CSc-180 (Gordon) Week 1A notes

Key points:

• Consciousness o A “very hard problem” that we mostly will ignore o See: “Chinese Room Experiment” by John Searle o See: “Partial Brain Thought Experiment” by Jacques Mallah o See: “Consciousness” by Susan Blackmore (book) • What is intelligence? • Elements of intelligence (partial list): o Reasoning / solving problems o Imagination, new ideas o Ability to generalize o Abiltity to learn o Planning o Human-like behavior (or animal-like) o Rational behavior (able to explain) o Knowledge • Definitions of “Artificial Intelligence” (from various textbooks): o Negnevitsky: “Ability to learn and understand, to solve problems and make decisions.” o Kurzweil: “…machines that perform functions that require intelligence when performed by people.” o Coppin: “…systems that act in a way that to any observer would appear to be intelligent.” o Coppin: “…using methods based on the intelligent behavior of humans and other animals to solve problems” o Russell/Norvig: . Systems that act like humans, OR . Systems that think like humans, OR . Systems that act rationally, OR . Systems that think rationally • “Turing Test” o Human in one room, hooked to an “agent” in another room via computer chat session (keyboard) o Human tries to determine if “agent” is a human or a computer o Human is free to discuss anything, can ask challenging questions (human knows this is a “test”) o Passing presumably requires knowledge base, natural language processing, context understanding • “Eliza” o Computer program written in 1966 (Weizenbaum) o Trivial program (~200 lines of code), simply bounces back human input with minor modifications o Many people thought it was a human o Challenges significance of Turing Test • “Weak AI” vs “Strong AI” o “WEAK” – can machines ACT intelligently? (see Kurzweil, and Coppin #1 definitions) o “STRONG” – can machines really have an underlying understanding? (see Negnevitsky, Coppin #2) o Passing Turing Test might only require weak AI… we don’t know yet. o Open question – does AI require having lots of knowledge? Or is intelligence knowledge-independent? o “Strong” doesn’t mean “perform better”. Many of the best systems are “weak AI”. • Ethical Considerations: o People can lose their jobs to automation o Loss of accountability – what if an AI system makes a fatal mistake? . What if the AI system can’t explain how it made its decision? . Is the programmer liable? The agency that used the software? o Is it ethical to turn off a possibly-conscious system? o “Technical Singluarity” (proposed by Kurzweil) – when computers exceed us, and improve themselves

CSc-180 (Gordon) Week 1A notes

Some important AI events in history

1800s Ada describes how Babbage’s analytical engine could be programmed to play chess 1930/40s artificial neurons (McCulloch/Pitts) algorithms for playing chess (Turing, Mitchie) 1950s Turing proposes Turing Test Shannon publishes minimax algorithm, including a full spec for a chess program Perceptron learning algorithm (Rosenblatt, problem: linear separability) Term “AI” coined by J. McCarthy McCarthy also defines the LISP language Logic Theorist and General Problem Solver (Newell/Simon/Shaw) Samuels’ learning checker program defeats a strong human player 1960s Blocks World (Minsky/Huffman) Fuzzy Logic (Zadeh) A* (Hart/Nilsson/Raphael) Eliza (Weizenbaum) Shakey the Robot (Stanford) AI skeptics (Lucas, Dreyfus – “What Computers Can’t Do”) 1970s Theory of NP Completeness (Cook/Levin, Hopcroft) Expert Systems (DENDRAL, MYCIN, INTERNIST) Frames (Minsky) STRIPS planning language (Sacerdoti) BKG (Berliner) defeats backgammon world champion Genetic Algorithms (Holland) 1980s Industrial use of expert systems (ALACRITY, NASA/CLIPS) “5th Generation Computing” (Prolog, mostly in Japan) ALVINN (Pomerleau) autonomous land vehicle Backpropagation solves linear separability problem (Rumelhart/McClelland) 1990s Genetic Programming (Koza) Deep Blue (Hsu/IBM) defeats world chess champion PSO / Swarm Intelligence (Kennedy/Eberhart) Ant Colony Optimization (Dorigo) 2000s Chinook solves checkers (Schaeffer) Robotics advances (NASA, ASIMO, Roomba, Nomad) Self-driving cars “Elbot” fools three judges at Loebner competition (Roberts) 2010s “Watson” (Ferucci/IBM) wins at Jeopardy Advances in natural language (Siri, Google Now, Cortana) Advances in multilayer neural networks – “deep learning” Convolutional Neural Networks for image recognition (Ciresan/Meier/Schmidhuber) “Alpha Go” (DeepMind/Google) defeats world Go champions