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My question is this:

Is there any difference between machine learning and artificial intelligence? Or do these terms refer to the same thing?

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    $\begingroup$ AI = thinking robots. ML = estimating functions. $\endgroup$ – Emre May 19 '17 at 7:36
  • $\begingroup$ @Emre: Could you Elaborate? Possibly enough to be an answer? 😊 $\endgroup$ – Sairaam Venkatraman May 19 '17 at 7:39
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    $\begingroup$ @Emre aren't thinking robots just some entities that estimate functions? $\endgroup$ – famargar May 19 '17 at 13:00
  • $\begingroup$ @famargar: That description does not convey the gist of it. Would you describe your intelligence as just function estimation? $\endgroup$ – Emre May 19 '17 at 16:38
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    $\begingroup$ @Emre Why not, though? It could be that the function is very complicated. $\endgroup$ – Sairaam Venkatraman May 20 '17 at 6:29
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The subject areas Artifical Intelligence and Machine Learning (plus Data Science) are loosely defined, such that it is hard to make strict statements about how they relate. In the general case, it seems that there are parts that overlap, but that they are quite far from being "the same subject with two different names" as suggested in the question.

The term Artifical Intelligence has many possible meanings and interpretations - which version to refer varies by time and by the source using it. Textbooks on artificial intelligence will often cover topics such as search algorithms, logical deduction and other things which are clearly not machine learning as it is practised today.

For instance, we could take it to refer to Artificial General Intelligence (or "hard AI"), and it should be clear in this case that at least some form of learning algorithm(s) would be required to meet the goals of AGI. However, it is far less clear how much of AGI can be solved by combining machine learning into complex structures.

The term Machine Learning has a few different working definitions, but this is a popular one:

A computer program is said to learn from experience E with respect to some class of tasks T and performance measure P if its performance at tasks in T, as measured by P, improves with experience E.

This is far more tightly defined than Artificial Intelligence, but still has a lot of scope.

The trend to conflate AI and ML appears to be a media and marketing issue, not a technical one. I suspect this is in part due to advances in the last 5-10 years in neural networks. Neural network models have made strong progress, especially in signal processing of images, video, audio. There is also an analogy with biological brains which can be compelling - especially when the subject matter is simplified for consumption by mainstream media.

It is worth mentioning Data Science too. Like Artificial Intelligence, the term is somewhat fuzzily defined. Also like AI, Data Science has more to it than just Machine Learning. To Data Science practitioners, ML is part of a toolkit to achieve goals - for some people it is a large part of what they do, for others it is just one part of a wider scope (actually training and refining a ML model might take only a small fraction of a professional data scientist, analyst or statistician's time). I think it is reasonable to state that Artificial Intelligence and Data Science relate to Machine Learning in a similar way.

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Machine learning in layman terms is an algorithm that allows machines to identify patterns in data and then develop a model which can be used to predict unseen data.

Artificial Intelligence is the ability of machines to make intelligent decisions which are equal to or better than their human counterpart.

Difference between the two:

A.I. is a very broad field of computer intelligence in which machine learning is one of the ways it gains the intelligence to predict outcomes. But AI also contains robotics, speech synthesis, computer vision and others.

So if I were to draw a Venn diagram of artificial intelligence then machine learning would be a subset.

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    $\begingroup$ This is how I explain this to not technical people. It is interesting the common knowledge of this being so misunderstood subject. and when most people speak about A.I. the are talking about AGI. Everyone is afraid of AIG but not AI as we use that every day from car manufacturing to packaging. At least that is my perspective on this. What is scary to me is AIG with ML and not having any rules (morals per say). $\endgroup$ – JayRizzo Aug 5 '18 at 21:53
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Deep Learning is a subset of Machine Learning which is a subset of Artificial Intelligence. Machine learning is a particular approach for AI but not the only one. Symbolic Logic, Bayersian Statistics are a few examples of AI approaches which do not use any kind of machine learning algorithms.

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    $\begingroup$ A brief and clearer definition of AI and machine learning do that the difference between the two is seen would be more helpful. $\endgroup$ – Sairaam Venkatraman May 19 '17 at 9:36
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A good example of AI, but not machine learning is evolutionary computation. Here instead of learning from experience (as in Tom M. Mitchell's definition) we have genotype changing in each generation of computer program version, measured by its performance at task (phenotype expression in environment).

As Melanie Mitchell puts it:

'...from the earliest days computers were applied... to modeling the brain, mimicking human learning, and simulating biological evolution... The first has grown into the field of neural networks, the second into machine learning, and the third into what is now called "evolutionary computation,"...' Although, now neural networks are mostly considered as part of machine learning .

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ML, by Tom M. Mitchell:

A computer program is said to learn from experience E with respect to some class of tasks T and performance measure P if its performance at tasks in T, as measured by P, improves with experience E.

AI, but not ML:

Thank you, Servan Grüninger, for your help.

See also: How does machine learning relate to artificial intelligence?

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As the great Tom Mitchell has said in his book "Machine Learning is the ability to learn without being explicitly programmed."

Machine learning algorithms are widely employed and are encountered daily. Examples are automatic recommendations when buying a product or voice recognition software that adapts to your voice.

AI is any technology that enables a system to demonstrate human-like intelligence.

"If we plug several photos of cats doing different things or in different places into a computer, but all the photos are still tagged as cats, then the computer will learn from each photo it is shown,” said Kamelia Aryafar, Ph.D., director of machine learning at Overstock. “Eventually, it will recognize that the cat is the common denominator in each set of data, in turn helping the computer learn to identify cats.”

When a machine can tell the difference between objects and make a choice to discard or accept them, based on understood criteria, AI is born. In fact, any time a decision is being made by a machine, that is artificial intelligence and has gone beyond mere machine learning.

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Let's take the total Turing test as an example. A computer is often said to be intelligent if it can pass the total Turing test.

A computer passes the test if a human interrogator, after posing some written questions, cannot tell whether the written responses come from a person or from a computer. The total Turing Test also includes a video signal so that the interrogator can test the subject's perceptual abilities, as well as the opportunity for the interrogator to pass physical objects "through the hatch."

To pass the total Turing Test, the computer would need to possess the following capabilities:

  • natural language processing to enable it to communicate successfully in English;
  • knowledge representation to store what it knows or hears;
  • automated reasoning to use the stored information to answer questions and to draw new conclusions;
  • robotics to manipulate objects and move about;
  • computer vision to perceive objects, and
  • machine learning to adapt to new circumstances and to detect and extrapolate patterns.

As you may already see, Machine Learning is a subset of Artificial Intelligence that concerns with the ability of an intelligent agent to learn.

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Artificial intelligence : program that can sense, reason, act and adapt.

Machine learning : Algorithms whose performance improve as they are exposed to mode data over time.

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