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AsheyDS t1_ja3kmyz wrote

Addressing your problems individually...

Bad Learning: This is a problem of bad data. So it either needs to be able to identify and discard bad data as you define it, or you need to go through the data as it learns it and make sure it understands what is good data and bad data, so it can gradually build up recognition for these things. Another way might be AI-mediated manual data input. I don't know how the memory in your system works, but if data can be manually input, then it's a matter of formatting the data to work with the memory. If you can design a second AI (or perhaps even just a program) to format data input into it so it is compatible with your memory schema, then you can perhaps automate the process. But that's just adding more steps in-between for safety. How you train it and what you train it on is more of a personal decision though.

Data Privacy: You won't get that if it's doing any remote calls that include your data. Keeping it all local is the best you can do. Any time anyone has access to it, that data is vulnerable. If it can learn to selectively divulge information, that's fine, but if the data is human-readable then it can be accessed one way or another, and extracted.

Costs: Again, you'll probably need to keep it local. LLM isn't the best way to go in my opinion, but if you intend on sticking with it, you'll want something lightweight. I think Meta is coming out with a LLM that can run on a single GPU, so I'd maybe look into that or something similar. That could potentially solve or partially solve two of your issues.

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Lesterpaintstheworld OP t1_ja3rt9d wrote

Thanks for the answers. What alternatives do you have from LLMs? The single GPU is interesting Indeed, it would allow me to let it run 24/7

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AsheyDS t1_ja3zkxk wrote

>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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