Artificial Intelligence and Machine Learning Sydney Carter, Thomas - - PowerPoint PPT Presentation

Artificial Intelligence and Machine Learning

Sydney Carter, Thomas Politopoulos, Tariq Jamal, Kian Ettehadieh

What is AI?

Four schools of thought:

Acting Humanly

The Turing Test

• Natural language processing
• Knowledge representation
• Automated reasoning
• Machine learning

Total Turing Test adds:

• Computer vision
• Robotics

Thinking humanly

Relies on cognitive science

• Follow the same steps as humans
• But how do humans think?

Difference between performing a task well and performing it like a human.

Thinking rationally

Thinking logically “Patterns for argument structures that always yielded correct conclusions when given correct premises” Logical notation: “Socrates is a man; all men are mortal; therefore, Socrates is mortal.” Obstacles:

• Translating to logical notation
• Are these premises correct?
• Computational resources

Acting rationally

Rational Agent:

• Acts autonomously
• In pursuit of the optimal outcome

Inference: a conclusion reached on the basis of evidence and reasoning.

• Only part of being rational
• Quick vs Deliberate

Limited rationality

• Perfect rationality isn’t always feasible
• Computational limits

The Foundations of AI

Philosophy

• Aristotle (384-322 BC)

○ Logic

• Ramon Lull (1315)

○ human made objects

• Thomas Hobbes (1588-1689)

○ Leviathan (1651) ○ Artificial Animal

• Rene Descartes (1596-1650)

○ “if the mind is governed entirely by physical laws, then it has no more free will than a rock “deciding” to fall toward the center of the earth” ○ Dualism, we are a body, and a soul (something outside of nature) ○ Materialism, the mind creates consciousness

• Logical Positivism

○ Rationalism + Empiricism = (Logic + Observed Sensory Experiences)

Mathematics

• Logic

○ George Boole ■ BOOLEAN logic ○ Gottlob Frege

• Computation

○ Limits between Logic and Computation

• Probability

○ Gerolamo Cardano, 16th Century

Economics

• Adam Smith (1723-1790)

○ “was the first to treat [economics] as a science”

• Leon Valrasse/ Frank Ramsey

○ UTILITY

• Decision Theory

○ (probability + utility theory)

Neuroscience

• Paul Broca (1824-1880)

○ Study on Aphasia in patients with brain damage (1861)

• Singularity

Computer Engineering

Hardware

• ww2
• ENIAC
• + Power
• + Capacity
• Price

Software

• computer science
• AI has recompensed

Control Theory/ Cybernetics

• Ktesibios of Alexandria

○ Water Clock (250 BC)

• Cornelis Drebbel

○ Thermostat (17th Century)

• Norbert Wiener

○ “the scientific study of control and communication in the animal and the machine” (1948)

• Objective Function

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(self-regulated feedback control systems)

Linguistics

• Noam Chomsky (b. 1928)

○ Syntactic Structures (1957) ■ Goes against Behaviourist theory

• Natural Language Processing

○ context and understanding of subject matter

• Knowledge Representation

History of AI

• The Gestation of AI (1943-1955)

○ First recognizable work done by Warren McCulloch and Walter Pitts ■ Basic physiology and functions of neurons ■ Analysis of propositional logic ■ Turing’s theory of computation ○ Sparked many further developments in early understandings of AI ■ SNARC, Turing Test

• The Birth of AI (1956)

○ John McCarthy organized AI workshop at Dartmouth ■ Minsky, Claude Shannon, Nathaniel Rochester ■ No major breakthroughs ■ Established AI as it’s own separate field from the other computer sciences

The History of AI (cont.)

• Early Enthusiasms (1952-1969)

○ People were excited by the notion of computers solving nonlinear problems ○ General Problem Solver, Newell and Simon ■ First program to embody ‘thinking humanly’ ○ Sparked development of more ‘intelligent’ programs, and AI culture ■ Physical Symbol System (Newell and Simon) ■ Geometry Theorem Prover (Herbert Gelernter) ■ Checkers playing program (Arthur Samuel) ■ LISP (McCarthy, early dominant AI programming language) ○ However, despite all the enthusiasm, AI did not develop as quickly or impactfully as was predicted

The History of AI (cont.)

• Developments in AI (1969-1979)

○ Success in principal = practical failure ■ AI systems of this time were unable to interpret variable such as ambiguity ■ As the problems AI was intended to solve grew more complex, original theories tested

• n simplistic problems proved not to work

○ Many governments and educational institutions stopped or decreased funding for AI research and development ○ The DENDRAL program, Buchanan 1969, offered counter approach to the ‘weak method’ problem solving of AI machines of the time ■ First specific knowledge intensive system ■ Resurfaced hope for the AI and intelligent machine industry

The History of AI (cont.)

• AI becomes an industry (1980-present)

○ The first commercial AI system was R1, Mcdermott ■ Digital Equipment Corporation, helped configure orders for new computer systems ■ Saved to company $40million and year by 1986 ■ By 1988, other companies jumped on board, incorporating AI to their systems ○ AI industry boomed from a few million dollars in 1980 to billions of dollars in 1988

The History of AI (cont.)

• Further notable developments

○ AI adopts the scientific method ■ AI has come to embrace its scientific counterparts

• Influenced by fields such as neuroscience, psychology, computer science

■ It has established itself as its own scientific field which is continuously evolving ○ The emergence of intelligent agents ■ AI has reached a level of sophistication where intelligent machines and programs have developed a sense of agency

• Siri and Alexa, Facebook and Instagram algorithms

Quick note: Common AI Analogies (Hebron)

Biological systems

• The brain and body systems as inspiration and as a model: “a central metric

in evaluating machine intelligence since the inception of the field.” Thermodynamics

• Balancing a multiplicity of factors to reach equilibrium

Electrical systems

• If neural communication is made up of electrical signals, can we create

intelligent circuits?

• Problem: neural networks don’t work with switches and paths, they are more

like peer-to-peer sharing; each neuron has a piece of the larger whole.

“What can AI do today?” - Russell et al

Robotic Vehicles

• DARPA’s ‘Stanley’: object detection

+ human reaction data

• NVIDIA’s AI driverless car uses

human-driven routes as data to learn from Speech Recognition and translation

• Microsoft’s speech recognition AI

recently hit a 5.1% margin of error.

• Language translation

Autonomous logistics & planning

• NASA’s Remote Agent and

MAPGEN, planning daily

• perations for spacecraft, Mars

rovers

• Military planning (Iraq War)

★ AI is often most effective when dealing with large complex problems and LOTS of accurate data*** ★ Usually, more data > smarter AI

More examples!

Siri, Cortana, Alexa, Bixby

• Speech recognition, app integration

(shopping, GPS, internet searches), basic scheduling and communication

Facebook, Twitter, Netflix, Spotify etc.

• Behavioural analysis and content delivery

Chat bots Healthcare

• Personal care assistance, diagnostics
• Cybernetic limbs require learning human

nerve inputs

More examples!

Gaming

• Legitimate AI

○ Human vs. Computer board games (IBM chess CPU)

• The AI effect

○ Devs attempt to create the illusion that the game’s characters/opponents are thinking ○ Positive user experience doesn’t require realistic AI

• The Uncanny Valley

○ Similar to the uncanny valley of CGI, extreme fidelity can be unsettling to players

QUESTIONS!~

• What sort of task or problem do you think would benefit from an AI

application? What might be some advantages., risks, precedents etc?

• If a 100% accurate AI system existed, would there be any tasks that you would

not allow it to handle?

• Consider some of media representations of AI in areas such as film,

advertising, literature and digital tech. How do you think they have influenced

• ur popular conception of AI?