Symbolic artificial intelligence: Difference between revisions

Revision as of 05:39, 2 July 2024

Revision as of 08:57, 2 July 2024

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[[Artificial neural network|Neural networks]], a subsymbolic approach, had been pursued from early days and reemerged strongly in 2012. Early examples are [[Frank Rosenblatt|Rosenblatt]]'s [[perceptron]] learning work, the [[backpropagation]] work of Rumelhart, Hinton and Williams,<ref>{{cite journal| doi = 10.1038/323533a0| issn = 1476-4687| volume = 323| issue = 6088| pages = 533–536| last1 = Rumelhart| first1 = David E.| last2 = Hinton| first2 = Geoffrey E.| last3 = Williams| first3 = Ronald J.| title = Learning representations by back-propagating errors| journal = Nature| date = 1986 | bibcode = 1986Natur.323..533R| s2cid = 205001834}}</ref> and work in [[convolutional neural network]]s by LeCun et al. in 1989.<ref>{{Cite journal| volume = 1| issue = 4| pages = 541–551| last1 = LeCun| first1 = Y.| last2 = Boser| first2 = B.| last3 = Denker| first3 = I.| last4 = Henderson| first4 = D.| last5 = Howard| first5 = R.| last6 = Hubbard| first6 = W.| last7 = Tackel| first7 = L.| title = Backpropagation Applied to Handwritten Zip Code Recognition| journal = Neural Computation| date = 1989| doi = 10.1162/neco.1989.1.4.541| s2cid = 41312633}}</ref> However, neural networks were not viewed as successful until about 2012: "Until Big Data became commonplace, the general consensus in the Al community was that the so-called neural-network approach was hopeless. Systems just didn't work that well, compared to other methods. ... A revolution came in 2012, when a number of people, including a team of researchers working with Hinton, worked out a way to use the power of [[GPUs]] to enormously increase the power of neural networks."{{sfn|Marcus |Davis|2019}} Over the next several years, [[deep learning]] had spectacular success in handling vision, [[speech recognition]], speech synthesis, image generation, and machine translation. However, since 2020, as inherent difficulties with bias, explanation, comprehensibility, and robustness became more apparent with deep learning approaches; an increasing number of AI researchers have called for [[Neuro-symbolic AI|combining]] the best of both the symbolic and neural network approaches<ref name="Rossi">

{{cite web |last1=Rossi |first1=Francesca |title=Thinking Fast and Slow in AI |url=https://aaai-2022.virtualchair.net/plenary_13.html |publisher=AAAI |access-date=5 July 2022}}</ref><ref name="Selman"ss: The State of AI |url=https://aaai-2022.virtualchair.net/plenary_2.html |publisher=AAAI |access-date=5 July 2022}}>

{{cite web |last1=Selman |first1=Bart |title=AAAI Presidential Address: The State of AI |url=https://aaai-2022.virtualchair.net/plenary_2.html |publisher=AAAI |access-date=5 July 2022}}</ref> and addressing areas that both approaches have difficulty with, such as [[Commonsense reasoning|common-sense reasoning]].{{sfn|Marcus |Davis|2019}}

== History ==