Machine Learning & Artificial Intelligence – Same but Different
For those that wonder what Google is actually aiming for, Larry Page offers clear direction. He said recently, “Artificial intelligence would be the ultimate version of Google. The ultimate search engine that would understand everything on the Web. It would understand exactly what you wanted, and it would give you the right thing.” The key components of the quote are that this AI-based uber-Google would not only know “everything” on the web (basically the sum of all human knowledge) but would also have an insight into the specific needs of each user, presumably even before the need was stated (hence the use of the term “what you wanted”). These two differences are central to the AI leap – essentially aimed at helping Google make the move from a highly sophisticated database search engine that compares text to a system that has a level of “understanding” of the context. The simplest definitions of AI still call this a branch of computer science concerned with making computer systems behave like humans.
Google is, of course, not the only company to set its sights on AI. In fact, a report in Nov. 2015 by Stratistics MRC predicted that due to strong impetus from areas like healthcare, transportation, aviation, defense and financial systems the global market for AI systems would grow at over 25% y-o-y from 2014 through to 2022 and reach $ 40 Billion. Starting with rudimentary forms of AI on our smartphones like Siri, there are a variety of real world applications, claiming to be AI, already out there. Expert Systems in various areas are already widely used and continue to proliferate. Among the most exciting efforts out there is by Cycorp. They are looking to, essentially, collate all the millions (billions?) of pieces of human intelligence out there into an AI system, Cyc, that can, in turn, serve computer systems that need that intelligence. Clearly we need to be interested in AI – and not only because it will help us when the robots do take charge of the world.
As a Data Science focused company we have spoken often of Machine Learning and sometimes for the sake of convenience we have been guilty of using the terms Machine Learning and Artificial Intelligence interchangeably – that is, of course, not really the case. First, our defense for using the terms interchangeably (we do know what we are talking about!). At their core both Machine Learning and Artificial Intelligence are concerned with allowing systems to analyze the data sets at hand, extrapolate from them and draw conclusions that allow them to take (or suggest) appropriate courses of action. The applicability of each term in the context of the big data, analytics and business intelligence is reasonably similar. In the wider context though where, then does one end and the other take over?
Most agree that Machine Learning is a kind of subset of Artificial Intelligence. The focus of Machine Learning are the algorithms that allow the system to use smaller data-sets and extrapolate to address new and, previously, unknown situations. Since the focus of AI is to create a system that can behave like humans much more is needed. To behave like a human a system has to pass the Turing Test, i.e. provide a response to a situation that to an interpreter would be indistinguishable from that a human would provide. The expectation from the AI system would be that not only would it be able to arrive at the right course of action but would also be able to communicate it, put in place an execution plan (even if it is only to deliver the communication) and in the case of a physical action, act on it. The rationale is thus, that for a computer system to become artificially intelligent it would have to draw on machine learning in addition to several other capabilities like Natural Language Processing, Knowledge Representation, Planning, Scheduling and most likely Robotics too.
In many ways this is where the real world and the future diverge – while there is great excitement and anticipation at the possibilities there are not yet many cases of computers exhibiting human behavior either widely or consistently. It is also, perhaps, true to say that a lot of the good vibes surrounding AI are due to the successes of Machine Learning. There seems to be a way to go yet – but there is no denying the promise. Let’s leave the last word to possibly the most recognizable “Artificial Intelligence” entity out there, The Terminator himself, Arnold Schwarzenegger. He said, “We are going in the direction of artificial intelligence or hybrid intelligence where a part of our brain will get information from the cloud and the other half is from you, so all this stuff will happen in the future. What can we say except, “Hasta la vista”!