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CIS 140, Week 1 Notes

by: Alexis Mitchnick

CIS 140, Week 1 Notes CIS 140

Alexis Mitchnick
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About this Document

Covers Turing Machines, Marr Levels, universal computing, comparisons between brain and computer, concept: "can computers think?"
Intro to Cognitive Science
David Hoyt Brainard, Lyle H Ungar
Class Notes
Cognitive Psychology, Turing, Marr




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This 5 page Class Notes was uploaded by Alexis Mitchnick on Sunday September 4, 2016. The Class Notes belongs to CIS 140 at University of Pennsylvania taught by David Hoyt Brainard, Lyle H Ungar in Fall 2016. Since its upload, it has received 288 views. For similar materials see Intro to Cognitive Science in Cognitive Science at University of Pennsylvania.

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Date Created: 09/04/16
Tuesday, August 30, 2016 CIS 140 Introduction - Can computers think? Can they recognize irony/do it without being told? • • Are they conscious/what would it mean if they could be conscious? • Qualia: sensory perceptions, feelings - Can computers behave as if they think? • Real thinking requires qualia, but they can behave as if they think - Can computers be intelligent? • Intelligence: being able to retain knowledge, make connections, digest information, solving problems, perception of surrounding, observing/copying, learning and adapting, decision-making, predicting, memory, hypothetical thinking, imagination, insight and creativity, physical stimuli, faith/opinions, curiosity (in sense of systematic exploration), autonomy, *is consciousness necessary - The relation between mind and brain - Traditional view: Locke says “incogitative Matter and Motion can’t produce thought” - Dualism: mind and brain are separate but connected (Descartes) - how are they connected? - Identity: mind and brain are different descriptions of same thing (Spinoza) - music as example: can be represented by the notes or by the actual sound - can’t really prove it - identity theory is hard to generalize into different architectures, physical differences matter more - Functionalism: the function matters, not how it is done - “clock is something used to tell time”.. all clocks are the same - machine functionalism: mind is to brain as program is to computer 1 Tuesday, August 30, 2016 - disregards how it is done, but sometimes that might matter - Turing Tes:tCan machines think? - replaces question: “Imitation Game” aka Turing Test - Involves interrogator, person, and computer… if by the end the interrogator can’t tell between person and computer then the computer has passed the Turing Test - no computers have passed yet - Is it a good test of intelligence? - tests if computer can behave as if it is intelligent, shows intelligence of programmer not computer - Is the test showing a necessary condition of intelligence or just a sufficient condition of intelligence? - not necessary: something can be intelligent and fail Turing Test - argument against test: not sufficient — computers lack semantics, qualia, feelings, consciousness, intentionality - Semantics: can computer be grounded in real world? Ex. self-driving cars, things that can manipulate the world - Qualia: can a computer be happy? sad? What would it mean for computer to be happy? - Intentionality: computers just do what they're told; if a robot “raised its arm” did it intend to? - Turing says it doesn't matter how you think; others say it matters - Marr Levels: what it does (black box), how it does it (algorithm), how it does it (hardware/wetware) - Ned Block: Turing Test is insufficient - Ways to pass: store all the answers in computer? doesn't work because questions can be based on previous questions - Aunt Bertha Machine — form a tree based on the answers, exponential growth 2 Tuesday, August 30, 2016 - Block says computer should have capacity to answer without having to store all the answers… universe does not have storage capacity to have a Bertha machine! - Turing Test can be thought of as an interactive proof of intelligence Lecture 2 - Computational explanations of intelligence • Computers and brains Marr levels • • Universal Computing (Turing Machines) - Are brains like computers? - different hardware, parallel vs serially - Hypothesis: brains are like computers - both info. processing devices (receive input, store and retrieve data, compute, output results); computation = thought - Implicit assumptions (doesn't matter what the hardware is, still the same computation); logic is “the language of thought” - Machine Functionalism - Alternative hypothesis - different hardware for each problem (“swiss army knife” approach) - hardware and wetware matter (how hardware is wired determined how it works) - Marr Levels (David Marr): - Computational Level (what does the system do?) - Representation and algorithm (how is computation implemented?) - Hardware implementation (how can algorithm be realized physically?) - ex. “look up a word” - Representation level: how you store words… through context, alphabetically listed, most frequent to least frequent (brains sort of behave as if they're stored that way) 3 Tuesday, August 30, 2016 - Turner Test: concerned with computational level - Aunt Bertha Machine: concerned with representation level - Representation matters: different representations can be logically = (ex. IV + IX vs 4 + 9)… but could make reasoning harder/easier - Universal computing: abstract away hardware and focus on what’s done - “Computers can’t be happy or sad”: Marr level can be argued, ex. hardware: levels of serotonin determine happiness - Logical vs. physical architectures - logical arch. - ex. Google, Eniac appear to be single machine - physical arch. - both are many machines, you just can’t see which machine you're using - View mind and brain as “modules”… map between functional mind modules and brain regions - Universal Computation: one form of abstract computing which can emulate any other computing method - Turing believes universal computation is possible - Foundations of math logic: - Hilbert: consistent? complete? decidable? - Godel: math logic is incomplete - Turing: math logic is undecidable - Turing Machine comes in different variations, is a universal computer - Tape: shows input and then final output; State: helps keep track of what program is doing - Turing proves with his machine that logic is undecidable: some programs never stop and go on infinitely, therefore proof of that program is unsolvable; shows undecidability - The Game of Life: little cubes on a big grid — equivalent to a Turing Machine - Why are Turing Machines important for cog. sci.? 4 Tuesday, August 30, 2016 - Modern computers are TMs - Human brains are stochastic (random): given same input can produce a different output (unlike computer)… but can write a pseudo-random output using TM - Turing vs. Wittgenstein: war of computational logic… whether or not contradictions matter (Turing says yes, Wittgenstein says no) - Digital vs. Analog: - Digital = discrete, on/off, 1/0 - Analog = continuous, real numbers - Themes: - Parallelism/interconnection - continuous vs. discrete - random vs. deterministic 5


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