- 1st ai boom
- past AI were good at X domain, but didn’t prove success to extent to wider tasks
- to overcome combinatorial explosion, an algorithm that exploit structure in the target domain & take advantage of prior knowledge by using heuristic search, planning & flexible abstract representations were needed (poorly developed at that time)
- other problems
- poor methods of handling uncertainty
- reliance on ungrounded symbolic representation
- data scarcity
- severe hardware limitations
- 2nd boom
- includes neural networks & genetic algorithms
- NNs
- a damage resulted in small degradation of performance instead of a total crash
- learn from experience
- backprop algorithm
- connectionism → emphasized the importance of massively parallel sub-symbolic processing
- genetic algorithms/programming
- in evolutionary models, a population of candidate solutions (which can be data structures or programs) is maintained, and new candidate solutions are generated randomly by mutating or recombining variants in the existing population
- periodically, population is pruned by applying a selection criterion that allows only the better candidates survive
- iterated over 1000s generations → the avg quality of solutions in the candidate pool gradually increases
- when it works, it can produce solutions strikingly novel & unintuitive
- in principle, this can happen without much human input (the criteria)
- but often defeated by the combinatorial explosion