to calculate integrals • General Problem Solver : elementary logic and algebra (ex : Tower of Hanoi) • STUDENT : high school algebra word problems • ELIZA : psychiatry conversation • Micro-worlds Minsky, 1970 : "In from three to eight years we will have a machine with the general intelligence of an average human being."
: “utter failure of AI to achieve its grandiose objectives” Example : “The spirit is willing, but the flesh is weak” -> Russian -> English : “The whisky is strong, but the meat is rotten” Funding killed for a decade
to develop - harder to maintain - can’t replicate across fields Still can’t interact with the real world : - impossible to acquire data - all brain but no body ("Elephants Don't Play Chess") Funding stopped again
Human interaction : ◦ Handwriting ◦ Speech ◦ Natural language • OCR • Image recognition • Information retrieval • Artificial personal assistants • Recommendations systems • Drones • Game playing • ...
DeepBlue vs Kasparov : 1996 : 1W, 2D, 3L 1997 : 2W, 3D, 1L This switched the canonical example of a game where humans outmatched machines to the ancient Chinese game of Go, a game of simple rules and far more possible moves than chess, which requires more intuition and is less susceptible to brute force.
2016 : 4-1 "All but the very best Go players craft their style by imitating top players. AlphaGo seems to have totally original moves it creates itself." Lee Sedol : "I misjudged the capabilities of AlphaGo and felt powerless."
human through text messaging ? Judges interact with humans and robots. They must find out which is which. Success in June 2014 AI passed as 13-year Ukrainian boy, Eugene Goostman
human through text messaging ? Judges interact with humans and robots. They must find out which is which. Success in June 2014 AI passed as 13-year Ukrainian boy, Eugene Goostman
are generic algorithms that can tell you something interesting about a set of data without you having to write any custom code specific to the problem. Instead of writing code, you feed data to the generic algorithm and it builds its own logic based on the data.
on data • system is both the algorithm and the data • starts with a hypothesis about how we can represent the data (for linear regression : a straight line) • only as good as your data • can deal poorly with outliers • lots of calculation to learn, but very fast to apply (can run on mobile)
of previous nodes More nodes can handle more complexity Input must be normalized For example, all images -> 20x20 Training in multiple steps - left to right to evaluate the training set - right to left to propagate errors
◦ Prescription recommendations • Legal : ◦ Hired as a lawyer (“Ross”) • Teaching : ◦ Used as a TA (“Jill Watson”) • Cooking : ◦ published a recipe book ◦ new combinations ◦ able to avoid allergies
The old man the boat. • While the man hunted the deer ran into the woods. • While Anna dressed the baby played in the crib. • Buffalo buffalo Buffalo buffalo buffalo buffalo Buffalo buffalo. It makes mistakes on: • I convinced her children are noisy. • The coach smiled at the player tossed the frisbee. • The cotton clothes are made up of grows in Mississippi. • James while John had had had had had had had had had had had a better effect on the teacher
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