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 Software ww2 computer science ● ● ● ENIAC ● AI has recompensed + Power ● ● + Capacity - Price ●
Control Theory/ Cybernetics ● Ktesibios of Alexandria } ○ Water Clock (250 BC) (self-regulated feedback control systems) Cornelis Drebbel ● ○ Thermostat (17th Century) Norbert Wiener ● “the scientific study of control and communication in the animal and the machine” (1948) ○ ● Objective Function
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 ■ on 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 Speech Recognition and translation ● DARPA’s ‘Stanley’: object detection ● Microsoft’s speech recognition AI + human reaction data recently hit a 5.1% margin of error. ● NVIDIA’s AI driverless car uses ● Language translation human-driven routes as data to learn from Autonomous logistics & planning AI is often most effective when dealing ★ with large complex problems and LOTS of ● NASA’s Remote Agent and accurate data*** MAPGEN, planning daily Usually, more data > smarter AI ★ operations for spacecraft, Mars rovers ● Military planning (Iraq War)
More examples! Siri, Cortana, Alexa, Bixby Chat bots ● Speech recognition, app integration (shopping, GPS, internet searches), basic scheduling and communication Facebook, Twitter, Netflix, Spotify etc. ● Behavioural analysis and content delivery 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 our popular conception of AI?
Recommend
More recommend
