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Me watching this video makes me think i'll see full, conscious hard AI in my lifetime. You can never really point on the evolutionary timelines and say, 'That'

Me watching this video makes me think i'll see full, conscious hard AI in my lifetime. You can never really point on the evolutionary timelines and say, 'That's it! that's when consciousness arose. It's obviously going to be the same with AI. When AI rises to the point where it is obviously conscious, there's never going to be one key technological breakthrough that put it over the edge. Like the fall of rome or anything complex really, it will be a collection of things and a backwards pointing measure of when it actually occurred. Jeff hawkins has some extremely interesting thoughts on AI. His book 'On intelligence' is one of the best books i've ever read. These aren't entirely his thoughts butThree things; some ability to move around your world (e.g. cyberspace, robots), perceive the world (Vision, data,dumps, training data, supervised learning, deep learning) Some mechanism with interacting with the world based on motives and desires. What happens if a non-sudo'd trading bot, with a wallet starts trading on the blockchain? Seed it with capital, have it access some smart contract, and delete sudo on both the smart contract and wallet. it's not doing anything too 'sophisticated' but it is a) able to 'Die' by it's wallets balance to hit zero. b.) has a 'motive' to increase it's total coins. pre-programmed, yes but also is ingesting data from oracles and acting on that information.

andyxor 5y ago
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".
- ChrisClark 5y ago
  Why not? This is NOT my area, and I'm most likely VERY wrong. But we get input from our eyes, it goes through all the layers of the brain and comes out as a word in speech. Could that not be how our mind evolved before getting this 'consciousness layer' on top of it? Maybe building on this adding more and more abstract layers, training them to return useful info, could nudge things in the right way?
junon 5y ago
Probably not. NNs right now are just glorified pattern matching/curve fitting. Getting to a generalized AI is some serious "draw the rest of the owl" work.
CodeGlitch 5y ago
Someone correct me if this is not true, but the current AIs using NNs are "static", in that the model is created offline using tonnes of data, and then at runtime it's simply doing pattern matching / prediction. There's no "plasticity" as seen in human-like intelligence. It seems to me that we need a slightly different approach that will change the NNs during runtime.

andyxor 5y ago

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".

ChrisClark 5y ago
Why not? This is NOT my area, and I'm most likely VERY wrong. But we get input from our eyes, it goes through all the layers of the brain and comes out as a word in speech. Could that not be how our mind evolved before getting this 'consciousness layer' on top of it? Maybe building on this adding more and more abstract layers, training them to return useful info, could nudge things in the right way?

junon 5y ago

Probably not. NNs right now are just glorified pattern matching/curve fitting. Getting to a generalized AI is some serious "draw the rest of the owl" work.

CodeGlitch 5y ago

Someone correct me if this is not true, but the current AIs using NNs are "static", in that the model is created offline using tonnes of data, and then at runtime it's simply doing pattern matching / prediction. There's no "plasticity" as seen in human-like intelligence. It seems to me that we need a slightly different approach that will change the NNs during runtime.