Why do members here think DL-based methods can't achieve AGI?

[dmac]

There are four categories of AI: supervised, unsupervised, reinforcement, and heuristics/hard-coded. The brain does all of them and so it stands to reason that AGI will require all four as well.

• The cerebellum does supervised learning.
• The cortex does unsupervised learning.
• The basal ganglia does reinforcement learning.
• The brainstem has hardcoded & heuristic knowledge, which was learned through evolution.

Deep learning is based on a technique called “error backpropagation”. There is evidence that backpropagation does happen in the cerebellum, however the cerebellum is a few layers deep, not the 50+ layers that deep learning uses. The layers of the real cerebellum do not repeat / they are not stacked. Furthermore, there is scant evidence that backpropagation is happeneing anywhere else in the brain.

Edit: for more see

Types of Learning

According Russell and Norvig there are three categories of learning algorithms, distinguished by the type of feedback the algorithm receives. Unsupervised learning algorithms receive no feedback about their performance. Supervised learning algorithms receive the “correct” output to learn from. Reinforcement learning algorithms receive a series of rewards and punishments in response to their performance. Evolutionary learning algorithms don’t receive any explicit feedback, they either survi…