Midterm 1

• Knowledge representation - processing and representing facts about the world in some abstract way (rules of chess game in IF-THEN logic) • Pattern recognition - extracting knowledge from images (face identification) • Reasoning and inference/prediction - deriving conclusions from incomplete/noisy observations (medical diagnosis, weather forecasting) • machine learning - Improving performance from experience (AlphaGo, google search engine) • 5.Planning - planning complex sequence of actions to achieve a goal (robot navigation) • natural language - production and interpretation of spoken and written language (chatgpt)

the science and engineering of making computer programs that exhibit characteristics of human intelligence

To understand the requirements for and mechanisms of human, animal, machine, robotic intelligence

To apply scientific knowledge to build useful artifacts (machines & robots) capable to do things done by humans or animals

Theory that our perception operates like a species-specific desktop: we perceive the world in representations that do not represent the “truth” about the world as it actually is but rather, are useful “icons” to represent fitness-relevant information about the world. The Psychonomic Society.

a search algorithm that examines all possible paths to a solution without using any additional information about the problem.

The computational part of the ability to solve problems and achieve goals in the world in an efficient manner.

Pattern recognition, knowledge representation, reasoning & inference

At a summer workshop at Dartmouth in 1956 led by John McCarthy

A computer program by Newell and Simon in 1960-70s that solved simple problems/puzzles like the tower of Hanoi. Based on mean-ends analysis (work backward from the goal to decide on what actions will help you achieve in which goals are decomposed into subgoals in a recursive fashion). Influenced development of symbolic logic.

Weak AI view: The computer gives us powerful useful tools to aid human activities but can never exhibit human-level intelligence. Strong AI view: A programmed computer can truly think and can therefore explain how the human mind works, helping us to create an artificial mind. AKA - artificial general intelligence

symbolic systems approach: 1950-2000 traditional AI. CPU. Machine learning starting at 1980. neural network approach: starting after geoffrey hinton breakthrough 2010s. Deep learning & then deep neural networks around 2011. GPU.

Alan Turing. Subsequently led to a definition of intelligence or intelligent agents such as humans, animals, and ultimately, all sorts of “automatic” machines (ATM, cellphone)

Alan Turing

gives a theoretical definition of computation (so intelligence); NOT an actual physical machine; an idealized mathematical abstraction of a digital computer. • 1-dim tape of cells of unlimited length (written on each cell is a symbol from finite alphabet) • read/write head • control (action) table or program (if -> then actions)

CPUs are general-purpose processors that can handle many tasks, while GPUs are specialized processors that excel at parallel processing

Modern computers are VNM. They can accomplish what TM do in a long sequence of steps in just one step.

TM: only has one register, has linear access memory, had only a few hardware instructions, has a control table. VNM: has several dozen registers, has random access (addressable memory), has several dozen instructions in CPU, has a program that controls CPU instead of a control table.

It is turing computable (turing computable functions or problems) if it can be implemented on a turing machine; ie the solution can be found as a “halting” output written on the tape of a turing machine.

A single turing machine that can simulate any other turing machine. Programmable computers (PC, mac, von neumann machine) & DNA (biological computers)

It states that a function on the natural numbers can be calculated by an effective method if and only if it is computable by a UTM

a problem is computable if it can be solved in a finite number of steps by a UTM, and uncomputable if no UTM can solve the problem.

If we found a non-UTM that solves what is thought to be an uncomputable problem.

The halting problem - based programs code and input, will it ever finish running? The word problem for cancellation semigroups