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AsheyDS t1_ja3zkxk wrote

Reply to comment by Lesterpaintstheworld in Raising AGIs - Human exposure by Lesterpaintstheworld

>What alternatives do you have from LLMs?

I don't personally have an alternative for you, but I would steer away from just ML and more towards a symbolic/neurosymbolic approach. LLMs are fine for now if you're just trying to throw something together, but they shouldn't be your final solution. As you layer together more processes to increase its capabilities, you'll probably start to view the LLM as more and more of a bottleneck, or even a dead-end.

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Lesterpaintstheworld OP t1_ja402r0 wrote

One of the difficulties have been sewing together different types of data (text & images, other percepts, or even lower levels). I wonder what approaches could be relevant

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AsheyDS t1_ja46bxe wrote

I'm not sure if you can find anything useful looking into DeepMind's Gato, which is 'multi-modal' and what some might consider 'Broad AI'. But the problem with that and what you're running into is that there's no easy way to train it, and you'll still have issues with things like transfer learning. That's why we haven't reached AGI yet, we need a method for generalization. Looking at humans, we can easily compare one unrelated thing to another, because we can recognize one or more similarities. Those similarities are what we need to look for in everything, and find a root link that we can use as a basis for a generalization method (patterns and shapes in the data perhaps). It shouldn't be that hard for us to figure out, since we're limited by the types of data that can be input (through our senses) and what we can output (mostly just vocalizations, and both fine and gross motor control). The only thing that makes it more complex is how we combine those things into new structures. So I would stay more focused on the basics of I/O to figure out generalization.

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