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Comment by drillsteps5 | Hacker News Reader

Someone should just build an ANN as big as currently as possible with current hardware, while still having both inference and training to be as close to real-time as possible (micro-to milli-seconds), build the self-learning using some loose equivalents of pain/pleasure feedback in actual brains, plug sensors and actuators from some sort of robot, and just see what happens.

I think anything less than that is just a parlor trick.

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That's still not enough. The biggest architectural hurdle to actualised artificial intelligence is the feed forward model. Unlike AI, all animals take in various inputs and produce various outputs asynchronously. That means the feed forward model of all NNs are fundamentally limited. Even recurrent NNs can't overcome this since they need a new input every iteration.

Makes sense.

The counterpoint would be that when they started to build LLMs they must have clearly seen limitations of the approach and proceeded regardless, and achieved quite a bit. So the approach to introduce continuous (in-vivo if you will) self-guided training AND multiple sensors and actuators would still be limited but might yield some interesting results nevertheless.

Taking the biological approach to 11 there?

ANN is an attempted model/ripoff (turned out to be extremely simplified but still) of a brain, why not go further? Continuous autonomous learning (which requires continuous feedback in a way of good/bad stimuli) is clearly what makes it work.

The current approach of guided pre-training and inference on essentially a "dead brain" clearly causes limitations.

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