there is only one problem, most of these "AI" algorithms don't have an actual understanding of what's going on. These visual pattern matchers can't generalize b

there is only one problem, most of these "AI" algorithms don't have an actual understanding of what's going on. These visual pattern matchers can't generalize beyond very narrow meaning of the word. You can train it to associate certain patterns with certain labels or responses, but it's basically a mechanical puppet that always needs a puppeteer to "train" it. As long as the prevalent view of AI is based on machine learning, using some form of optimization to match patterns, it has no hope of reaching AGI. It's fundamentally backwards looking and bigger models won't make any less "dumb", for a lack of better word. DL can probably represent lower levels of sensory processing, akin to dimensionality reduction in visual cortex, but it can't generate new abstract concepts from lower level ones, form clusters of such concepts and facilitate navigation in "concept space", in other words go from low level associations to "understanding". In natural cognition learning & memory are highly intertwined, and acquisition of basic knowledge is not based on some teaching signal or "delta rule" reinforcement, it's more akin to self-organizing hierarchical networks operating via very limited set of computational primitives such as JOIN and LINK, temporal synchronization and innate system of coordinates in abstract concept spaces via something like grid cells https://en.wikipedia.org/wiki/Grid_cell https://en.wikipedia.org/wiki/Grid_cell The constraints nature puts on living organisms, such as limited energy and time define the computational architecture of cognition. As long as these constraints are ignored AI will always be "5 years away".