MLG 002 What is AI, ML, DS

Machine Learning Guide

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What is artificial intelligence, machine learning, and data science? What are their differences? AI history.

Hierarchical breakdown: DS(AI(ML)). Data science: any profession dealing with data (including AI & ML). Artificial intelligence is simulated intellectual tasks. Machine Learning is algorithms trained on data to learn patterns to make predictions.

Artificial Intelligence (AI) - Wikipedia

Oxford Languages: the theory and development of computer systems able to perform tasks that normally require human intelligence, such as visual perception, speech recognition, decision-making, and translation between languages.

AlphaGo Movie, very good!

Sub-disciplines

Reasoning, problem solving
Knowledge representation
Planning
Learning
Natural language processing
Perception
Motion and manipulation
Social intelligence
General intelligence

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

Machine Learning (ML) - Wikipedia

Oxford Languages: the use and development of computer systems that are able to learn and adapt without following explicit instructions, by using algorithms and statistical models to analyze and draw inferences from patterns in data.

Data Science (DS) - Wikipedia

Wikipedia: Data science is an interdisciplinary field that uses scientific methods, processes, algorithms and systems to extract knowledge and insights from noisy, structured and unstructured data, and apply knowledge and actionable insights from data across a broad range of application domains. Data science is related to data mining, machine learning and big data.

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

57 episodes

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