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Top 10 Machine Language PowerPoint Presentation Templates in 2024

Machine learning is a transformative technology that enables systems to learn from data, identify patterns, and make decisions with minimal human intervention. In the context of PowerPoint presentations, machine learning can be effectively illustrated through various use cases, showcasing its applications across multiple industries. For instance, businesses can leverage machine learning algorithms to analyze customer data, predict purchasing behavior, and tailor marketing strategies accordingly. Educational institutions can utilize machine learning to personalize learning experiences, adapting course content to meet individual student needs and improving engagement.In healthcare, machine learning is revolutionizing diagnostics by analyzing medical images and patient data to assist in early disease detection and treatment recommendations. Additionally, finance professionals use machine learning for risk assessment and fraud detection, enhancing security and efficiency in transactions. When creating PowerPoint presentations on machine learning, users can incorporate visualizations of algorithms, case studies, and real-world applications to effectively communicate complex concepts to their audience. The flexibility of PowerPoint allows for the integration of graphs, charts, and infographics, making it easier to present data-driven insights and foster a deeper understanding of machine learning's impact across various sectors. This approach not only enhances the visual appeal of the presentation but also ensures that the content is engaging and informative.

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Strengths and limitations of machine learning language

Presenting this set of slides with name Strengths And Limitations Of Machine Learning Language. This is a two stage process. The stages in this process are Strengths, Limitations. This is a completely editable PowerPoint presentation and is available for immediate download. Download now and impress your audience.

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  • Limitations

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Glimpse About ChatGPT As AI Natural Language Processing About Components Use ChatGPT SS V

This slide provides information regarding natural language processing methodology that assists in building machines that understand and impersonate human language in written, spoken or organized format. It presents elements of NLP such as components, use case, pros and cons. Increase audience engagement and knowledge by dispensing information using Glimpse About ChatGPT As AI Natural Language Processing About Components Use ChatGPT SS V. This template helps you present information on two stages. You can also present information on Human Language, Artificial Intelligence, Natural Language Processing using this PPT design. This layout is completely editable so personaize it now to meet your audiences expectations.

This slide provides information regarding natural language processing methodology that assists in building machines that understand and impersonate human language in written, spoken or organized format. It presents elements of NLP such as components, use case, pros and cons.

  • Human Language
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Meta data programming in machine language icon

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Horizontal pillar indicating machine learning language benefits

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AI Tools Machine Learning Natural Language Processing Illustration

This colourful PowerPoint Illustration depicts AI tools, making it ideal for presentations on the topic. It is bright and eye-catching, and can be used to illustrate concepts related to artificial intelligence. It is perfect for any presentation or document related to AI technology.

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What is Machine Language?

Machine language is a low-level programming language that is understood by computers. Machine language is made up of binary bits 0 and 1. Machine language is also known as machine codes or object code. As machine language consists of only 0 and 1, that’s why it is difficult to understand in raw form. Machine language cannot understood by humans. The CPU processes this machine code as input. In this article, we are going to learn about what is Machine language, the features of machine language, the advantages and disadvantages of machine learning, and why it is difficult for humans to understand machine language(low-level language).

Machine language is a low-level programming language that consists of binary bits i.e. only 0 and 1. The data present in binary form is the reason for its fast execution. In Machine language, instructions are directly executed by the CPU. Machine language is also known as object code or machine code. Machine language is binary language.

Machine Language

Machine Language

Needs of Machine Language

As a human, we write code in high level language. The programming language which we use to write codes such as C, C++ and java are high level languages. High level language is not understood by computer directly so it is converted into low level machine language to understand the meaning of code and perform execution. Computers compile the code written by us and translate into machine code and then execute it. Computers are only able to understand machine language.

Features of Machine Language

Below are some feature of Machine Language.

  • Machine language is a low level language.
  • Machine language consist of only 0 and 1 bits.
  • Machine languages are platform dependent.
  • It is nearly impossible to learn machine language for humans because it requires a lot of memoization.
  • Machine language is used to create and construct drivers as well.

Understand the Complexity of Machine language

In machine language every character, integer and special symbols are written in form of 0 and 1 . To understand machine language let’s take an example of a machine language instruction. This is a simple addition operation: 01100110 00001010. This binary sequence represents an instruction that tells the computer to add two numbers together.

Meaning of Binary bits in Machine Language:

A sequence of bits is used to give commands in machine languages.

  • The 1s (one) represents the true or on states.
  • On the other hand, the 0s (zero) represent the off or false states.
  • That’s why no human can remember the binary codes of machine languages. As a result, learning these languages is not possible for humans.

Machine Language Instruction Components

Machine language consist of two instruction components :

1. Operand(s)

The operand(s) represents the data that the operation must be performed on. This data can take various forms, depending on the processor’s architecture. This can be a register containing a value, a memory address pointing to a location in memory where the data is stored, or a constant value embedded within the instruction itself.

The opcode (Operation code) represents the operation that the processor must perform. This indicate that the instruction is an arithmetic operation such as addition, subtraction, multiplication, or division.

Advantages of Machine Language

Some advantages of machine language are listed below:

  • Machine languages are faster in execution because they are in binary form.
  • Machine language does not need to be translated , because it is already present in simple binary form.
  • The CPU directly executes the machine language.
  • The evolution of the computer system and operating system over the time period is due to machine language.
  • Machine languages are used in developing a high-grade computer system.

Disadvantages of Machine Language

Some disadvantages of machine language are listed below:

  • Machine language are complex to understand and memorize.
  • Writing codes in machine language is time-consuming.
  • It is very difficult to resolve bugs and errors present in the codes and programs.
  • Codes written in machine languages are more prone to error.
  • Machine languages are not easy to modify.
  • Machine language are platform Independent.

FAQs on Machine Language

Q.1: why machine language is low level language .

Machine language is made up of binary bits and is understood by computer. The CPU process this machine language as input for execution. That’s why it is low level language.

Q.2: What is difference between Assembly language and Machine language ?

Machine language is only understood by computers not by humans while Assembly language is understood by humans not by computers.

Q.3: What is the meaning of 0 and 1 in binary bits ?

0 represent false value or false state and 1 represent true value or true state.

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Chapter 2.2 Machine Language.

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Chapter 2.2 Machine Language

Chapter 2: Data Manipulation

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Chapter 2 Data Manipulation Dr. Farzana Rahman Assistant Professor Department of Computer Science James Madison University 1 Some sldes are adapted from.

presentation on machine language

 Suppose for a moment that you were asked to perform a task and were given the following list of instructions to perform:

presentation on machine language

2.3) Example of program execution 1. instruction  B25 8 Op-code B means to change the value of the program counter if the contents of the indicated register.

presentation on machine language

Computer Architecture and Data Manipulation Chapter 3.

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Copyright © 2008 Pearson Education, Inc. Publishing as Pearson Addison-Wesley Computer Science: An Overview Tenth Edition by J. Glenn Brookshear Chapter.

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Computer Systems. Computer System Components Computer Networks.

presentation on machine language

Computer Organization Boolean Logic and the CPU i206 Fall 2010 John Chuang Some slides adapted from Marti Hearst, Brian Hayes, or Glenn Brookshear.

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Processor Technology and Architecture

presentation on machine language

Midterm Wednesday Chapter 1-3: Number /character representation and conversion Number arithmetic Combinational logic elements and design (DeMorgan’s Law)

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Data Manipulation Computer System consists of the following parts:

presentation on machine language

Memory - Registers Instruction Sets

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Computer Processing CSCE 110 J. Michael Moore.

presentation on machine language

Chapter 4 Processor Technology and Architecture. Chapter goals Describe CPU instruction and execution cycles Explain how primitive CPU instructions are.

presentation on machine language

1 Sec (2.3) Program Execution. 2 In the CPU we have CU and ALU, in CU there are two special purpose registers: 1. Instruction Register 2. Program Counter.

presentation on machine language

Lecture 3. Diff b/w RAM and Registers Registers are used to hold data immediately applicable to the operation at hand Registers are used to hold data.

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Instruction Set Architecture

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Data Manipulation, Communication and Architecture Fall 2012.

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Data manipulation, Part one Introduction to computer, 2nd semester, 2010/2011 Mr.Nael Aburas Faculty of Information.

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Von Neumann Machine Objectives: Explain Von Neumann architecture:  Memory –Organization –Decoding memory addresses, MAR & MDR  ALU and Control Unit –Executing.

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Humanities and Social Sciences Review, 2019

The evolution of global intelligence via computers is creating a myth-digital environment with epidemic consequences, not only for the human brain, but also for computer users all over the world. Therefore, there is a well-programmed strategy utilizing physics and biology to reshape the autonomous agent of each computer user according to the dominant configurations of this posthuman environment, and AI developers predicted this reshaping decades ago. This posthuman metamorphosis is a consequence of the dominant configurations espoused by evolutionary science because evolutionary theory brings together two imaginative elements implicit in much nineteenth-century thinking and creativity. One was the fascination with growth. The other was the concept of transformation. Our society's fascination with growth and transformation might indeed have mythical implications. Therefore, the mythical character of machine language and code is an active force that has power to transform our minds and our behaviours according to the dominant configurations of a posthuman environment, and this posthuman metamorphosis was insightfully pointed out years ago by the character Morphus in the film Matrix when Morphus answers Neo's question: "What is the Matrix?" "The Matrix is a computer generated dream world built [on digital codes]...to change a human being..." In the Wachowski Brothers' 1999 film The Matrix, the Matrix is a simulated dream world. This world of "mental projections of our digital self" derives from digital forms or machine language. However, this dream world is also a simulation of a real world, and these mental projections of our digital self derive from binary codes. From digital codes, a computer creates pictograms-seen on the screen in green-, which are compiled to make a virtual reality; reality humans perceived to be real. Likewise, upon reflecting how binary codes can create an alternate reality for humans, I came to the realization that computers, operating on symbolic codes too, could also transform our perceptions. Hence, through symbols, or symbolic dramatizations, the Matrix projects a virtual reality developed from digital (binary) codes. Likewise, the symbolic nature of computer codes and computer language led me to consider how their metaphorical nature influences our brain patterns and behaviours too. The Matrix, as metaphor-or symbolic dramatization-enlightened me also about the possibility of a grand narrative operating on our minds visa-vie machine language and computer codes. The influence of machine language and codes on our minds and behaviours is certainly dramatized in the film when Morphus, within the Construct (the simulation), shows Neo the reality behind the "dream" images created by the Matrix. It is at this point in the film that Morphus answers Neo's question: "What is the Matrix?" "The Matrix is a computer generated dream world built [on these digital green codes]...to change a human being..." Because computer images/icons have metaphorical properties-as portrayed in The Matrix too-I felt compelled to consider the ideology underlying these symbolic codes, and how 467

The Mythical Character of Machine Language and Code Cover Page

Computers are tools for manipulating and analyzing information. Computer programs are the means for specifying what actions a computer performs. This chapter will look at a simple computer program. Some specific elements covered are: ✓ High-level computer languages ✓ Compiled vs. interpreted languages ✓ A simple C++ program ✓ Specifying variables ✓ Assignment and mathematical operations Programming Languages Computers are among the most complex artifacts made by humans and are comprised of billions of distinct elements. Direct control of such complexity is beyond our abilities. Instead, computer programming relies upon the principal of abstraction to allow us to specify what actions a computer should perform. Abstraction Abstraction is the pruning away of complexity resulting in a simplified mental model of a process. For example, the driver of a car is not required to understand the mechanics of a car engine in order to drive. As long as there is a mental model that the gas pedal makes the car go faster, the brake slows the car, and the steering wheel makes the car turn, a driver can be reasonably effective. Note that this mental model is not really correct – pressing the gas provides torque to the wheels, which may or may not accelerate the car. This example highlights one danger of abstraction; in unusual situations, the simplified model may provide incorrect predictions. For this reason, computer programming practitioners should have some understanding of how computers operate as well as detailed knowledge of the programming language.

Chapter 1: Computer Programs Cover Page

As we saw in Chapter 1, every finite computational task can be realized by a combinational circuit. While this is an important concept, it is not very practical; we cannot afford to design a special circuit for each computational task. Instead we generally perform computational tasks with machines having memory. In a strong sense to be explored in this chapter, the memory of such machines allows them to reuse their equivalent circuits to realize functions of high circuit complexity. In this chapter we examine the deterministic and nondeterministic finite-state machine (FSM), the random-access machine (RAM), and the Turing machine. The finite-state machine moves from state to state while reading input and producing output. The RAM has a central processing unit (CPU) and a random-access memory with the property that each memory word can be accessed in one unit of time. Its CPU executes instructions, reading and writing data from and to the memory. The Turing machine has a control unit...

Machines with Memory Cover Page

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Introduction to Large Language Models

New to language models or large language models? Check out the resources below.

What is a language model?

A language model is a machine learning model that aims to predict and generate plausible language. Autocomplete is a language model, for example.

These models work by estimating the probability of a token or sequence of tokens occurring within a longer sequence of tokens. Consider the following sentence:

If you assume that a token is a word, then a language model determines the probabilities of different words or sequences of words to replace that underscore. For example, a language model might determine the following probabilities:

A "sequence of tokens" could be an entire sentence or a series of sentences. That is, a language model could calculate the likelihood of different entire sentences or blocks of text.

Estimating the probability of what comes next in a sequence is useful for all kinds of things: generating text, translating languages, and answering questions, to name a few.

What is a large language model?

Modeling human language at scale is a highly complex and resource-intensive endeavor. The path to reaching the current capabilities of language models and large language models has spanned several decades.

As models are built bigger and bigger, their complexity and efficacy increases. Early language models could predict the probability of a single word; modern large language models can predict the probability of sentences, paragraphs, or even entire documents.

The size and capability of language models has exploded over the last few years as computer memory, dataset size, and processing power increases, and more effective techniques for modeling longer text sequences are developed.

How large is large?

The definition is fuzzy, but "large" has been used to describe BERT (110M parameters) as well as PaLM 2 (up to 340B parameters).

Parameters are the weights the model learned during training, used to predict the next token in the sequence. "Large" can refer either to the number of parameters in the model, or sometimes the number of words in the dataset.

Transformers

A key development in language modeling was the introduction in 2017 of Transformers, an architecture designed around the idea of attention . This made it possible to process longer sequences by focusing on the most important part of the input, solving memory issues encountered in earlier models.

Transformers are the state-of-the-art architecture for a wide variety of language model applications, such as translators.

If the input is "I am a good dog." , a Transformer-based translator transforms that input into the output "Je suis un bon chien." , which is the same sentence translated into French.

Full Transformers consist of an encoder and a decoder . An encoder converts input text into an intermediate representation, and a decoder converts that intermediate representation into useful text.

Self-attention

Transformers rely heavily on a concept called self-attention. The self part of self-attention refers to the "egocentric" focus of each token in a corpus. Effectively, on behalf of each token of input, self-attention asks, "How much does every other token of input matter to me ?" To simplify matters, let's assume that each token is a word and the complete context is a single sentence. Consider the following sentence:

The animal didn't cross the street because it was too tired.

There are 11 words in the preceding sentence, so each of the 11 words is paying attention to the other ten, wondering how much each of those ten words matters to them. For example, notice that the sentence contains the pronoun it . Pronouns are often ambiguous. The pronoun it always refers to a recent noun, but in the example sentence, which recent noun does it refer to: the animal or the street?

The self-attention mechanism determines the relevance of each nearby word to the pronoun it .

What are some use cases for LLMs?

LLMs are highly effective at the task they were built for, which is generating the most plausible text in response to an input. They are even beginning to show strong performance on other tasks; for example, summarization, question answering, and text classification. These are called emergent abilities . LLMs can even solve some math problems and write code (though it's advisable to check their work).

LLMs are excellent at mimicking human speech patterns. Among other things, they're great at combining information with different styles and tones.

However, LLMs can be components of models that do more than just generate text. Recent LLMs have been used to build sentiment detectors, toxicity classifiers, and generate image captions.

LLM Considerations

Models this large are not without their drawbacks.

The largest LLMs are expensive. They can take months to train, and as a result consume lots of resources.

They can also usually be repurposed for other tasks, a valuable silver lining.

Training models with upwards of a trillion parameters creates engineering challenges. Special infrastructure and programming techniques are required to coordinate the flow to the chips and back again.

There are ways to mitigate the costs of these large models. Two approaches are offline inference and distillation .

Bias can be a problem in very large models and should be considered in training and deployment.

As these models are trained on human language, this can introduce numerous potential ethical issues, including the misuse of language, and bias in race, gender, religion, and more.

It should be clear that as these models continue to get bigger and perform better, there is continuing need to be diligent about understanding and mitigating their drawbacks. Learn more about Google's approach to responsible AI .

Learn more about LLMs

Interested in a more in-depth introduction to large language models? Check out the new Large language models module in Machine Learning Crash Course .

Except as otherwise noted, the content of this page is licensed under the Creative Commons Attribution 4.0 License , and code samples are licensed under the Apache 2.0 License . For details, see the Google Developers Site Policies . Java is a registered trademark of Oracle and/or its affiliates.

Last updated 2024-09-06 UTC.

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Artificial Intelligence

Machine learning, explained

Apr 21, 2021

Machine learning is behind chatbots and predictive text, language translation apps, the shows Netflix suggests to you, and how your social media feeds are presented. It powers autonomous vehicles and machines that can diagnose medical conditions based on images. 

When companies today deploy artificial intelligence programs, they are most likely using machine learning — so much so that the terms are often used interchangeably, and sometimes ambiguously. Machine learning is a subfield of artificial intelligence that gives computers the ability to learn without explicitly being programmed.

“In just the last five or 10 years, machine learning has become a critical way, arguably the most important way, most parts of AI are done,” said MIT Sloan professor Thomas W. Malone, the founding director of the MIT Center for Collective Intelligence . “So that's why some people use the terms AI and machine learning almost as synonymous … most of the current advances in AI have involved machine learning.”

With the growing ubiquity of machine learning, everyone in business is likely to encounter it and will need some working knowledge about this field. A 2020 Deloitte survey found that 67% of companies are using machine learning, and 97% are using or planning to use it in the next year.

From manufacturing to retail and banking to bakeries, even legacy companies are using machine learning to unlock new value or boost efficiency. “Machine learning is changing, or will change, every industry, and leaders need to understand the basic principles, the potential, and the limitations,” said MIT computer science professor Aleksander Madry , director of the MIT Center for Deployable Machine Learning .

While not everyone needs to know the technical details, they should understand what the technology does and what it can and cannot do, Madry added. “I don’t think anyone can afford not to be aware of what’s happening.”

That includes being aware of the social, societal, and ethical implications of machine learning. “It's important to engage and begin to understand these tools, and then think about how you're going to use them well. We have to use these [tools] for the good of everybody,” said Dr. Joan LaRovere , MBA ’16, a pediatric cardiac intensive care physician and co-founder of the nonprofit The Virtue Foundation. “AI has so much potential to do good, and we need to really keep that in our lenses as we're thinking about this. How do we use this to do good and better the world?”

What is machine learning?

Machine learning is a subfield of artificial intelligence, which is broadly defined as the capability of a machine to imitate intelligent human behavior. Artificial intelligence systems are used to perform complex tasks in a way that is similar to how humans solve problems.

The goal of AI is to create computer models that exhibit “intelligent behaviors” like humans, according to Boris Katz , a principal research scientist and head of the InfoLab Group at CSAIL. This means machines that can recognize a visual scene, understand a text written in natural language, or perform an action in the physical world.

Machine learning is one way to use AI. It was defined in the 1950s by AI pioneer Arthur Samuel as “the field of study that gives computers the ability to learn without explicitly being programmed.”

The definition holds true, according to Mikey Shulman, a lecturer at MIT Sloan and head of machine learning at  Kensho , which specializes in artificial intelligence for the finance and U.S. intelligence communities. He compared the traditional way of programming computers, or “software 1.0,” to baking, where a recipe calls for precise amounts of ingredients and tells the baker to mix for an exact amount of time. Traditional programming similarly requires creating detailed instructions for the computer to follow.

But in some cases, writing a program for the machine to follow is time-consuming or impossible, such as training a computer to recognize pictures of different people. While humans can do this task easily, it’s difficult to tell a computer how to do it. Machine learning takes the approach of letting computers learn to program themselves through experience. 

Machine learning starts with data — numbers, photos, or text, like bank transactions, pictures of people or even bakery items , repair records, time series data from sensors, or sales reports. The data is gathered and prepared to be used as training data, or the information the machine learning model will be trained on. The more data, the better the program.

From there, programmers choose a machine learning model to use, supply the data, and let the computer model train itself to find patterns or make predictions. Over time the human programmer can also tweak the model, including changing its parameters, to help push it toward more accurate results. (Research scientist Janelle Shane’s website AI Weirdness is an entertaining look at how machine learning algorithms learn and how they can get things wrong — as happened when an algorithm tried to generate recipes and created Chocolate Chicken Chicken Cake.)

Some data is held out from the training data to be used as evaluation data, which tests how accurate the machine learning model is when it is shown new data. The result is a model that can be used in the future with different sets of data.

Successful machine learning algorithms can do different things, Malone wrote in a recent research brief about AI and the future of work that was co-authored by MIT professor and CSAIL director Daniela Rus and Robert Laubacher, the associate director of the MIT Center for Collective Intelligence.

“The function of a machine learning system can be descriptive , meaning that the system uses the data to explain what happened; predictive , meaning the system uses the data to predict what will happen; or prescriptive , meaning the system will use the data to make suggestions about what action to take,” the researchers wrote.  

There are three subcategories of machine learning:

Supervised machine learning models are trained with labeled data sets, which allow the models to learn and grow more accurate over time. For example, an algorithm would be trained with pictures of dogs and other things, all labeled by humans, and the machine would learn ways to identify pictures of dogs on its own. Supervised machine learning is the most common type used today.

In unsupervised machine learning, a program looks for patterns in unlabeled data. Unsupervised machine learning can find patterns or trends that people aren’t explicitly looking for. For example, an unsupervised machine learning program could look through online sales data and identify different types of clients making purchases.

Reinforcement machine learning trains machines through trial and error to take the best action by establishing a reward system. Reinforcement learning can train models to play games or train autonomous vehicles to drive by telling the machine when it made the right decisions, which helps it learn over time what actions it should take.

Infographic entitled "What do you want your machine learning system to do?"

Source: Thomas Malone | MIT Sloan. See: https://bit.ly/3gvRho2, Figure 2.

In the Work of the Future brief, Malone noted that machine learning is best suited for situations with lots of data — thousands or millions of examples, like recordings from previous conversations with customers, sensor logs from machines, or ATM transactions. For example, Google Translate was possible because it “trained” on the vast amount of information on the web, in different languages.

In some cases, machine learning can gain insight or automate decision-making in cases where humans would not be able to, Madry said. “It may not only be more efficient and less costly to have an algorithm do this, but sometimes humans just literally are not able to do it,” he said.

Google search is an example of something that humans can do, but never at the scale and speed at which the Google models are able to show potential answers every time a person types in a query, Malone said. “That’s not an example of computers putting people out of work. It's an example of computers doing things that would not have been remotely economically feasible if they had to be done by humans.”

Machine learning is also associated with several other artificial intelligence subfields:

Natural language processing

Natural language processing is a field of machine learning in which machines learn to understand natural language as spoken and written by humans, instead of the data and numbers normally used to program computers. This allows machines to recognize language, understand it, and respond to it, as well as create new text and translate between languages. Natural language processing enables familiar technology like chatbots and digital assistants like Siri or Alexa.

Neural networks

Neural networks are a commonly used, specific class of machine learning algorithms. Artificial neural networks are modeled on the human brain, in which thousands or millions of processing nodes are interconnected and organized into layers.

In an artificial neural network, cells, or nodes, are connected, with each cell processing inputs and producing an output that is sent to other neurons. Labeled data moves through the nodes, or cells, with each cell performing a different function. In a neural network trained to identify whether a picture contains a cat or not, the different nodes would assess the information and arrive at an output that indicates whether a picture features a cat.

Deep learning

Deep learning networks are neural networks with many layers. The layered network can process extensive amounts of data and determine the “weight” of each link in the network — for example, in an image recognition system, some layers of the neural network might detect individual features of a face, like eyes, nose, or mouth, while another layer would be able to tell whether those features appear in a way that indicates a face.  

Like neural networks, deep learning is modeled on the way the human brain works and powers many machine learning uses, like autonomous vehicles, chatbots, and medical diagnostics.

“The more layers you have, the more potential you have for doing complex things well,” Malone said.

Deep learning requires a great deal of computing power, which raises concerns about its economic and environmental sustainability.

How businesses are using machine learning

Machine learning is the core of some companies’ business models, like in the case of Netflix’s suggestions algorithm or Google’s search engine . Other companies are engaging deeply with machine learning, though it’s not their main business proposition.

Others are still trying to determine how to use machine learning in a beneficial way. “In my opinion, one of the hardest problems in machine learning is figuring out what problems I can solve with machine learning,” Shulman said. “There’s still a gap in the understanding.” 

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In a 2018 paper , researchers from the MIT Initiative on the Digital Economy outlined a 21-question rubric to determine whether a task is suitable for machine learning. The researchers found that no occupation will be untouched by machine learning, but no occupation is likely to be completely taken over by it. The way to unleash machine learning success, the researchers found, was to reorganize jobs into discrete tasks, some which can be done by machine learning, and others that require a human.

Companies are already using machine learning in several ways, including:

Recommendation algorithms. The recommendation engines behind Netflix and YouTube suggestions, what information appears on your Facebook feed, and product recommendations are fueled by machine learning. “[The algorithms] are trying to learn our preferences,” Madry said. “They want to learn, like on Twitter, what tweets we want them to show us, on Facebook, what ads to display, what posts or liked content to share with us.”

Image analysis and object detection. Machine learning can analyze images for different information, like learning to identify people and tell them apart — though facial recognition algorithms are controversial. Business uses for this vary. Shulman noted that hedge funds famously use machine learning to analyze the number of cars  in parking lots, which helps them learn how companies are performing and make good bets.

Fraud detection . Machines can analyze patterns, like how someone normally spends or where they normally shop, to identify potentially fraudulent credit card transactions , log-in attempts, or spam emails.

Automatic helplines or chatbots. Many companies are deploying online chatbots, in which customers or clients don’t speak to humans, but instead interact with a machine. These algorithms use machine learning and natural language processing, with the bots learning from records of past conversations to come up with appropriate responses.

Self-driving cars. Much of the technology behind self-driving cars is based on machine learning, deep learning in particular .

Medical imaging and diagnostics. Machine learning programs can be trained to examine medical images or other information and look for certain markers of illness, like a tool that can predict cancer risk based on a mammogram.

Read report: Artificial Intelligence and the Future of Work

How machine learning works: promises and challenges

While machine learning is fueling technology that can help workers or open new possibilities for businesses, there are several things business leaders should know about machine learning and its limits.

Explainability

One area of concern is what some experts call explainability, or the ability to be clear about what the machine learning models are doing and how they make decisions. “Understanding why a model does what it does is actually a very difficult question, and you always have to ask yourself that,” Madry said. “You should never treat this as a black box, that just comes as an oracle … yes, you should use it, but then try to get a feeling of what are the rules of thumb that it came up with? And then validate them.”

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This is especially important because systems can be fooled and undermined, or just fail on certain tasks, even those humans can perform easily. For example, adjusting the metadata in images can confuse computers — with a few adjustments, a machine identifies a picture of a dog as an ostrich .

Madry pointed out another example in which a machine learning algorithm examining X-rays seemed to outperform physicians. But it turned out the algorithm was correlating results with the machines that took the image, not necessarily the image itself. Tuberculosis is more common in developing countries, which tend to have older machines. The machine learning program learned that if the X-ray was taken on an older machine, the patient was more likely to have tuberculosis. It completed the task, but not in the way the programmers intended or would find useful.

The importance of explaining how a model is working — and its accuracy — can vary depending on how it’s being used, Shulman said. While most well-posed problems can be solved through machine learning, he said, people should assume right now that the models only perform to about 95% of human accuracy. It might be okay with the programmer and the viewer if an algorithm recommending movies is 95% accurate, but that level of accuracy wouldn’t be enough for a self-driving vehicle or a program designed to find serious flaws in machinery.   

Bias and unintended outcomes

Machines are trained by humans, and human biases can be incorporated into algorithms — if biased information, or data that reflects existing inequities, is fed to a machine learning program, the program will learn to replicate it and perpetuate forms of discrimination. Chatbots trained on how people converse on Twitter can pick up on offensive and racist language , for example.

In some cases, machine learning models create or exacerbate social problems. For example, Facebook has used machine learning as a tool to show users ads and content that will interest and engage them — which has led to models showing people extreme content that leads to polarization and the spread of conspiracy theories when people are shown incendiary, partisan, or inaccurate content.

Ways to fight against bias in machine learning including carefully vetting training data and putting organizational support behind ethical artificial intelligence efforts, like making sure your organization embraces human-centered AI , the practice of seeking input from people of different backgrounds, experiences, and lifestyles when designing AI systems. Initiatives working on this issue include the Algorithmic Justice League and  The Moral Machine  project.

Putting machine learning to work

Shulman said executives tend to struggle with understanding where machine learning can actually add value to their company. What’s gimmicky for one company is core to another, and businesses should avoid trends and find business use cases that work for them.

The way machine learning works for Amazon is probably not going to translate at a car company, Shulman said — while Amazon has found success with voice assistants and voice-operated speakers, that doesn’t mean car companies should prioritize adding speakers to cars. More likely, he said, the car company might find a way to use machine learning on the factory line that saves or makes a great deal of money.

“The field is moving so quickly, and that's awesome, but it makes it hard for executives to make decisions about it and to decide how much resourcing to pour into it,” Shulman said.

It’s also best to avoid looking at machine learning as a solution in search of a problem, Shulman said. Some companies might end up trying to backport machine learning into a business use. Instead of starting with a focus on technology, businesses should start with a focus on a business problem or customer need that could be met with machine learning. 

A basic understanding of machine learning is important, LaRovere said, but finding the right machine learning use ultimately rests on people with different expertise working together. “I'm not a data scientist. I'm not doing the actual data engineering work — all the data acquisition, processing, and wrangling to enable machine learning applications — but I understand it well enough to be able to work with those teams to get the answers we need and have the impact we need,” she said. “You really have to work in a team.”

Learn more:  

Sign-up for a  Machine Learning in Business Course .

Watch an  Introduction to Machine Learning through MIT OpenCourseWare .

Read about how  an AI pioneer thinks companies can use machine learning to transform .

Watch a discussion with two AI experts about  machine learning strides and limitations .

Take a look at  the seven steps of machine learning .

Read next: 7 lessons for successful machine learning projects 

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Machine Language

Extremely difficult and tedius to write programs in machine code, but this was ... hydrodynamics of explosions. statistics. game theory (pioneered mad policy) ... – powerpoint ppt presentation.

  • Computer hardware can process only a small number of primitive instructions
  • arithmetical and logical operations
  • program flow operations
  • moving data between memory and the CPU
  • Extremely difficult and tedius to write programs in machine code, but this was necessary before the invention of high-level languages (1950s).
  • Each instruction in a high-level language (such as Javascript, Basic, C, C, Java, Python, Pascal) is translated into a sequence of machine language instructions.
  • Made deep contributions to
  • quantum physics
  • computer science
  • hydrodynamics of explosions
  • game theory (pioneered MAD policy)
  • and various areas of classical mathematics
  • On the Manhattan project, helped to work out the key ideas involved in thermonuclear reactions and the hydrogen bomb.
  • At 29, an original member of IAS (the demigods).
  • Pioneered the idea of a stored program computer. Before that, programming basically meant moving wires around.
  • and more...
  • Fetch the next instruction
  • Look in PC for location of next instruction, and copy that instruction into the IR.
  • Decode the instruction
  • Get data (if needed)
  • Instruction specifies location of data to be used. Copy data to registers.
  • Execute instruction
  • 3 registers
  • Accumulator
  • Instruction register (IR)
  • Program counter (PC)
  • Instructions are 4-digit decimal numbers.
  • Memory locations are numbered 00, 01, , 99.
  • 10xx (READ)
  • Read a number from the keyboard into memory location xx.
  • 11xx (WRITE)
  • Write a number from memory location xx onto the screen.
  • 20xx (LOAD)
  • Load a number from memory location xx into the accumulator.
  • 21xx (STORE)
  • Store a number from the accumulator into a memory location xx.
  • Add a number from memory location xx
  • to the number in the accumulator.
  • 31xx (SUBTRACT)
  • 32xx (DIVIDE)
  • 33xx (MULTIPLY)
  • 40xx (BRANCH)
  • Copy xx into the program counter.
  • 41xx (BRANCHNEG)
  • Copy xx into the program counter if the number in the accumulator is negative.
  • 42xx (BRANCHZERO)
  • Copy xx into the program counter if the number in the accumulator is zero.
  • 43xx (HALT)
  • Terminate execution of the program.

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