutting-edge innovations and the profound impact AI has on our lives. 🚀 So, get ready to unlock the potential of AI with your words!

Artificial ... Read More Writing an essay on artificial intelligence is not just an academic exercise; it's a chance to explore the cutting-edge innovations and the profound impact AI has on our lives. 🚀 So, get ready to unlock the potential of AI with your words! Artificial Intelligence Essay Topics for "Artificial Intelligence" 📝

Choosing the right topic is key to writing a compelling essay. Here's how to pick the perfect one:

Artificial Intelligence Argumentative Essay 🤨

Argumentative AI essays require you to take a stance on AI-related issues. Here are ten thought-provoking topics:

  • 1. The ethical implications of AI in autonomous weaponry.
  • 2. Should AI be granted legal personhood and rights?
  • 3. Analyze the impact of AI on the job market and employment prospects.
  • 4. The role of AI in addressing climate change and environmental challenges.
  • 5. Discuss the risks and benefits of AI in healthcare and medical diagnostics.
  • 6. AI's impact on privacy and surveillance in modern society.
  • 7. Evaluate the use of AI in education and personalized learning.
  • 8. The role of AI in improving cybersecurity and data protection.
  • 9. Discuss the potential biases and discrimination in AI algorithms.
  • 10. AI and its implications for creativity and the arts.

Artificial Intelligence Cause and Effect Essay 🤯

Dive into cause and effect relationships in the AI realm with these topics:

  • 1. Explore how AI-powered virtual assistants have changed communication habits.
  • 2. Analyze the effects of AI-driven predictive policing on crime rates.
  • 3. Discuss how AI-driven healthcare advancements have extended human lifespans.
  • 4. The consequences of AI-powered autonomous vehicles on transportation and traffic safety.
  • 5. Investigate the impact of AI algorithms on social media echo chambers and polarization.
  • 6. The influence of AI-driven personalized marketing on consumer behavior.
  • 7. Explore how AI has revolutionized the entertainment industry and storytelling.
  • 8. Analyze the cause and effect of AI's role in financial markets and investment strategies.
  • 9. Discuss the effects of AI on reducing energy consumption and sustainable living.
  • 10. The consequences of AI in aiding scientific research and discovery.

Artificial Intelligence Opinion Essay 😌

Express your personal views and interpretations on AI through these essay topics:

  • 1. Share your opinion on the potential dangers of superintelligent AI.
  • 2. Discuss your perspective on AI's role in enhancing human capabilities.
  • 3. Express your thoughts on the future of work in an AI-dominated world.
  • 4. Debate the significance of AI in addressing global challenges like pandemics.
  • 5. Share your views on the ethical responsibilities of AI developers and researchers.
  • 6. Discuss the impact of AI on human creativity and innovation.
  • 7. Express your opinion on AI's influence on education and personalized learning.
  • 8. Debate the ethics of AI in decision-making, such as self-driving car dilemmas.
  • 9. Share your perspective on AI's potential to bridge the digital divide and promote equity.
  • 10. Discuss your favorite AI-related invention or innovation and its implications.

Artificial Intelligence Informative Essay 🧐

Inform and educate your readers with these informative AI essay topics:

  • 1. Explore the history and evolution of artificial intelligence.
  • 2. Provide an in-depth analysis of popular AI technologies like deep learning and neural networks.
  • 3. Investigate the significance of AI in autonomous robotics and space exploration.
  • 4. Analyze the role of AI in natural language processing and language translation.
  • 5. Examine the applications of AI in climate modeling and environmental conservation.
  • 6. Explore the cultural and societal impacts of AI in science fiction literature and films.
  • 7. Provide insights into the ethics of AI in medical decision-making and diagnosis.
  • 8. Analyze the potential for AI in disaster response and emergency management.
  • 9. Discuss the role of AI in enhancing cybersecurity and threat detection.
  • 10. Examine the future trends and possibilities of AI in various industries.

Artificial Intelligence Essay Example 📄

Artificial intelligence thesis statement examples 📜.

Here are five examples of strong thesis statements for your AI essay:

  • 1. "The rapid advancements in artificial intelligence present both unprecedented opportunities and ethical dilemmas, as we navigate the journey toward an AI-driven future."
  • 2. "In analyzing the impact of AI on healthcare, we unveil a transformative force that promises to revolutionize medical diagnosis and treatment, but also raises concerns about data privacy and security."
  • 3. "The development of superintelligent AI systems demands careful consideration of ethical frameworks to ensure their responsible and beneficial integration into society."
  • 4. "Artificial intelligence is not a replacement for human creativity but a powerful tool that amplifies our capabilities, ushering in an era of unprecedented innovation and discovery."
  • 5. "AI-driven autonomous vehicles represent a technological leap that holds the potential to reshape transportation, reduce accidents, and increase accessibility, but also raises questions about liability and safety."

Artificial Intelligence Essay Introduction Examples 🚀

Here are three captivating introduction paragraphs to begin your essay:

  • 1. "In a world driven by data and algorithms, artificial intelligence has emerged as both a beacon of innovation and a source of profound ethical contemplation. As we embark on this essay journey into the realm of AI, we peel back the layers of silicon and software to explore the implications, promises, and challenges of our AI-driven future."
  • 2. "Imagine a world where machines not only assist us but also think, learn, and adapt. The rise of artificial intelligence has ignited a conversation that transcends technology—it delves into the very essence of human potential and the responsibilities we bear as creators. Join us as we navigate the AI landscape, one algorithm at a time."
  • 3. "In an era marked by digital transformations and the ubiquity of smart devices, artificial intelligence stands as the sentinel of change. As we step into the world of AI analysis, we are confronted with a paradox: the immense power of machines and the ethical dilemmas they pose. Together, let's dissect the AI phenomenon, from its inception to its potential to shape the destiny of humanity."

Artificial Intelligence Conclusion Examples 🌟

Conclude your essay with impact using these examples:

  • 1. "As we draw the curtains on this AI exploration, we stand at the intersection of innovation and ethics. Artificial intelligence, with all its wonders and complexities, challenges us to not only harness its power for progress but also to ensure its responsible and ethical use. The journey continues, and the conversation evolves as we navigate the evolving landscape of AI."
  • 2. "In the closing frame of our AI analysis, we reflect on the ever-expanding possibilities and responsibilities that AI brings to our doorstep. The pages of this essay mark a beginning—a call to action. Together, we have explored the AI landscape, and the future is now in our hands, waiting for our choices to shape it."
  • 3. "As the AI narrative reaches its conclusion, we find ourselves at the crossroads of human ingenuity and artificial intelligence. The journey has been both enlightening and thought-provoking, reminding us that the future of AI is a collaborative endeavor, guided by ethics, curiosity, and a shared vision of a better world."

Understanding The Real Potential and Limits of Artificial Intelligence

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Ethical Issues in Using Ai Technology Today

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Artificial intelligence (AI) refers to the intellectual capabilities exhibited by machines, contrasting with the innate intelligence observed in living beings, such as animals and humans.

The inception of artificial intelligence research as an academic field can be traced back to its establishment in 1956. It was during the renowned Dartmouth conference of the same year that artificial intelligence acquired its distinctive name, definitive purpose, initial accomplishments, and notable pioneers, thereby earning its reputation as the birthplace of AI. The esteemed figures of Marvin Minsky and John McCarthy are widely recognized as the founding fathers of this discipline.

Early pioneers such as John McCarthy, Marvin Minsky, and Allen Newell played instrumental roles in shaping the foundations of AI research. In the following years after its original inception, AI witnessed both periods of optimism and periods of skepticism, as researchers explored different approaches and techniques. Notable breakthroughs include the development of expert systems in the 1970s, which aimed to replicate human knowledge and reasoning, and the emergence of machine learning algorithms in the 1980s and 1990s. The turn of the 21st century witnessed significant advancements in AI, with the rise of big data, powerful computing technologies, and deep learning algorithms. This led to remarkable achievements in areas such as natural language processing, computer vision, and autonomous systems.

There are four types of artificial intelligence: reactive machines, limited memory, theory of mind and self-awareness.

Healthcare: AI assists in medical diagnosis, drug discovery, personalized treatment plans, and analyzing medical images. Finance: AI is used for automated trading, fraud detection, risk assessment, and customer service through chatbots. Transportation: AI powers autonomous vehicles, traffic optimization, logistics, and supply chain management. Entertainment: AI contributes to recommendation systems, AI-generated music and art, virtual reality experiences, and content creation. Cybersecurity: AI helps in detecting and preventing cyber threats and enhancing network security. Agriculture: AI optimizes farming practices, crop management, and precision agriculture. Education: AI enables personalized learning, adaptive assessments, and intelligent tutoring systems. Natural Language Processing: AI facilitates language translation, voice assistants, chatbots, and sentiment analysis. Robotics: AI powers robots in various applications, such as manufacturing, healthcare, and exploration. Environmental Conservation: AI aids in environmental monitoring, wildlife protection, and climate modeling.

John McCarthy: Coined the term "artificial intelligence" and organized the Dartmouth Conference in 1956, which is considered the birth of AI as an academic discipline. Marvin Minsky: A cognitive scientist and AI pioneer, Minsky co-founded the Massachusetts Institute of Technology's AI Laboratory and made notable contributions to robotics and cognitive psychology. Geoffrey Hinton: Renowned for his work on neural networks and deep learning, Hinton's research has greatly advanced the field of AI and revolutionized areas such as image and speech recognition. Andrew Ng: An influential figure in the field of AI, Ng co-founded Google Brain, led the development of the deep learning framework TensorFlow, and has made significant contributions to machine learning algorithms. Fei-Fei Li: A prominent researcher in computer vision and AI, Li has made groundbreaking contributions to image recognition and has been a strong advocate for responsible and ethical AI development.. Demis Hassabis: Co-founder of DeepMind, a leading AI research company, Hassabis has made notable contributions to areas such as deep reinforcement learning and has led the development of groundbreaking AI systems. Elon Musk: Although primarily known for his role in space exploration and electric vehicles, Musk has also made notable contributions to AI through his involvement in companies like OpenAI and Neuralink, advocating for AI safety and ethics.

1. According to a report by IDC, global spending on AI systems is expected to reach $98.4 billion in 2023, indicating a significant increase from the $37.5 billion spent in 2019. 2. The job market for AI professionals is thriving. LinkedIn's 2021 Emerging Jobs Report listed AI specialist as one of the top emerging jobs, with a 74% annual growth rate over the past four years. 3. AI-powered chatbots are revolutionizing customer service. A study by Oracle found that 80% of businesses plan to use chatbots by 2022. Furthermore, 58% of consumers have already interacted with chatbots for customer support, indicating the growing acceptance and adoption of AI in enhancing customer experiences. 4. McKinsey Global Institute estimates that by 2030, automation and AI technologies could contribute to a global economic impact of $13 trillion. 5. The healthcare industry is leveraging AI for improved patient care. A study published in the journal Nature Medicine reported that an AI model was able to detect breast cancer with an accuracy of 94.5%, outperforming human radiologists.

The topic of artificial intelligence (AI) holds immense importance in today's world, making it an intriguing subject to explore in an essay. AI has revolutionized multiple facets of human life, ranging from technology and business to healthcare and transportation. Understanding its significance is crucial for comprehending the potential and impact of this rapidly evolving field. Firstly, AI has the power to reshape industries and transform economies. It enables automation, streamlines processes, and enhances efficiency, leading to increased productivity and economic growth. Moreover, AI advancements have the potential to address complex societal challenges, such as healthcare accessibility, environmental sustainability, and resource management. Secondly, AI raises ethical considerations and socio-economic implications. Discussions on privacy, bias, job displacement, and AI's role in decision-making become essential for navigating its responsible implementation. Examining the ethical dimensions of AI fosters critical thinking and encourages the development of guidelines and regulations to ensure its ethical use. Lastly, exploring AI allows us to envision the future possibilities and risks associated with this technology. It sparks discussions on the boundaries of machine intelligence, the potential for sentient AI, and the impact on human existence. By studying AI, we gain insights into technological progress, its limitations, and the responsibilities associated with harnessing its potential.

1. Russell, S. J., & Norvig, P. (2016). Artificial Intelligence: A Modern Approach (3rd ed.). Prentice Hall. 2. Goodfellow, I., Bengio, Y., & Courville, A. (2016). Deep Learning. MIT Press. 3. Kurzweil, R. (2005). The Singularity Is Near: When Humans Transcend Biology. Viking. 4. Bostrom, N. (2014). Superintelligence: Paths, Dangers, Strategies. Oxford University Press. 5. Chollet, F. (2017). Deep Learning with Python. Manning Publications. 6. Domingos, P. (2018). The Master Algorithm: How the Quest for the Ultimate Learning Machine Will Remake Our World. Basic Books. 7. Ng, A. (2017). Machine Learning Yearning. deeplearning.ai. 8. Marcus, G. (2018). Rebooting AI: Building Artificial Intelligence We Can Trust. Vintage. 9. Winfield, A. (2018). Robotics: A Very Short Introduction. Oxford University Press. 10. Shalev-Shwartz, S., & Ben-David, S. (2014). Understanding Machine Learning: From Theory to Algorithms. Cambridge University Press.

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introduction of artificial intelligence essay

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The brief history of artificial intelligence: The world has changed fast – what might be next?

Despite their brief history, computers and ai have fundamentally changed what we see, what we know, and what we do. little is as important for the future of the world, and our own lives, as how this history continues..

To see what the future might look like, it is often helpful to study our history. This is what I will do in this article. I retrace the brief history of computers and artificial intelligence to see what we can expect for the future.

How did we get here?

How rapidly the world has changed becomes clear by how even quite recent computer technology feels ancient today. Mobile phones in the ‘90s were big bricks with tiny green displays. Two decades before that, the main storage for computers was punch cards.

In a short period, computers evolved so quickly and became such an integral part of our daily lives that it is easy to forget how recent this technology is. The first digital computers were only invented about eight decades ago, as the timeline shows.

introduction of artificial intelligence essay

Since the early days of this history, some computer scientists have strived to make machines as intelligent as humans. The next timeline shows some of the notable artificial intelligence (AI) systems and describes what they were capable of.

The first system I mention is the Theseus. It was built by Claude Shannon in 1950 and was a remote-controlled mouse that was able to find its way out of a labyrinth and could remember its course. 1 In seven decades, the abilities of artificial intelligence have come a long way.

introduction of artificial intelligence essay

The language and image recognition capabilities of AI systems have developed very rapidly

The chart shows how we got here by zooming into the last two decades of AI development. The plotted data stems from a number of tests in which human and AI performance were evaluated in five different domains, from handwriting recognition to language understanding.

Within each of the five domains, the initial performance of the AI system is set to -100, and human performance in these tests is used as a baseline set to zero. This means that when the model’s performance crosses the zero line is when the AI system scored more points in the relevant test than the humans who did the same test. 3

Just 10 years ago, no machine could reliably provide language or image recognition at a human level. But, as the chart shows, AI systems have become steadily more capable and are now beating humans in tests in all these domains. 4

Outside of these standardized tests, the performance of these AIs is mixed. In some real-world cases, these systems are still performing much worse than humans. On the other hand, some implementations of such AI systems are already so cheap that they are available on the phone in your pocket: image recognition categorizes your photos and speech recognition transcribes what you dictate.

Language and image recognition capabilities of AI systems have improved rapidly 2

introduction of artificial intelligence essay

From image recognition to image generation

The previous chart showed the rapid advances in the perceptive abilities of artificial intelligence. AI systems have also become much more capable of generating images.

This series of nine images shows the development over the last nine years. None of the people in these images exist; all were generated by an AI system.

The series begins with an image from 2014 in the top left, a primitive image of a pixelated face in black and white. As the first image in the second row shows, just three years later, AI systems were already able to generate images that were hard to differentiate from a photograph.

In recent years, the capability of AI systems has become much more impressive still. While the early systems focused on generating images of faces, these newer models broadened their capabilities to text-to-image generation based on almost any prompt. The image in the bottom right shows that even the most challenging prompts – such as “A Pomeranian is sitting on the King’s throne wearing a crown. Two tiger soldiers are standing next to the throne” – are turned into photorealistic images within seconds. 6

Timeline of images generated by artificial intelligence 5

Timeline of images generated by artificial intelligence

Language recognition and production is developing fast

Just as striking as the advances of image-generating AIs is the rapid development of systems that parse and respond to human language.

Shown in the image are examples from an AI system developed by Google called PaLM. In these six examples, the system was asked to explain six different jokes. I find the explanation in the bottom right particularly remarkable: the AI explains an anti-joke specifically meant to confuse the listener.

AIs that produce language have entered our world in many ways over the last few years. Emails get auto-completed, massive amounts of online texts get translated, videos get automatically transcribed, school children use language models to do their homework, reports get auto-generated, and media outlets publish AI-generated journalism.

AI systems are not yet able to produce long, coherent texts. In the future, we will see whether the recent developments will slow down – or even end – or whether we will one day read a bestselling novel written by an AI.

Output of the AI system PaLM after being asked to interpret six different jokes 7

introduction of artificial intelligence essay

Where we are now: AI is here

These rapid advances in AI capabilities have made it possible to use machines in a wide range of new domains:

When you book a flight, it is often an artificial intelligence, no longer a human, that decides what you pay. When you get to the airport, it is an AI system that monitors what you do at the airport. And once you are on the plane, an AI system assists the pilot in flying you to your destination.

AI systems also increasingly determine whether you get a loan , are eligible for welfare or get hired for a particular job. Increasingly they help determine who gets released from jail .

Several governments have purchased autonomous weapons systems for warfare, and some use AI systems for surveillance and oppression .

AI systems help to program the software you use and translate the texts you read. Virtual assistants , operated by speech recognition, have entered many households over the last decade. Now self-driving cars are becoming a reality.

In the last few years, AI systems helped to make progress on some of the hardest problems in science.

Large AIs called recommender systems determine what you see on social media, which products are shown to you in online shops, and what gets recommended to you on YouTube. Increasingly they are not just recommending the media we consume, but based on their capacity to generate images and texts, they are also creating the media we consume.

Artificial intelligence is no longer a technology of the future; AI is here, and much of what is reality now would have looked like sci-fi just recently. It is a technology that already impacts all of us, and the list above includes just a few of its many applications .

The wide range of listed applications makes clear that this is a very general technology that can be used by people for some extremely good goals – and some extraordinarily bad ones, too. For such “dual-use technologies”, it is important that all of us develop an understanding of what is happening and how we want the technology to be used.

Just two decades ago, the world was very different. What might AI technology be capable of in the future?

What is next?

The AI systems that we just considered are the result of decades of steady advances in AI technology.

The big chart below brings this history over the last eight decades into perspective. It is based on the dataset produced by Jaime Sevilla and colleagues. 8

The rise of artificial intelligence over the last 8 decades: As training computation has increased, AI systems have become more powerful 9

introduction of artificial intelligence essay

Each small circle in this chart represents one AI system. The circle’s position on the horizontal axis indicates when the AI system was built, and its position on the vertical axis shows the amount of computation used to train the particular AI system.

Training computation is measured in floating point operations , or FLOP for short. One FLOP is equivalent to one addition, subtraction, multiplication, or division of two decimal numbers.

All AI systems that rely on machine learning need to be trained, and in these systems, training computation is one of the three fundamental factors that are driving the capabilities of the system. The other two factors are the algorithms and the input data used for the training. The visualization shows that as training computation has increased, AI systems have become more and more powerful.

The timeline goes back to the 1940s, the beginning of electronic computers. The first shown AI system is ‘Theseus’, Claude Shannon’s robotic mouse from 1950 that I mentioned at the beginning. Towards the other end of the timeline, you find AI systems like DALL-E and PaLM, whose abilities to produce photorealistic images and interpret and generate language we have just seen. They are among the AI systems that used the largest amount of training computation to date.

The training computation is plotted on a logarithmic scale so that from each grid line to the next, it shows a 100-fold increase. This long-run perspective shows a continuous increase. For the first six decades, training computation increased in line with Moore’s Law , doubling roughly every 20 months. Since about 2010, this exponential growth has sped up further, to a doubling time of just about 6 months. That is an astonishingly fast rate of growth. 10

The fast doubling times have accrued to large increases. PaLM’s training computation was 2.5 billion petaFLOP, more than 5 million times larger than AlexNet, the AI with the largest training computation just 10 years earlier. 11

Scale-up was already exponential and has sped up substantially over the past decade. What can we learn from this historical development for the future of AI?

Studying the long-run trends to predict the future of AI

AI researchers study these long-term trends to see what is possible in the future. 12

Perhaps the most widely discussed study of this kind was published by AI researcher Ajeya Cotra. She studied the increase in training computation to ask at what point the computation to train an AI system could match that of the human brain. The idea is that, at this point, the AI system would match the capabilities of a human brain. In her latest update, Cotra estimated a 50% probability that such “transformative AI” will be developed by the year 2040, less than two decades from now. 13

In a related article , I discuss what transformative AI would mean for the world. In short, the idea is that such an AI system would be powerful enough to bring the world into a ‘qualitatively different future’. It could lead to a change at the scale of the two earlier major transformations in human history, the agricultural and industrial revolutions. It would certainly represent the most important global change in our lifetimes.

Cotra’s work is particularly relevant in this context as she based her forecast on the kind of historical long-run trend of training computation that we just studied. But it is worth noting that other forecasters who rely on different considerations arrive at broadly similar conclusions. As I show in my article on AI timelines , many AI experts believe that there is a real chance that human-level artificial intelligence will be developed within the next decades, and some believe that it will exist much sooner.

Building a public resource to enable the necessary public conversation

Computers and artificial intelligence have changed our world immensely, but we are still in the early stages of this history. Because this technology feels so familiar, it is easy to forget that all of these technologies we interact with are very recent innovations and that the most profound changes are yet to come.

Artificial intelligence has already changed what we see, what we know, and what we do. This is despite the fact that this technology has had only a brief history.

There are no signs that these trends are hitting any limits anytime soon. On the contrary, particularly over the course of the last decade, the fundamental trends have accelerated: investments in AI technology have rapidly increased , and the doubling time of training computation has shortened to just six months.

All major technological innovations lead to a range of positive and negative consequences. This is already true of artificial intelligence. As this technology becomes more and more powerful, we should expect its impact to become greater still.

Because of the importance of AI, we should all be able to form an opinion on where this technology is heading and understand how this development is changing our world. For this purpose, we are building a repository of AI-related metrics, which you can find on OurWorldinData.org/artificial-intelligence .

We are still in the early stages of this history, and much of what will become possible is yet to come. A technological development as powerful as this should be at the center of our attention. Little might be as important for how the future of our world – and the future of our lives – will play out.

Acknowledgements: I would like to thank my colleagues Natasha Ahuja, Daniel Bachler, Julia Broden, Charlie Giattino, Bastian Herre, Edouard Mathieu, and Ike Saunders for their helpful comments to drafts of this essay and their contributions in preparing the visualizations.

On the Theseus see Daniel Klein (2019) – Mighty mouse , Published in MIT Technology Review. And this video on YouTube of a presentation by its inventor Claude Shannon.

Data from Kiela et al. (2021) – Dynabench: Rethinking Benchmarking in NLP. arXiv:2104.14337v1; https://doi.org/10.48550/arXiv.2104.14337

The chart shows that the speed at which these AI technologies developed increased over time. Systems for which development was started early – handwriting and speech recognition – took more than a decade to approach human-level performance, while more recent AI developments led to systems that overtook humans in only a few years. However, one should not overstate this point. To some extent, this is dependent on when the researchers started to compare machine and human performance. One could have started evaluating the system for language understanding much earlier, and its development would appear much slower in this presentation of the data.

It is important to remember that while these are remarkable achievements — and show very rapid gains — these are the results from specific benchmarking tests. Outside of tests, AI models can fail in surprising ways and do not reliably achieve performance that is comparable with human capabilities.

The relevant publications are the following:

2014: Goodfellow et al.: Generative Adversarial Networks

2015: Radford, Metz, and Chintala: Unsupervised Representation Learning with Deep Convolutional Generative Adversarial Networks

2016: Liu and Tuzel: Coupled Generative Adversarial Networks

2017: Karras et al.: Progressive Growing of GANs for Improved Quality, Stability, and Variation

2018: Karras, Laine, and Aila: A Style-Based Generator Architecture for Generative Adversarial Networks (StyleGAN from NVIDIA)

2019: Karras et al.: Analyzing and Improving the Image Quality of StyleGAN

AI-generated faces generated by this technology can be found on thispersondoesnotexist.com .

2020: Ho, Jain, and Abbeel: Denoising Diffusion Probabilistic Models

2021: Ramesh et al: Zero-Shot Text-to-Image Generation (first DALL-E from OpenAI; blog post ). See also Ramesh et al. (2022) – Hierarchical Text-Conditional Image Generation with CLIP Latents (DALL-E 2 from OpenAI; blog post ).

2022: Saharia et al: Photorealistic Text-to-Image Diffusion Models with Deep Language Understanding (Google’s Imagen; blog post )

Because these systems have become so powerful, the latest AI systems often don’t allow the user to generate images of human faces to prevent abuse.

From Chowdhery et al. (2022) –  PaLM: Scaling Language Modeling with Pathways . Published on arXiv on 7 Apr 2022.

See the footnote on the chart's title for the references and additional information.

The data is taken from Jaime Sevilla, Lennart Heim, Anson Ho, Tamay Besiroglu, Marius Hobbhahn, Pablo Villalobos (2022) – Compute Trends Across Three eras of Machine Learning . Published in arXiv on March 9, 2022. See also their post on the Alignment Forum .

The authors regularly update and extend their dataset, a helpful service to the AI research community. At Our World in Data, my colleague Charlie Giattino regularly updates the interactive version of this chart with the latest data made available by Sevilla and coauthors.

See also these two related charts:

Number of parameters in notable artificial intelligence systems

Number of datapoints used to train notable artificial intelligence systems

At some point in the future, training computation is expected to slow down to the exponential growth rate of Moore's Law. Tamay Besiroglu, Lennart Heim, and Jaime Sevilla of the Epoch team estimate in their report that the highest probability for this reversion occurring is in the early 2030s.

The training computation of PaLM, developed in 2022, was 2,700,000,000 petaFLOP. The training computation of AlexNet, the AI with the largest training computation up to 2012, was 470 petaFLOP. 2,500,000,000 petaFLOP / 470 petaFLOP = 5,319,148.9. At the same time, the amount of training computation required to achieve a given performance has been falling exponentially.

The costs have also increased quickly. The cost to train PaLM is estimated to be $9–$23 million, according to Lennart Heim, a researcher in the Epoch team. See Lennart Heim (2022) – Estimating PaLM's training cost .

Scaling up the size of neural networks – in terms of the number of parameters and the amount of training data and computation – has led to surprising increases in the capabilities of AI systems. This realization motivated the “scaling hypothesis.” See Gwern Branwen (2020) – The Scaling Hypothesis ⁠.

Her research was announced in various places, including in the AI Alignment Forum here: Ajeya Cotra (2020) –  Draft report on AI timelines . As far as I know, the report always remained a “draft report” and was published here on Google Docs .

The cited estimate stems from Cotra’s Two-year update on my personal AI timelines , in which she shortened her median timeline by 10 years.

Cotra emphasizes that there are substantial uncertainties around her estimates and therefore communicates her findings in a range of scenarios. She published her big study in 2020, and her median estimate at the time was that around the year 2050, there will be a 50%-probability that the computation required to train such a model may become affordable. In her “most conservative plausible”-scenario, this point in time is pushed back to around 2090, and in her “most aggressive plausible”-scenario, this point is reached in 2040.

The same is true for most other forecasters: all emphasize the large uncertainty associated with their forecasts .

It is worth emphasizing that the computation of the human brain is highly uncertain. See Joseph Carlsmith's New Report on How Much Computational Power It Takes to Match the Human Brain from 2020.

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Ethics of Artificial Intelligence pp 1–7 Cite as

The Ethics of Artificial Intelligence: An Introduction

  • Bernd Carsten Stahl 6 , 7 ,
  • Doris Schroeder 8 &
  • Rowena Rodrigues 9  
  • Open Access
  • First Online: 02 November 2022

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Part of the SpringerBriefs in Research and Innovation Governance book series (BRIEFSREINGO)

This chapter introduces the themes covered by the book. It provides an overview of the concept of artificial intelligence (AI) and some of the technologies that have contributed to the current high level of visibility of AI. It explains why using case studies is a suitable approach to engage a broader audience with an interest in AI ethics. The chapter provides a brief overview of the structure and logic of the book by indicating the content of the cases covered in each section. It concludes by identifying the concept of ethics used in this book and how it is located in the broader discussion of ethics, human rights and regulation of AI.

  • Artificial intelligence
  • Machine learning
  • Deep learning ethics

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The ethical challenges presented by artificial intelligence (AI) are one of the biggest topics of the twenty-first century. The potential benefits of AI are said to be numerous, ranging from operational improvements, such as the reduction of human error (e.g. in medical diagnosis), to the use of robots in hazardous situations (e.g. to secure a nuclear plant after an accident). At the same time, AI raises many ethical concerns, ranging from algorithmic bias and the digital divide to serious health and safety concerns.

The field of AI ethics has boomed into a global enterprise with a wide variety of players. Yet the ethics of artificial intelligence (AI) is nothing new. The concept of AI is almost 70 years old (McCarthy et al. 2006 ) and ethical concerns about AI have been raised since the middle of the twentieth century (Wiener 1954 ; Dreyfus 1972 ; Weizenbaum 1977 ). The debate has now gained tremendous speed thanks to wider concerns about the use and impact of better algorithms, the growing availability of computing resources and the increasing amounts of data that can be used for analysis (Hall and Pesenti 2017 ).

These technical developments have favoured specific types of AI, in particular machine learning (Alpaydin 2020 ; Faggella 2020 ), of which deep learning is one popular form (see box) (LeCun et al. 2015 ). The success of these AI approaches led to a rapidly expanding set of uses and applications which frequently resulted in consequences that were deemed ethically problematic, such as unfair or illegal discrimination, exclusion and political interference.

Deep Learning Deep learning is one of the approaches to machine learning that have led to the remarkable successes of AI in recent years (Bengio et al. 2021 ). The development of deep learning is a result of the use of artificial neural networks, which are attempts to replicate or simulate brain functions. Natural intelligence arises from parallel networks of neurons that learn by adjusting the strengths of their connections. Deep learning attempts to perform brain-like activities using statistical measures to determine how well a network is performing. Deep learning derives its name from deep neural networks, i.e. networks with many layers. It has been successfully applied to problems ranging from image recognition to natural speech processing. Despite its successes, deep learning has to contend with a range of limitations (Cremer 2021 ). It is open to debate how much further machine learning based on approaches like deep learning can progress and whether fundamentally different principles might be required, such as the introduction of causality models (Schölkopf et al. 2021 ).

With new uses of AI, AI ethics has flourished well beyond academia. For instance, the Rome Call for AI Ethics, Footnote 1 launched in February 2020, links the Vatican with the UN Food and Agriculture Organization (FAO), Microsoft, IBM and the Italian Ministry of Innovation. Another example is that UNESCO appointed 24 experts from around the world in July 2021 and launched a worldwide online consultation on AI ethics and facilitated dialogue with all UNESCO member states. Media interest is also considerable, although some academics consider the treatment of AI ethics by the media as “shallow” (Ouchchy et al. 2020 ).

One of the big problems that AI ethics and ethicists might face is the opaqueness of what is actually happening in AI, given that a good grasp of an activity itself is very helpful in determining its ethical issues.

[I]t is not the role nor to be expected of an AI Ethicist to be able to program the systems themselves. Instead, a strong understanding of aspects such as the difference between supervised and unsupervised learning, what it means to label a dataset, how consent of the user is obtained – essentially, how a system is designed, developed, and deployed – is necessary. In other words, an AI Ethicist must comprehend enough to be able to apprehend the instances in which key ethical questions must be answered (Gambelin 2021 ).

There is thus an expectation that AI ethicists are familiar with the technology, yet “[n]o one really knows how the most advanced algorithms do what they do” (Knight 2017 ), including AI developers themselves.

Despite this opacity of AI in its current forms, it is important to reflect on and discuss which ethical issues can arise due to its development and use. The approach to AI ethics we have chosen here is to use case studies, as “[r]eal experiences in AI ethics present … nuanced examples” (Brusseau 2021 ) for discussion, learning and analysis. This approach will enable us to illustrate the main ethical challenges of AI, often with reference to human rights (Franks 2017 ).

Case studies are a proven method for increasing insights into theoretical concepts by illustrating them through real-world situations (Escartín et al. 2015 ). They also increase student participation and enhance the learning experience (ibid) and are therefore well-suited to teaching (Yin 2003 ).

We have therefore chosen the case study method for this book. We selected the most significant or pertinent ethical issues that are currently discussed in the context of AI (based on and updated from Andreou et al. 2019 and other sources) and dedicated one chapter to each of them.

The structure of each chapter is as follows. First, we introduce short real-life case vignettes to give an overview of a particular ethical issue. Second, we present a narrative assessment of the vignettes and the broader context. Third, we suggest ways in which these ethical issues could be addressed. This often takes the form of an overview of the tools available to reduce the ethical risks of the particular case; for instance, a case study of algorithmic bias leading to discrimination will be accompanied by an explanation of the purpose and scope of AI impact assessments. Where tools are not appropriate, as human decisions need to be made based on ethical reasoning (e.g. in the case of sex robots), we provide a synthesis of different argument strategies. Our focus is on real-life scenarios, most of which have already been published by the media or research outlets. Below we present a short overview of the cases.

Unfair and Illegal Discrimination (Chap. 2 )

The first vignette deals with the automated shortlisting of job candidates by an AI tool trained with CVs (résumés) from the previous ten years. Notwithstanding efforts to address early difficulties with gender bias, the company eventually abandoned the approach as it was not compatible with their commitment to workplace diversity and equality.

The second vignette describes how parole was denied to a prisoner with a model rehabilitation record based on the risk-to-society predictions of an AI system. It became clear that subjective personal views given by prison guards, who may have been influenced by racial prejudices, led to an unreasonably high risk score.

The third vignette tells the story of an engineering student of Asian descent whose passport photo was rejected by New Zealand government systems because his eyes were allegedly closed. This was an ethnicity-based error in passport photo recognition, which was also made by similar systems elsewhere, affecting, for example, dark-skinned women in the UK.

Privacy (Chap. 3 )

The first vignette is about the Chinese social credit scoring system, which uses a large number of data points to calculate a score of citizens’ trustworthiness. High scores lead to the allocation of benefits, whereas low scores can result in the withdrawal of services.

The second vignette covers the Saudi Human Genome Program, with predicted benefits in the form of medical breakthroughs versus genetic privacy concerns.

Surveillance Capitalism (Chap. 4 )

The first vignette deals with photo harvesting from services such as Instagram, LinkedIn and YouTube in contravention of what users of these services were likely to expect or have agreed to. The relevant AI software company, which specialises in facial recognition software, reportedly holds ten billion facial images from around the world.

The second vignette is about a data leak from a provider of health tracking services, which made the health data of 61 million people publicly available.

The third vignette summarises Italian legal proceedings against Facebook for misleading its users by not explaining to them in a timely and adequate manner, during the activation of their account, that data would be collected with commercial intent.

Manipulation (Chap. 5 )

The first vignette covers the Facebook and Cambridge Analytica scandal, which allowed Cambridge Analytica to harvest 50 million Facebook profiles, enabling the delivery of personalised messages to the profile holders and a wider analysis of voter behaviour in the run-up to the 2016 US presidential election and the Brexit referendum in the same year.

The second vignette shows how research is used to push commercial products to potential buyers at specifically determined vulnerable moments, e.g. beauty products being promoted at times when recipients of online commercials are likely to feel least attractive.

Right to Life, Liberty and Security of Person (Chap. 6 )

The first vignette is about the well-known crash of a Tesla self-driving car, killing the person inside.

The second vignette summarises the security vulnerabilities of smart home hubs, which can lead to man-in-the-middle attacks, a type of cyberattack in which the security of a system is compromised, allowing an attacker to eavesdrop on confidential information.

The third vignette deals with adversarial attacks in medical diagnosis, in which an AI-trained system could be fooled to the extent of almost 70% with fake images.

Dignity (Chap. 7 )

The first vignette describes the case of an employee who was wrongly dismissed and escorted off his company’s premises by security guards, with implications for his dignity. The dismissal decision was based on opaque decision-making by an AI tool, communicated by an automatic system.

The second vignette covers sex robots, in particular whether they are an affront to the dignity of women and female children.

Similarly, the third vignette asks whether care robots are an affront to the dignity of elderly people.

AI for Good and the UN’s Sustainable Development Goals (Chap. 8 )

The first vignette shows how seasonal climate forecasting in resource-limited settings has led to the denial of credits for poor farmers in Zimbabwe and Brazil and the accelerated the layoff of workers in the fishing industry in Peru.

The second vignette deals with a research team from a high-income country requesting vast amounts of mobile phone data from users in Sierra Leone, Guinea and Liberia to track population movements during the Ebola crisis. Commentators argued that the time spent negotiating the request with seriously under-resourced governance structures should have been used to handle the escalating Ebola crisis.

This is a book of AI ethics case studies and not a philosophical book on ethics. We nevertheless need to be clear about our use of the term “ethics”. We use the concept of ethics cognisant of the venerable tradition of ethical discussion and of key positions such as those based on an evaluation of the duty of an ethical agent (Kant 1788 , 1797 ), the consequences of an action (Bentham 1789 ; Mill 1861 ), the character of the agent (Aristotle 2000 ) and the keen observation of potential biases in one’s own position, for instance through using an ethics of care (Held 2005 ). We slightly favour a Kantian position in several chapters, but use and acknowledge others. We recognize that there are many other ethical traditions beyond the dominant European ones mentioned here, and we welcome debate about how these may help us understand further aspects of ethics and technology. We thus use the term “ethics” in a pluralistic sense.

This approach is pluralistic because it is open to interpretations from the perspective of the main ethical theories as well as other theoretical positions, including more recent attempts to develop ethical theories that are geared more specifically to novel technologies, such as disclosive ethics (Brey 2000 ), computer ethics (Bynum 2001 ), information ethics (Floridi 1999 ) and human flourishing (Stahl 2021 ).

Our pluralistic reading of the ethics of AI is consistent with much of the relevant literature. A predominant approach to AI ethics is the development of guidelines (Jobin et al. 2019 ), most of which are based on mid-level ethical principles typically developed from the principles of biomedical ethics (Childress and Beauchamp 1979 ). This is also the approach adopted by the European Union’s High-Level Expert Group on AI (AI HLEG 2019 ). The HLEG’s intervention has been influential, as it has had a great impact on the discussion in Europe, which is where we are physically located and which is the origin of the funding for our work (see Acknowledgements). However, there has been significant criticism of the approach to AI ethics based on ethical principles and guidelines (Mittelstadt 2019 ; Rességuier and Rodrigues 2020 ). One key concern is that it remains far from the application and does not explain how AI ethics can be put into practice. With the case-study-based approach presented in this book, we aim to overcome this point of criticism, enhance ethical reflection and demonstrate possible practical interventions.

We invite the reader to critically accompany us on our journey through cases of AI ethics. We also ask the reader to think beyond the cases presented here and ask fundamental questions, such as whether and to what degree the issues discussed here are typical or exclusively relevant to AI and whether one can expect them to be resolved.

Overall, AI is an example of a current and dynamically developing technology. An important question is therefore whether we can keep reflecting and learn anything from the discussion of AI ethics that can be applied to future generations of technologies to ensure that humanity benefits from technological progress and development and has ways to deal with the downsides of technology.

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Stahl, B.C., Schroeder, D., Rodrigues, R. (2023). The Ethics of Artificial Intelligence: An Introduction. In: Ethics of Artificial Intelligence. SpringerBriefs in Research and Innovation Governance. Springer, Cham. https://doi.org/10.1007/978-3-031-17040-9_1

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Essay on Artificial Intelligence

Students are often asked to write an essay on Artificial Intelligence in their schools and colleges. And if you’re also looking for the same, we have created 100-word, 250-word, and 500-word essays on the topic.

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100 Words Essay on Artificial Intelligence

Introduction to artificial intelligence.

Artificial Intelligence, or AI, is a branch of computer science. It involves creating machines that can think and learn like humans. This is done by programming computers to understand language, solve problems, and make decisions.

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There are two types of AI: narrow and general. Narrow AI is designed to do a specific task, like recommending songs on Spotify. General AI, which doesn’t exist yet, would be able to understand and learn anything that a human being can.

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AI is important because it can help us do tasks faster and more accurately. It can analyze large amounts of data, help doctors diagnose diseases, and even drive cars. AI has the potential to greatly improve our lives.

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250 Words Essay on Artificial Intelligence

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Artificial Intelligence (AI) is a rapidly advancing field of technology that seeks to create machines capable of thought and learning. It is a multidisciplinary field that combines computer science, mathematics, and engineering to develop systems that can mimic human intelligence.

AI can be classified into two types: Narrow AI, which is designed to perform a specific task, like voice recognition, and General AI, which can perform any intellectual task that a human being can do. The latter is still a theoretical concept.

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AI is an exciting field with immense potential. It is crucial to navigate its challenges and harness its benefits responsibly. As we continue to explore the capabilities of AI, we are shaping a future where machines can augment human abilities and open new avenues of innovation and productivity.

500 Words Essay on Artificial Intelligence

Artificial Intelligence (AI), a term coined by John McCarthy in 1956, refers to the simulation of human intelligence processes by machines, particularly computer systems. These processes encompass learning, reasoning, problem-solving, perception, and language understanding.

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AI has evolved significantly since its inception. Initially, AI was about rule-based systems that emulate human intelligence. However, the advent of machine learning has shifted the focus towards creating systems that learn from data and improve over time. Today, AI systems can not only mimic human intelligence but also surpass humans in certain tasks, thanks to the advancements in deep learning and neural networks.

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AI’s impact on society and various sectors is profound. In healthcare, AI aids in predicting and diagnosing diseases, personalizing treatment, and improving patient care. In finance, AI helps in fraud detection, risk assessment, and algorithmic trading. In transportation, AI powers autonomous vehicles and optimizes logistics.

However, AI’s impact is not entirely positive. Concerns about job displacement due to automation, privacy breaches, and AI’s potential misuse are growing. These issues necessitate careful consideration and regulation.

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The future of AI holds immense possibilities, with the development of artificial general intelligence (AGI) being the ultimate goal. AGI refers to highly autonomous systems that outperform humans at most economically valuable work. While AGI’s realization may be decades away, its implications are profound, ranging from solving complex problems to potential risks of superintelligent AI overpowering humanity.

AI is a transformative technology that holds the potential to revolutionize various sectors and aspects of human life. As we continue to advance AI, it is crucial to address the ethical and societal implications to ensure its benefits are maximized, and potential harms are minimized. The future of AI, while uncertain, is undoubtedly an exciting frontier of human innovation and discovery.

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The present and future of AI

Finale doshi-velez on how ai is shaping our lives and how we can shape ai.

image of Finale Doshi-Velez, the John L. Loeb Professor of Engineering and Applied Sciences

Finale Doshi-Velez, the John L. Loeb Professor of Engineering and Applied Sciences. (Photo courtesy of Eliza Grinnell/Harvard SEAS)

How has artificial intelligence changed and shaped our world over the last five years? How will AI continue to impact our lives in the coming years? Those were the questions addressed in the most recent report from the One Hundred Year Study on Artificial Intelligence (AI100), an ongoing project hosted at Stanford University, that will study the status of AI technology and its impacts on the world over the next 100 years.

The 2021 report is the second in a series that will be released every five years until 2116. Titled “Gathering Strength, Gathering Storms,” the report explores the various ways AI is  increasingly touching people’s lives in settings that range from  movie recommendations  and  voice assistants  to  autonomous driving  and  automated medical diagnoses .

Barbara Grosz , the Higgins Research Professor of Natural Sciences at the Harvard John A. Paulson School of Engineering and Applied Sciences (SEAS) is a member of the standing committee overseeing the AI100 project and Finale Doshi-Velez , Gordon McKay Professor of Computer Science, is part of the panel of interdisciplinary researchers who wrote this year’s report. 

We spoke with Doshi-Velez about the report, what it says about the role AI is currently playing in our lives, and how it will change in the future.  

Q: Let's start with a snapshot: What is the current state of AI and its potential?

Doshi-Velez: Some of the biggest changes in the last five years have been how well AIs now perform in large data regimes on specific types of tasks.  We've seen [DeepMind’s] AlphaZero become the best Go player entirely through self-play, and everyday uses of AI such as grammar checks and autocomplete, automatic personal photo organization and search, and speech recognition become commonplace for large numbers of people.  

In terms of potential, I'm most excited about AIs that might augment and assist people.  They can be used to drive insights in drug discovery, help with decision making such as identifying a menu of likely treatment options for patients, and provide basic assistance, such as lane keeping while driving or text-to-speech based on images from a phone for the visually impaired.  In many situations, people and AIs have complementary strengths. I think we're getting closer to unlocking the potential of people and AI teams.

There's a much greater recognition that we should not be waiting for AI tools to become mainstream before making sure they are ethical.

Q: Over the course of 100 years, these reports will tell the story of AI and its evolving role in society. Even though there have only been two reports, what's the story so far?

There's actually a lot of change even in five years.  The first report is fairly rosy.  For example, it mentions how algorithmic risk assessments may mitigate the human biases of judges.  The second has a much more mixed view.  I think this comes from the fact that as AI tools have come into the mainstream — both in higher stakes and everyday settings — we are appropriately much less willing to tolerate flaws, especially discriminatory ones. There's also been questions of information and disinformation control as people get their news, social media, and entertainment via searches and rankings personalized to them. So, there's a much greater recognition that we should not be waiting for AI tools to become mainstream before making sure they are ethical.

Q: What is the responsibility of institutes of higher education in preparing students and the next generation of computer scientists for the future of AI and its impact on society?

First, I'll say that the need to understand the basics of AI and data science starts much earlier than higher education!  Children are being exposed to AIs as soon as they click on videos on YouTube or browse photo albums. They need to understand aspects of AI such as how their actions affect future recommendations.

But for computer science students in college, I think a key thing that future engineers need to realize is when to demand input and how to talk across disciplinary boundaries to get at often difficult-to-quantify notions of safety, equity, fairness, etc.  I'm really excited that Harvard has the Embedded EthiCS program to provide some of this education.  Of course, this is an addition to standard good engineering practices like building robust models, validating them, and so forth, which is all a bit harder with AI.

I think a key thing that future engineers need to realize is when to demand input and how to talk across disciplinary boundaries to get at often difficult-to-quantify notions of safety, equity, fairness, etc. 

Q: Your work focuses on machine learning with applications to healthcare, which is also an area of focus of this report. What is the state of AI in healthcare? 

A lot of AI in healthcare has been on the business end, used for optimizing billing, scheduling surgeries, that sort of thing.  When it comes to AI for better patient care, which is what we usually think about, there are few legal, regulatory, and financial incentives to do so, and many disincentives. Still, there's been slow but steady integration of AI-based tools, often in the form of risk scoring and alert systems.

In the near future, two applications that I'm really excited about are triage in low-resource settings — having AIs do initial reads of pathology slides, for example, if there are not enough pathologists, or get an initial check of whether a mole looks suspicious — and ways in which AIs can help identify promising treatment options for discussion with a clinician team and patient.

Q: Any predictions for the next report?

I'll be keen to see where currently nascent AI regulation initiatives have gotten to. Accountability is such a difficult question in AI,  it's tricky to nurture both innovation and basic protections.  Perhaps the most important innovation will be in approaches for AI accountability.

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Ethics of Artificial Intelligence

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Ethics of Artificial Intelligence

A Short Introduction to the Ethics of Artificial Intelligence

  • Published: September 2020
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This introduction outlines in section I.1 some of the key issues in the study of the ethics of artificial intelligence (AI) and proposes ways to take these discussions further. Section I.2 discusses key concepts in AI, machine learning, and deep learning. Section I.3 considers ethical issues that arise because current machine learning is data hungry; is vulnerable to bad data and bad algorithms; is a black box that has problems with interpretability, explainability, and trust; and lacks a moral sense. Section I.4 discusses ethical issues that arise because current machine learning systems may be working too well and human beings can be vulnerable in the presence of these intelligent systems. Section I.5 examines ethical issues arising out of the long-term impact of superintelligence such as how the values of a superintelligent AI can be aligned with human values. Section I.6 presents an overview of the essays in this volume.

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This paper is in the following e-collection/theme issue:

Published on 29.2.2024 in Vol 26 (2024)

Promises, Pitfalls, and Clinical Applications of Artificial Intelligence in Pediatrics

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The Utilization of Artificial Intelligence Essay

Introduction.

What is Artificial intelligence? And has it benefited people through its many fascinations? Artificial intelligence has become particularly widespread in the modern world, but there are significant controversies in people’s lives. Hence, this technology has a negative impact on society and causes its significant decline and the emergence of several drawbacks. However, many people are of the opinion that artificial intelligence has multiple positive qualities to improve people’s lives. Despite this, the outweighing disadvantages of artificial intelligence are a rise in unemployment and an increase in laziness.

No job satisfaction originates when people start losing jobs they love to artificial intelligence, thus leading them to have jobs they don’t enjoy. Thus, innovative technology covers an increasing number of professions that can be performed without the participation of human resources. Research shows that “unemployment would result because workers could not survive working for the market-clearing wage, and it would pay employers to raise real wages above the level because of the increase in worker productivity” (Korinek & Stiglitz, 2018, p. 352). Moreover, it is pointed out that “jobs with high exposure to automation technologies experienced a decline in employment and wages” (Bordot, 2022, p. 119). Therefore, people might find it hard to find jobs that are good which leads to unemployment which further leads to poverty.

Another significant negative aspect of using artificial intelligence is the lack of inspiration and creativity. These characteristics are an integral part of the work of human consciousness, which cannot be introduced into the process of work using this technology. In addition, the introduction of artificial intelligence has a negative effect on reducing the level of work ethic and enthusiasm. People lose interest in performing the actions assigned to them, as they consider it unnecessary to perform activities that can be replaced by a machine. The lack of initiative also results in obesity and other health issues that arise due to the inactivity of employees. Thus, depending on the technology, it could lead to humans using less knowledge and their brain functions to perform actions.

However, despite all the disadvantages, there is a point of view that artificial intelligence provides significant benefits. Henceforth, it is considered that it has an unlimited time limit. Thus, if people tend to be tired and, in some cases, burnout, innovative technology can work constantly. Moreover, artificial intelligence is characterized by greater efficiency and accuracy. Korinek and Stiglitz (2018) point out that some suggest that “artificial intelligence will mainly assist humans in being more productive, and refer to such new technologies as intelligence- assisting innovation” (p. 350). Moreover, research has shown that the use of new technology contributes to increased productivity, thereby influencing an increase in future income (Mutascu, 2021). Thus, the improvement of productivity indicators is due to more precise functioning and the fact that the technology is unlikely to get any errors.

In conclusion, the most significant limitations of artificial intelligence are rising unemployment and increasing laziness. Thus, it negatively affects society and its productivity in the workplace. On the other hand, others believe it is beneficial as it is faster and more efficient. Moreover, it contributes to improving performance since the innovative technology can work for an unlimited amount of time. Therefore, this argumentative essay concluded that artificial intelligence could be as used as an assistant to avoid people losing their jobs which could impact people’s lives.

Bordot, F. (2022). Artificial intelligence, robots and unemployment: Evidence from OECD countries. Journal of Innovation Economics Management, 37 (1), 117-138. Web.

Korinek, A., & Stiglitz, J. E. (2018). Artificial intelligence and its implications for income distribution and unemployment. In The economics of artificial intelligence: An agenda (pp. 349-390). University of Chicago Press.

Mutascu, M. (2021). Artificial intelligence and unemployment: New insights . Economic Analysis and Policy, 69 , 653-667. Web.

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IvyPanda. (2023, November 21). The Utilization of Artificial Intelligence. https://ivypanda.com/essays/the-utilization-of-artificial-intelligence/

"The Utilization of Artificial Intelligence." IvyPanda , 21 Nov. 2023, ivypanda.com/essays/the-utilization-of-artificial-intelligence/.

IvyPanda . (2023) 'The Utilization of Artificial Intelligence'. 21 November.

IvyPanda . 2023. "The Utilization of Artificial Intelligence." November 21, 2023. https://ivypanda.com/essays/the-utilization-of-artificial-intelligence/.

1. IvyPanda . "The Utilization of Artificial Intelligence." November 21, 2023. https://ivypanda.com/essays/the-utilization-of-artificial-intelligence/.

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IvyPanda . "The Utilization of Artificial Intelligence." November 21, 2023. https://ivypanda.com/essays/the-utilization-of-artificial-intelligence/.

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Australian Government

The Hon Ed Husic MP Minister for Industry and Science Media Releases Speeches Transcripts Enter search terms Home Husic The Hon Ed Husic MP Media Releases Free AI education for small and medium businesses Free AI education for small and medium businesses

Joint media release with the Minister for Small Business, the Hon Julie Collins MP

Australian small and medium business owners will today be able to access a new free course in artificial intelligence 101, a development which has been welcomed by the Albanese Labor Government today.

The National AI Centre (NAIC), coordinated by Australia’s national science agency CSIRO, in partnership with the Institute of Applied Technology Digital (IATD) announced it will provide one million scholarships to support Australians to learn fundamental skills and adopt AI technology in their business operations.

The 'Introduction to Artificial Intelligence' course will be delivered through TAFE NSW from today, and will cover topics including challenges and risks, common misconceptions, real world applications, and advice from industry experts to start your career in AI.

More information on the free Introduction to Artificial Intelligence course, including registrations is available here . 

This is the latest milestone in delivering on the government’s ambition for AI as outlined in its interim response to the Safe and Responsible AI in Australia consultation. It is also another step towards delivering on the Government’s target to reach 1.2 million tech-related jobs by 2030.

The Albanese Labor Government is taking several other immediate steps to help businesses to develop and use safe and responsible AI, including the establishment of a Temporary Expert Advisory Group last month, and developing an AI safety standard.

Quotes attributable to the Hon Ed Husic, Minister for Industry and Science:

“A lot of business owners and workers have heard of AI, but they’re not sure about how it applies to them.

“With this course, they can dip their toe in the water and get the basic skills that get them thinking about how AI can get their business working smarter and faster.

“This is practical and pragmatic support for small businesses, who don’t have the same access to tech know-how as big business, but have just as big a need to improve productivity."

Quotes attributable to the Hon Julie Collins, Minister for Small Business

“The Albanese Labor Government understands the benefits to small businesses across the economy that are able to upskill digitally and harness new technologies.

“We are providing a range of support to help small businesses excel online by investing more than $60 million in small business cyber security and digital training programs.”

Full link to course: https://store.training.tafensw.edu.au/product/introduction-to-artificia…

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Illustration shows ChatGPT logo and AI Artificial Intelligence words

How to get away with AI-generated essays

Prof Paul Kleiman on putting ChatGPT to the test on his work. Plus letters from Michael Bulley and Dr Paul Flewers

No wonder Robert Topinka found himself in a quandary ( The software says my student cheated using AI. They say they’re innocent. Who do I believe?, 13 February ). To test ChatGPT’s abilities and weaknesses, I asked it to write a short essay on a particular topic that I specialised in. Before looking at what it produced, I wrote my own 100% original short essay on the same topic. I then submitted both pieces to ChatGPT and asked it to identify whether they were written by AI or a human. It immediately identified the first piece as AI-generated. But then it also said that my essay “was probably generated by AI”.

I concluded that if you write well, in logical, appropriate and grammatically correct English, then the chances are that it will be deemed to be AI-generated. To avoid detection, write badly. Prof Paul Kleiman Truro, Cornwall

Robert Topinka gets into a twist about whether his student’s essay was genuine or produced by AI. The obvious solution is for such work not to contribute to the final degree qualification. Then there would be no point in cheating.

Let there be real chat between teachers and students rather than ChatGPT , and let the degree be decided only by exams, with surprise questions, done in an exam room with pen and paper, and not a computer in sight. Michael Bulley Chalon-sur-Saône, France

Dr Robert Topinka overlooks a crucial factor with respect to student cheating – so long as a degree is a requirement to obtain a reasonable job, then chicanery is inevitable. When I left school at 16 in the early 1970s, an administrative job could be had with a few O-levels; when I finished my PhD two decades ago and was looking for that sort of job, each one required A-levels, and often a degree. I was a mature student, studying for my own edification, and so cheating was self-defeating. Cheating will stop being a major problem only when students attend university primarily to learn for the sake of learning and not as a means of gaining employment. Dr Paul Flewers London

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    Artificial Intelligence (AI) is a subfield of computer science that focuses on designing and developing intelligent agents. These are systems with the ability to reason, plan and learn. AI can be divided into two broad categories: narrow AI and general AI. Narrow AI, or weak AI, is designed to perform specific tasks such as playing chess or ...

  9. Introduction to artificial intelligence

    Artificial intelligence (AI) is the ability of machines to replicate or enhance human intellect, such as reasoning and learning from experience. Artificial intelligence has been used in computer programs for years, but it is now applied to many other products and services. For example, some digital cameras can determine what objects are present ...

  10. Introduction

    This guide provides an introduction to artificial intelligence (AI), with a focus on generative AI. You'll find explanations of the benefits and limitations as well as support to use, cite, research, and teach with artificial intelligence. AI tools and the legal and ethical landscape surrounding their use are changing rapidly.

  11. Artificial Intelligence Essay

    In this topic, we are going to provide an essay on Artificial Intelligence. This long essay on Artificial Intelligence will cover more than 1000 words, including Introduction of AI, History of AI, Advantages and disadvantages, Types of AI, Applications of AI, Challenges with AI, and Conclusion. This long essay will be helpful for students and ...

  12. Artificial Intelligence: A Very Short Introduction

    As a concept, Artificial Intelligence has fuelled and sharpened the philosophical debates concerning the nature of the mind, intelligence, and the uniqueness of human beings. Artificial Intelligence: A Very Short Introduction considers the history of Artificial Intelligence, its successes, its limitations, and its future goals. It also reviews ...

  13. ≡Essays on Artificial Intelligence: Top 10 Examples by

    Artificial Intelligence Essay Introduction Examples 🚀. Here are three captivating introduction paragraphs to begin your essay: 1. "In a world driven by data and algorithms, artificial intelligence has emerged as both a beacon of innovation and a source of profound ethical contemplation.

  14. Artificial Intelligence Essay: 500+ Words Essay for Students

    Artificial Intelligence Essay: Artificial Intelligence is becoming the synonym for future. In a world dominated by machines, machine minds and machine hearts, everything is based on technology. To gain control over technology is the task of Artificial Intelligence. Created with extraordinary human intelligence, Artificial Intelligence or AI is ...

  15. The brief history of artificial intelligence: The world has changed

    The rise of artificial intelligence over the last 8 decades: As training computation has increased, AI systems have become more powerful 9 Each small circle in this chart represents one AI system. The circle's position on the horizontal axis indicates when the AI system was built, and its position on the vertical axis shows the amount of ...

  16. Artificial intelligence

    Artificial intelligence (AI) is the intelligence of machines or software, as opposed to the intelligence of living beings, primarily of humans.It is a field of study in computer science that develops and studies intelligent machines. Such machines may be called AIs. AI technology is widely used throughout industry, government, and science.Some high-profile applications are: advanced web search ...

  17. The Ethics of Artificial Intelligence: An Introduction

    This chapter introduces the themes covered by the book. It provides an overview of the concept of artificial intelligence (AI) and some of the technologies that have contributed to the current high level of visibility of AI. It explains why using case studies is a suitable approach to engage a broader audience with an interest in AI ethics.

  18. Essay on Artificial Intelligence

    250 Words Essay on Artificial Intelligence Introduction. Artificial Intelligence (AI) is a rapidly advancing field of technology that seeks to create machines capable of thought and learning. It is a multidisciplinary field that combines computer science, mathematics, and engineering to develop systems that can mimic human intelligence. ...

  19. Artificial Intelligence Essay

    Artificial Intelligence Essay: In contrast to the natural intelligence of humans and animals when the machines are equipped to do intelligent tasks, it is called Artificial Intelligence. Some tasks that human intelligence is capable like learning, analyzing, problem-solving, etc. when done by machines is a noteworthy example of AI. All in all, the simulation of some […]

  20. Exploring Artificial Intelligence in Academic Essay: Higher Education

    Higher education perceptions of artificial intelligence. Studies have explored the diverse functionalities of these AI tools and their impact on writing productivity, quality, and students' learning experiences. The integration of Artificial Intelligence (AI) in writing academic essays has become a significant area of interest in higher education.

  21. Artificial Intelligence and Its Impact on Education Essay

    Introduction. Rooted in computer science, Artificial Intelligence (AI) is defined by the development of digital systems that can perform tasks, which are dependent on human intelligence (Rexford, 2018). Interest in the adoption of AI in the education sector started in the 1980s when researchers were exploring the possibilities of adopting ...

  22. The present and future of AI

    The 2021 report is the second in a series that will be released every five years until 2116. Titled "Gathering Strength, Gathering Storms," the report explores the various ways AI is increasingly touching people's lives in settings that range from movie recommendations and voice assistants to autonomous driving and automated medical ...

  23. A Short Introduction to the Ethics of Artificial Intelligence

    Current AI is what is known as narrow AI 20 because it is designed to perform a narrowly defined task such as driving a car or identifying a hostile target. In the long term, a number of AI researchers hope to create artificial general intelligence (AGI), which would be capable of performing any intellectual task that a human being can. 21 On one understanding, such AI, sometimes referred to ...

  24. Promises, Pitfalls, and Clinical Applications of Artificial

    Artificial intelligence (AI) broadly describes a branch of computer science focused on developing machines capable of performing tasks typically associated with human intelligence. Those who connect AI with the world of science fiction may meet its growing rise with hesitancy or outright skepticism. However, AI is becoming increasingly pervasive in our society, from algorithms helping to sift ...

  25. The Utilization of Artificial Intelligence Essay

    In addition, the introduction of artificial intelligence has a negative effect on reducing the level of work ethic and enthusiasm. People lose interest in performing the actions assigned to them, as they consider it unnecessary to perform activities that can be replaced by a machine. ... Therefore, this argumentative essay concluded that ...

  26. Artificial Intelligence in Modern Medical Technology: an Ethical

    With the introduction of artificial intelligence capable of learning, engineers and medical professionals are teaching these systems the differences between symptoms of various diseases in humans. ... Students looking for free, top-notch essay and term paper samples on various topics. Additional materials, such as the best quotations, synonyms ...

  27. Artificial Intelligence (AI) Systems And Their Use in Businesses

    Essay Sample: Introduction Research in the corporate and business world has shown that enterprise has gotten into a new era, with the data application taking a center ... Artificial Intelligence Techniques Case-Based Reasoning. Case-based reasoning, abbreviated as CBR, is an AI technique entailing the process of coming up with solutions to ...

  28. Free AI education for small and medium businesses

    More information on the free Introduction to Artificial Intelligence course, including registrations is available here. This is the latest milestone in delivering on the government's ambition for AI as outlined in its interim response to the Safe and Responsible AI in Australia consultation. It is also another step towards delivering on the ...

  29. How to get away with AI-generated essays

    Artificial intelligence (AI) Letters. ... To test ChatGPT's abilities and weaknesses, I asked it to write a short essay on a particular topic that I specialised in. Before looking at what it ...

  30. What is Artificial General Intelligence?

    Thus, human intelligence remains a mysterious concept, surrounded by lots of debate. So, what about artificial intelligence? Today's AI is highly specialised. A chess program is extremely good at playing chess but cannot write an essay on chemistry. Similarly, ChatGPT is great at producing written text but is very poor at maths. For these ...