{"id":957302,"date":"2021-12-07T07:25:36","date_gmt":"2021-12-07T12:25:36","guid":{"rendered":"https:\/\/www.prime-wow.com\/?p=957302"},"modified":"2021-12-07T07:25:36","modified_gmt":"2021-12-07T12:25:36","slug":"snapshots-from-the-history-of-ai-plus-ai-education-resources","status":"publish","type":"post","link":"https:\/\/www.prime-wow.com\/?p=957302","title":{"rendered":"Snapshots from the history of AI, plus AI education resources"},"content":{"rendered":"
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In Hello World issue 12<\/a>, our free magazine for computing educators, <\/em>George Boukeas<\/em><\/strong>, DevOps Engineer for the Astro Pi Challenge here at the Foundation, introduces big moments in the history of artificial intelligence (AI) to share with your learners<\/em>:<\/p>\n<\/div>\n<\/div>\n

The story of artificial intelligence (AI) is a story about humans trying to understand what makes them human. Some of the episodes in this story are fascinating. These could help your learners catch a glimpse of what this field is about and, with luck, compel them to investigate further.\u00a0\u00a0\u00a0 \u00a0\u00a0\u00a0 \u00a0\u00a0\u00a0 \u00a0\u00a0\u00a0 \u00a0\u00a0\u00a0<\/p>\n

The imitation game<\/h2>\n

In 1950, Alan Turing published a philosophical essay titled Computing Machinery and Intelligence<\/em>, which started with the words: \u201cI propose to consider the question: Can machines think?\u201d Yet Turing did not attempt to define what it means to think. Instead, he suggested a game as a proxy for answering the question: the imitation game. In modern terms, you can imagine a human interrogator chatting online with another human and a machine. If the interrogator does not successfully determine which of the other two is the human and which is the machine, then the question has been answered: this is a machine that can think.<\/p>\n

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The Alan Turing Memorial in Manchester<\/figcaption><\/figure>\n<\/div>\n

This imitation game is now a fiercely debated benchmark of artificial intelligence called the Turing test. Notice the shift in focus that Turing suggests: thinking is to be identified in terms of external behaviour, not in terms of any internal processes. Humans are still the yardstick for intelligence, but there is no requirement that a machine should think the way humans do, as long as it behaves in a way that suggests some sort of thinking to humans.<\/p>\n

In his essay, Turing also discusses learning machines. Instead of building highly complex programs that would prescribe every aspect of a machine\u2019s behaviour, we could build simpler programs that would prescribe mechanisms for learning, and then train the machine to learn the desired behaviour. Turing\u2019s text provides an excellent metaphor that could be used in class to describe the essence of machine learning: \u201cInstead of trying to produce a programme to simulate the adult mind, why not rather try to produce one which simulates the child\u2019s? If this were then subjected to an appropriate course of education one would obtain the adult brain. We have thus divided our problem into two parts: the child-programme and the education process.\u201d<\/p>\n

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Chess was among the games that early AI researchers like Alan Turing developed algorithms for.<\/figcaption><\/figure>\n<\/div>\n

It is remarkable how Turing even describes approaches that have since been evolved into established machine learning methods: evolution (genetic algorithms), punishments and rewards (reinforcement learning), randomness (Monte Carlo tree search). He even forecasts the main issue with some forms of machine learning: opacity. \u201cAn important feature of a learning machine is that its teacher will often be very largely ignorant of quite what is going on inside, although he may still be able to some extent to predict his pupil\u2019s behaviour.\u201d<\/p>\n

The evolution of a definition<\/h2>\n

The term \u2018artificial intelligence\u2019 was coined in 1956, at an event called the Dartmouth workshop. It was a gathering of the field\u2019s founders, researchers who would later have a huge impact, including John McCarthy, Claude Shannon, Marvin Minsky, Herbert Simon, Allen Newell, Arthur Samuel, Ray Solomonoff, and W.S. McCulloch.\u00a0\u00a0\u00a0<\/p>\n

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Go has vastly more possible moves than chess, and was thought to remain out of the reach of AI for longer than it did.<\/figcaption><\/figure>\n<\/div>\n

The simple and ambitious definition for artificial intelligence, included in the proposal for the workshop, is illuminating: \u2018making a machine behave in ways that would be called intelligent if a human were so behaving\u2019. These pioneers were making the assumption that \u2018every aspect of learning or any other feature of intelligence can in principle be so precisely described that a machine can be made to simulate it\u2019. This assumption turned out to be patently false and led to unrealistic expectations and forecasts. Fifty years later, McCarthy himself stated that \u2018it was harder than we thought\u2019.<\/p>\n

Modern definitions of intelligence are of distinctly different flavour than the original one: \u2018Intelligence is the quality that enables an entity to function appropriately and with foresight in its environment\u2019 (Nilsson). Some even speak of rationality, rather than intelligence: \u2018doing the right thing, given what it knows\u2019 (Russell and Norvig).<\/p>\n

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The amount of training data AI developers have access to has skyrocketed in the past decade.<\/figcaption><\/figure>\n<\/div>\n

Read the whole of this brief history of AI in Hello World #12<\/h2>\n

In the full article, which you can read in the free PDF copy of the issue<\/a>, George looks at:<\/p>\n