Cybernetics and the First Models
Before the name 'artificial intelligence,' research programs already treated mind and machine as the same kind of problem-logical neurons, feedback control, and operational tests of machine intelligence.
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Before the name 'artificial intelligence,' research programs already treated mind and machine as the same kind of problem-logical neurons, feedback control, and operational tests of machine intelligence.
The August 1955 proposal and the 1956 Dartmouth workshop named and organized a research program under the phrase 'artificial intelligence'-they did not invent the problems surveyed in the previous chapter.
Rosenblatt’s perceptron made learning in a neuron-like device an empirical research program; Minsky and Papert’s 1969 book proved sharp limits of a restricted class of machines-not that ‘neural nets are dead.’
After Dartmouth and alongside the perceptron line, a large part of AI became knowledge plus search-GPS, DENDRAL, MYCIN, and the expert-system boom-while the journalistic label “AI winter” names real but local funding contractions, not a single global morality play.
In the 1980s, distributed representations returned as an empirical research program-Hopfield networks, Boltzmann machines, and the PDP volumes-with new mathematics of energy, attractors, and hidden units, not a magical rebirth after a total death of neural nets.
Backpropagation-efficient reverse-mode differentiation through multilayer nets-made hidden-unit learning a community tool in 1986, after an earlier paper trail in automatic differentiation and ordered derivatives that was not invented out of nowhere that year.
In the 1990s, VC theory, soft-margin support-vector machines, and kernel methods offered a statistical-learning alternative to under-regularized multilayer nets-strong baselines with clearer capacity control, not a permanent replacement for neural networks.
Gradient descent, stochastic approximation, momentum, regularization, and Adam form a cumulative training-methods line-engineering and theory, not a folklore of sudden deep-learning inventions.
Before transformers, language and other sequences became trainable transduction problems through simple recurrent nets, LSTM, encoder–decoder seq2seq, and Bahdanau’s soft alignment-attention as an RNN add-on, not a 2017 invention.
What moved in 2012 was not a sudden invention of deep nets: ImageNet-scale labeled data, GPU training, ReLU, and dropout made a large ConvNet win a public, comparable benchmark-AlexNet as the ImageNet moment, not the first deep network.
Supervision is scarce: the modern stack learns reusable representations from large unlabeled or weakly labeled data-layerwise pretraining, ImageNet transfer, word2vec, BERT-style masked LMs, and contrastive vision-then adapts them, without collapsing the lineage into a BERT founding myth.
The 2017 transformer reorganized sequence modeling around self-attention without recurrence; GPT and BERT are two objectives on that backbone; Kaplan and Chinchilla scaling laws are empirical regularities about compute, data, and parameters-not a slogan that bigger is always better.