stagnant artificial intelligence

Transcription

1 stagnant artificial intelligence many believe artificial intelligence is alive and well and just waiting for enough computing power to deliver on its many promises. what is needed is more memory and more speed artificial intelligence suffers from a fundamental flaw in that it fails to adequately address what intelligence is, or what it means to understand something.. a brief look at history will explain how the field has gone off course. [Jeff Hawkins, 2004]

2 the universal computer Alan Turing invented the concept of the universal computer. the computer had three parts; a processing box, tape memory, and a device that reads and writes to the memory tape. Turing mathematically proved, with such a device, you can perform any definable operation in the universe.

3 Turing s test Turing believed computers could be intelligent, but did not want to get into arguments about whether this was possible or not. instead, he proposed an existence proof for intelligence. if a computer can fool a human interrogator into thinking that it too is a person, then by definition the computer must be intelligent.

4 beginning premise with the central dogma: the brain is just another kind of computer Turing helped launch artificial intelligence: it doesn t matter how you design an artificially intelligent system, it just has to produce humanlike behavior early artificial intelligence proponents said: the most impressive feats of human intelligence involve the manipulation of abstract symbols that s what computers do too.. Turing shows it does not matter how you represent and manipulate the symbols, cogs, switches, or neurons, the medium should be able to realize the functionality of the universal computer

5 ai successes? in 1943, Warren McCulloch and Walter Pitts described how simulated neurons could perform digital functions: neurons could implement simple logic operation. even though a computer is a collection of logic gates, it is unclear whether anyone asked " is this really how the brain works?" computer program Eliza, initially passed the Turing Test, but was a computer program designed to fool people. it mimicked a psychoanalyst and would rephrase your own questions back to you.

6 blocks world Blocks World or SHRDLU : did basic natural language processing by understanding and manipulating blocks in a simulated environment. dialog was limited to very simple environments. was not scalable.

7 Deep Blue IBM s Deep Blue beat the world chess champion Gary Kasparov. not by being smarter, but by being faster. Deep Blue simply searched through possible moves no learning, no intuition, no true intelligence.

8 expert systems databases of facts that could answer questions posed by human users within an area of expertise for example diagnose a patients disease given a list of symptoms. found to have limited use because it lacked general intelligence.

9 unfulfilled promise after many years of effort, artificial intelligence started to lose its luster. all of the technologies model the appearance of some part of intelligence, but not the underlying processes some still believe the problems can be solved with faster computers in 1980, influential philosophy professor John Searle at the University of California Berkeley said, computers were not, and could not, be intelligent. to prove it he came up with a thought experiment called the chinese room.

10 chinese room you have a closed room with a slot in one wall. inside the room is an english speaking person sitting at a desk with paper and pencil. at the desk, the person has a book of rules. flipping through the book reveals instructions - written in english - about ways to copy, erase, reorder and transcribe chinese characters. the rules say nothing about the meaning of the chinese characters, only how to manipulate them.

11 chinese room in this room, where does understanding occur?

12

13 understanding understanding Hawkins believes Searle had it right, thinking about how computers worked, he didn t see understanding happening anywhere in this model. what was needed was an understanding of what understanding was; a way to define it that would make it clear when a system was intelligent, and when it was not its behavior alone does not tell us this understanding cannot be measured by external behavior, it is an internal metric of how the brain remembers things, and uses those memories to make predictions. (most) computer programs do not do this.

14 conclusion you cannot simulate a brain without first understanding what it does.