1. C HAPTER 8. N EURAL N ETWORKS : T HE N EW C ONNECTIONISM Bodrov Alexey

2. O UTLINE 1. Computer Simulation and Artificial Intelligence 2. The Computer and the Brain 3. Symbolic and Connectionist models 4. Neural Networks 2

3. 1. C OMPUTER S IMULATION AND A RTIFICIAL I NTELLIGENCE 1.1 B ASIC CONCEPTS Artificial intelligence (AI) – field, where scientists try to device computer systems that could accomplish the same things as humans (it is a branch of computer science that tries to make computers smarter). Computer simulation – attempt to mimic the functions of the human (including errors and biases). Reasons for making computer smarter: Computer might do marvelous things for human (for ex. assembling machine parts) This may clarify questions about human cognitive process and mimic functions of a mind. 3

4. 1.2 C AN MACHINES THINK ? Sternberg and Ben-Zeev: “Computers cannot think, although they can sometimes be programmed to respond as if they were thinking”. Turing test’s scheme: If A can do x, y, and z, and B can do x, y, and z exactly, then B must possess whatever attributes A has that allow it to do x, y, and z. Conclusion: Machines can think. Searl shown: Turing’s test reduces easily to absurd. Conclusion: There is no answer whether machine can think or not. Modern view: Computers don’t need to think. Their lack of “passion” does underscore that hey are not human. 4

5. 2. T HE C OMPUTER AND THE B RAIN (1) Basis computer-as-cognitive-processor metaphor: Opponents of this metaphor claim, that viewing humans as machines robs them of the most important aspects of humanity (machines have no emotion and no volition). Penner point out that metaphors are just comparisons and we need only accept that computers and humans sufficiently similar that some features of one can be used as a sort of pattern for other. 5 Information processing

6. 2. T HE C OMPUTER AND THE B RAIN (2) Differences between brains and computers: o Brains are very slow; computers are lightning fast o Brains are smarter and storage of information is virtually unlimited o Human nervous system is incredibly more complex than even the largest and most sophisticated of modern computers o The computer’s ability to retrieve flawlessly from memory and to perform arithmetical computations rapidly and accurately far exceeds that of humans Conclusion: Brain is more like a parallel distributed processing (PDP) computer. 6

7. 3. S YMBOLIC AND C ONNECTIONIST MODELS 3.1 S YMBOLIC MODELS Basic assumptions: o all information can be represented in symbols o learning is explicit o information processing (thinking) involves the application of identifiable rules Historical illustration of this model: Newell, Shaw, and Simon Logic Theorist, which is capable of finding proofs for theorems in symbolic logic. General Problem Solver (GPS) – is designed to allow comparison between the desired end state and the current state. The only thing it has revealed is that far more flexible than GPS. SOAR – a model of cognitive architecture. Similar to chess programs. Modern chess programs: Use brute force, coupled with few key strategies Different “thinking procedures” with people. 7

8. 3.2 C ONNECTIONISTS MODELS Not all learning is explicit (as symbolic model claims), for example skill in darts. Cognition occurs in the brain not as a series of process but more as patterns of activation (Hebb). Assumptions of a model: brain’s collection of neurons is like the processing units in a PDP no central organizer or processor governs activities of neurons Neural Network – connectionists model of “mind”. 8

9. 4. N EURAL N ETWORKS 4.1 I NTRODUCTION “A neural network is a general mathematical computing paradigm that models the operations of biological neural systems” Learning of neural network: New connections might develop. Old connections might be lost. Probability that one unit will activate another might change. In cognitive research, neural networks are not physical arrangements of actual networks of neurons !!! 9

10. 4.2 I LLUSTRATION : NET TALK Task: Learn to read text. Machine learn itself by using back-propagation rule (uses information about the correctness or appropriateness of its responses to change itself so that the response might be more correct or more appropriate). Result: NETtalk could read not only studied words, but texts it had never seen. It learned rules, exceptions, had learned to generalize and made some sorts of errors that children always make. 10

11. 4.3 A DVANTAGES OF CONNECTIONISTS MODELS Neural network can make inferences without being given specific rules for so doing. This models allow for a fuzzier kind of logic (more peculiar to human). More accurately reflect the actual physiological structure of human nervous system. Present a functional analogy for the notion that experience alters the brain’s wiring. The applications of neural networks stretch well beyond the cognitive sciences and psychology. 11

12. 4.4 C RITICISM OF CONNECTIONISTS MODELS Computers don’t simulate human emotions at all. Computer simulations don’t reveal the insight of which human solvers are capable. Tell very little about how the human nervous system works (the successful functioning of connectionist models depends on certain properties of their units that are not properties of the human nervous system). Too big interference (second test of paired-associate learning). 12

13. Q UESTIONS Can machine think or not? What is the main difference between symbolic and connectionist models? What are main advantages and disadvantages of artificial neural networks? 13

14. T HANK Y OU 14