One of the most consistent theory about how our brain learns is described in HTM (Hierarchical Temporal Memory), a more biologically inspired neural network. See Jeff Hawkins' "On Intelligence".
It is based on:
* Input of continuous unlabeled time-based patterns.
* Associative Hebbian Learning (when distinct inputs/patterns come together, they are neuron-wired together). Synapses can be modified via experience. See "Hebbian Theory".
* The brain is a prediction machine: it is always trying to predict the future based on past learned patterns. Learning happens when reality does not match the originally prediction and we rewire the world model based on new input. See "Bayesian approaches to brain function".
* Input signals are processed by many layers, each one creating more abstraction from the previous one, from sensory neurons to the highest cortex layers.
* Each region of the hierarchy forms invariant memories (what a typical region of cortex learns is sequences of invariant representations).
* There is lots of feedback (highest level neurons back to the lowest levels). In some structures (e.g. the thalamus, that is a kind of "hub of information") connections going backward (toward the input) exceed the connections going forward by almost a factor of ten.
* Brain uses Sparse Distributed Memory (SDM). See SDM by Pentti Kanerva (NASA researcher).
* Neuron models have many more variable/parameters (that can be used to transfer or process information) than usual nodes/links from artificial neural networks. E.g.: Long-term potentiation vs Long-term depression, neuronal Habituation vs Sensitization, inhibitory vs excitatory neurons, firing rates, synchronization, neuromodulation, homeostasis and more.
* The backward propagation of errors in artificial neural networks only occurs during the learning phase. But the brain is always learning and updating weights and relationships between patterns, given new inputs.
* During repetitive learning, representations of objects move down the cortical hierarchy (from short-term memory to long-term memory), forming invariant memories.
* The brain needs to replay the memory (memory rehearsal) of a learned stimulus so it can be stored in long-term memory.
* The job of any cortical region is to find out how inputs are related (pattern recognition), to memorize the sequence of correlations between them, and to use this memory to predict how the inputs will behave in the future.
* Predictive coding: the brain is constantly generating and updating hypotheses that predict sensory input at varying levels of abstraction.
Jeff Hawkins is kind of a crank when it comes to neuroscience, and his AI companies have tended not to publish state-of-the-art results on machine learning problems either.
(Replying here for visibility.) In a different comment branch you mentioned counterfactuals. I've watched a video lecture about counterfactuals in graphical models by Pearl, but I'm not exactly seeing the significance as a "missing piece" in AI. Would you mind explaining a bit what you exactly mean?
Do counterfactuals have something to do with learning from negative examples and simulations? For example, if one shoots a ball and misses the goal to the right, one does not 'mindlessly' penalize the circuits that led to the exact motor decisions that were involved, but instead, one simulates alternative actions and uses e.g. (in this case linear) relationships between e.g. the angle of the foot or the wind speed and the shooting direction. The next time, one hence tries to aim slightly to the left.
Or are you referring to a much more fundamental level and my example might rather be a learning strategy that is more likely acquired by trial & error, reinforcement learning, meta learning ("learning how to learn") and/or via the shared concept space of language and culture?
Is it maybe related to e.g. prototype-based associative recall and a counterfactual is basically an alternative way of interpreting the data? "What error signal would I get, if I had interpreted X as Y?"
Or does it come from the Bayesian approach where you marginalize out all hypotheses, including the factual one that corresponds to the state of the world, but also all counterfactual hypotheses. So, including counterfactuals means going beyond the maximum likelihood point estimate e.g. by communicating confidence intervals or even entire distributions from neurons to neurons or neuron populations to other neuron populations?
Counterfactuals in Pearl's sense are what allow particular models to be causal: to represent cause and effect under intervention, as opposed to mere correlation. This is an important part of how to build models that think like people[1].
Is it in particular the dot product (correlation) in MLPs that prevents them from inferring all causal structures in the data? So, instead of template matching of co-occurrences of features in the layer below, we (also) need to learn whether and how one feature causes the other?
Again, it's the lack of counterfactuals: the ability to intervene on a node and cut it off from its parents, then see what happens, and the ability to perform inferences over discrete spaces.
Are there any concrete attempts at transferring this concept to MLPs? E.g. by overriding the values of particular nodes/features by feedback connections?
No, because neural nets do not work that way, even when they output actions. Making things More Neural doesn't make them better, and AFAIK, not everything good can be made More Neural.
Are there works that expose this limitation of MLPs more formally?
>not everything good can be made More Neural.
Neural networks are universal function approximators, so you probably mean not everything good can be made with MLPs trained by gradient descent?
>It's the lack of [...] the ability to perform inferences over discrete spaces.
How would you judge the extent to which AlphaGo has learned to react to single discrete changes in the input. It seems that it learned very well to react very sharply to whether a single stone is placed at a strategically significant position.
* Input of continuous unlabeled time-based patterns.
* Associative Hebbian Learning (when distinct inputs/patterns come together, they are neuron-wired together). Synapses can be modified via experience. See "Hebbian Theory".
* The brain is a prediction machine: it is always trying to predict the future based on past learned patterns. Learning happens when reality does not match the originally prediction and we rewire the world model based on new input. See "Bayesian approaches to brain function".
* Input signals are processed by many layers, each one creating more abstraction from the previous one, from sensory neurons to the highest cortex layers.
* Each region of the hierarchy forms invariant memories (what a typical region of cortex learns is sequences of invariant representations).
* There is lots of feedback (highest level neurons back to the lowest levels). In some structures (e.g. the thalamus, that is a kind of "hub of information") connections going backward (toward the input) exceed the connections going forward by almost a factor of ten.
* Brain uses Sparse Distributed Memory (SDM). See SDM by Pentti Kanerva (NASA researcher).
* Neuron models have many more variable/parameters (that can be used to transfer or process information) than usual nodes/links from artificial neural networks. E.g.: Long-term potentiation vs Long-term depression, neuronal Habituation vs Sensitization, inhibitory vs excitatory neurons, firing rates, synchronization, neuromodulation, homeostasis and more.
* The backward propagation of errors in artificial neural networks only occurs during the learning phase. But the brain is always learning and updating weights and relationships between patterns, given new inputs.
* During repetitive learning, representations of objects move down the cortical hierarchy (from short-term memory to long-term memory), forming invariant memories.
* The brain needs to replay the memory (memory rehearsal) of a learned stimulus so it can be stored in long-term memory.
* The job of any cortical region is to find out how inputs are related (pattern recognition), to memorize the sequence of correlations between them, and to use this memory to predict how the inputs will behave in the future.
* Predictive coding: the brain is constantly generating and updating hypotheses that predict sensory input at varying levels of abstraction.