  One physicalist response to these challenges is to say that they apply only to the classical computing model, and are avoided by connectionist theories. Classical computing works on rules of inference like those of standard logic, but connectionism is rather a form of associationism, which is supposedly closer to the way in which the brain works.(See the entry on Connectionism.) The latest version of this – ‘Deep Learning’ – is very good at face recognition and, in general, pattern recognition, including those involved in the playing of games like chess and Go. But Gary Marcus (2018 – see Other Internet Resources) and others have pointed out the ways in which these impressive machines are quite different from human thought. Marcus points to ten significant differences, but the two most easy to capture briefly are (i) ‘deep learning’ is data hungry. We can learn things with very few trials because we latch on to abstract relationships, whereas the machine requires many – perhaps thousands or millions – of examples to try to catch extensionally what we get by the abstract or intensional relation. (ii) Deep learning is shallow and has a limited capacity for transfer of what it has learnt from one context to another, even though the differences look trivial to us.  It seems that collecting examples cannot itself constitute ‘getting the point’, though it might manage to mimic it, if the circumstances are carefully managed.