>For comparison, neural nets cannot update their models - when the world changes, a neural net can only train its model all over again, from scratch
I mean, sure they can. Training a neural network is literally nothing but the network's model being updated one batch of training examples at a time. You can stop, restart, extend or change the data at any point in the process. There's whole fields of transfer learning and online learning which extend that to updating a trained model with new data.
edit: Also in a way reinforcement learning where the model controls the future data it sees and updates itself on.
The problem I'm describing is formally known as "catastrophic forgetting".
Quoting from wikipedia:
Catastrophic interference, also known as catastrophic forgetting, is the
tendency of an artificial neural network to completely and abruptly forget
previously learned information upon learning new information.
Of course neural nets can update their weights as they are trained, but the
problem is that weight updates are destructive: the new weights replace the old
weights and the old state of the network cannot be recalled.
Transfer learning, online learning and (deep) reinforcement learning are as
susceptible to this problem as any neural network techniques.
This is a widely recognised limitation of neural network systems, old and new,
and overcomging it is an active area of research. Many approaches have been
proposed over the years but it remains an open problem.
I mean, sure they can. Training a neural network is literally nothing but the network's model being updated one batch of training examples at a time. You can stop, restart, extend or change the data at any point in the process. There's whole fields of transfer learning and online learning which extend that to updating a trained model with new data.
edit: Also in a way reinforcement learning where the model controls the future data it sees and updates itself on.