Earlier quoted context omitted.
For now. Give it a truly persistent memory and 100x the size of the dataset I think most people would change their tune.
> For now. Give it a truly persistent memory and 100x the size of the dataset I think most people would change their tune. Why does it need 100x the dataset? Sentient creatures, including humans, manage to figure stuff out from as little as a single datapoint . For a human to differentiate between a cat and a dog takes, maybe, two examples of each, not a few million pictures. An adult human who sees a hotdog for the…
"How much data do you need to show a neural net to train it for a particular task? Again, it’s hard to estimate from first principles. Certainly the requirements can be dramatically reduced by using “transfer learning” to “transfer in” things like lists of important features that have already been learned in another network. But generally neural nets need to “see a lot of examples” to train well. And at least for some tasks it’s an important piece of neural net lore that the examples can be incredibly repetitive. And indeed it’s a standard strategy to just show a neural net all the examples one has, over and over again. In each of these “training rounds” (or “epochs”) the neural net will be in at least a slightly different state, and somehow “reminding it” of a particular example is useful in getting it to “remember that example”. (And, yes, perhaps this is analogous to the usefulness of repetition in human memorization.)"