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Home > Machine Learning Guide > 2. What is AI / ML
Podcast: Machine Learning Guide
Episode:

2. What is AI / ML
Category: Technology
Duration: 00:32:05
Publish Date: 2017-02-08 23:00:00
Description:

What is artificial intelligence and machine learning? What's the difference? How about compared to statistics and data science? AI history.

## Resources - Wikipedia:AI (https://en.wikipedia.org/wiki/Artificial_intelligence) `article:easy` - The Quest for Artificial Intelligence (http://amzn.to/2kRd4Ie) (Free PDF? (http://ai.stanford.edu/~nilsson/QAI/qai.pdf)) `book:hard` AI history - Machines of Loving Grace (http://amzn.to/2kRcBWq) `audio:easy` AI history

## Episode What is AI? - Simulate any intellectual task - Goals ** Search / planning (eg chess) ** Reasoning / knowledge representation (eg Watson on Jeopardy) ** Perception ** Ability to move and manipulate objects ** Natural language processing (communication) ** Learning - Applications ** Autonomous vehicles (drones, self-driving cars) ** Medical diagnosis ** Creating art (such as poetry) ** Proving mathematical theorems ** Playing games (such as Chess or Go) ** Search engines ** Online assistants (such as Siri) ** Image recognition in photographs ** Spam filtering ** Prediction of judicial decisions ** Targeting online advertisements - When a technique -> mainstream, no longer AI: "AI effect" ** Pre-programming ** Weak AI vs Strong / AGI

What is ML? - Pattern / Predict / Learn - Versus AI ** The "whole" (robotics, planning, etc) ** Professional: ML more interesting, subsuming other fields; ML is starter ** Conversation "AI when wow-ing or colloquial, ML when being professional. Like "coding" vs "software engineering"" - Versus Stats - Versus DataScience: professionally; ansense vs analytics

History - Greek mythology, Golums - First attempt: Ramon Lull, 13th century - Davinci's walking animals - Descartes, Leibniz - 1700s-1800s: Statistics & Mathematical decision making ** Thomas Bayes: reasoning about the probability of events ** George Boole: logical reasoning / binary algebra ** Gottlob Frege: Propositional logic - 1832: Charles Babbage & Ada Byron / Lovelace: designed Analytical Engine (1832), programmable mechanical calculating machines - 1936: Universal Turing Machine ** Computing Machinery and Intelligence - explored AI! - 1946: John von Neumann Universal Computing Machine - 1943: Warren McCulloch & Walter Pitts: cogsci rep of neuron; Frank Rosemblatt uses to create Perceptron (-> neural networks by way of MLP) - 50s-70s: "AI" coined @Dartmouth workshop 1956 - goal to simulate all aspects of intelligence. John McCarthy, Marvin Minksy, Arthur Samuel, Oliver Selfridge, Ray Solomonoff, Allen Newell, Herbert Simon ** Newell & Simon: Hueristics -> Logic Theories, General Problem Solver ** Slefridge: Computer Vision ** NLP ** Stanford Research Institute: Shakey ** Feigenbaum: Expert systems ** GOFAI / symbolism: operations research / management science; logic-based; knowledge-based / expert systems - 70s: Lighthill report (James Lighthill), big promises -> AI Winter - 90s: Data, Computation, Practical Application -> AI back (90s) ** Connectionism optimizations: Geoffrey Hinton: 2006, optimized back propagation - Bloomberg, 2015 was whopper for AI in industry - AlphaGo & DeepMind
Total Play: 2

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