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Comment by lelag | Hacker News Reader

It's a really cool demo and I'm impressed by the dexterity of the robot, however I'm a bit underwhelm by what's shown here in the sense that the speech and reasoning capabilities is just obvious to anyone who's been paying attention and has experience with GPT4. The function calling was great, but it had a very simple "world" to interact with.

It's really interesting to see it integrated with a robot that can interact with the world though. I think that what's really holding back the current crop of Gen AI is inference cost and speed. When we figure out to get thousands of token per seconds for cheap, I think we will be able to bruteforce many hard problems and actually start seeing amazing applications like this one (but in production rather than a cool demo).

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I take your point about familiarity with GPT4 making this less immediately impressive - but just as an end to end demo it's absolutely mind-blowing how far we've come.

Can you imagine seeing this ten years ago? Moving so far in such a short time frame would have been unbelievable.

This gave me the "I'm actually living in the future" vibes that I always imagined I'd get from flying cars.

Yea of course this is just an LLM interacting through a very crude interface with some control algorithms, but I think it is amazing that we sort of have an approach for both ends of the complexity spectrum now: LLMs for high level, vague, common sense reasoning, and traditional robot control, planning, machine learning methods for the physicak execution of simple movements. We just have to gradually connect these two systems.

I think the big deal here is that it's an end to end system.

It's very easy to forget the single line hallucinations of GPT3 not too long ago.

Look into providers like Groq that are running real-time inference with Transformer models.

It's only a matter of models improving then the hardware coming to market that this will be a major leap in robotic capabilities.

I think it's always going to be the case that we'll get to AGI through a collection of neural networks and symbolic logic systems and not through just a single neural network. For some reason a lot of people think that's "cheating", though.

If this thing can clean my kitchen I’ll let it cheat all day.

The way I see it, a symbolic logic system is not an integral part to an AGI, but rather a tool that it can use the same way humans use tools.

Tools can be useful for many things, but tend to be inflexible compared to neural nets.

The way I see it, the main benefit of such tools come when the part covered by the tool is very repetitive and well defined. In such cases, algorithmic solutions may offer better precision and lower power consumption than neural nets.

Anyway, once most of these algorithms are actually WRITTEN by AGI's, the difference may cease to matter.

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