In practical terms, however, the idea didn't really take off until 1950. Isaac Asimov published his Three Laws of Robotics. The phrase artificial intelligence was coined in 1956 by John McCarthy, who organized an academic conference at Dartmouth dedicated to the topic. The phrase "machine learning" also dates back to the middle of the last century. In 1959, Arthur Samuel defined machine learning as "the ability to learn without being explicitly programmed." And he went on to create a computer checkers application that was one of the first programs that could learn from its own mistakes and improve its performance over time. In that year, Alan Turing published a groundbreaking paper called "Computing Machinery and Intelligence" that posed the question of whether machines can think, in the famous Turing test. Joseph Weizenbaum (MIT) built ELIZA, an interactive program that carries on a dialogue in English language on any topic.
Like artificial intelligence research, machine learning fell out of vogue for a long time, but it became popular again when the concept of data mining began to take off around the 1990s. Data mining uses algorithms to look for patterns in a given set of information. Machine learning does the same thing, but then goes one step further – it changes its program's behavior based on what it learns. Other people prefer to use the term "machine learning" because they think it sounds more technical and a little less scary than "artificial intelligence." Someone on the Internet commented that the difference between the two is that "machine learning actually works." However, machine learning has been part of the discussion around artificial intelligence from the very beginning, and the two remain closely entwined in many applications coming to market today. For example, personal assistants and bots often have many different artificial intelligence features, including machine learning.
Over the past few years artificial intelligence has exploded, and especially since 2015. Much of that has to do with the wide availability of GPUs that make parallel processing ever faster, cheaper, and more powerful. It also has to do with the simultaneous one-two punch of practically infinite storage and a flood of data of every stripe (that whole Big Data movement) – images, text, transactions, mapping data, you name it. Stories about IBM's Watson AI winning the game show Jeopardy and when Google DeepMind’s AlphaGo deep learning program defeated S. Korean Master Lee Se-dol in the board game “Go”. These two examples and the appearance of BOTs have returned artificial intelligence to the forefront of public consciousness. Of course, "machine learning" and "artificial intelligence" aren't the only terms associated with this field of computer science. IBM frequently uses the term "cognitive computing," which is more or less synonymous with artificial intelligence.
However, some of the other terms do have very unique meanings. For example, an artificial neural network has been designed to process information in ways that are similar to the ways biological brains work. Things can get confusing because neural nets tend to be particularly good at machine learning, so those two terms are sometimes conflated.
The easiest way to think of their relationship is to visualize them as concentric circles with artificial intelligence — the idea that came first — the largest, then machine learning — which blossomed later, and finally deep learning — which is driving today’s artificial intelligence investment explosion.
Artificial Intelligence: Most Active VC Investors
Ranking, 2011 to 2016 year-to-date (as of June 2016)
1 Khosla Ventures
2 Intel Capital
2 Data Collective
4 Google Ventures
5 New Enterprise Associates
6 Andreessen Horowitz
7 Formation 8
8 Horizons Ventures
9 Accel Partners
10 Plug and Play Ventures
11 GE Ventures
12 Two Sigma Ventures
13 Samsung Ventures
14 Norwest Venture Partners
15 Bloomberg Beta
Khosla Ventures is the most active VC investor in AI-based companies. They 15 unique companies, including Atomwise, MetaMind (recently acquired by Salesforce), Scaled Inference, and LiftIgniter.
Intel Capital backed startups including Lumiata, DataRobot, Perfant Technology and Parallel Machines and Data Collective (backer of Blue River Technology, Descartes Labs, SigOpt, and Nervana Systems).
Maybe the VC guys forgot the failure of Thinking Machines, let's hope whatever you call it, it will be successful.
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