4 ms·
> LeCun explains that there is a need to develop new forms of AI systems “that would allow those systems to, first of all, understand the physical world, which
by abeppu 3y ago
> LeCun explains that there is a need to develop new forms of AI systems “that would allow those systems to, first of all, understand the physical world, which they can’t do at the moment. Remember, which they can’t do at the moment. Reason and plan, which they can’t do at the moment either.
I think LLMs have sucked most people's focus away from other areas but there is plenty of work on types of models that plan and have their own internal model of the physical world, and physical interactions. They're just not the things getting media attention, in part because they're not human-level at tasks that seem impressive to us.
But interesting frameworks for this stuff exist:
- model-based RL exists, and is about planning, and having an internal model of state transitions, in the world and between the agent's actions and the world
- "Bayesian cognitive science" as exemplified by Josh Tenenbaum and colleagues has done plenty of stuff with systems that include physics models (or off-the-shelf physics engines) to make counter-factual predictions
- The somewhat related "active inference" research literature is also in the "Bayesian brain" area, and has world/generative-models and planning as core components, but wrapped up with ideas about the agent's own preferred distribution of states.
To my knowledge, none of these have ever had even 1% the scope of data and computation that LLMs have had, and never benefited the co-evolution of a rich, optimized software ecosystem with specialized hardware to support it. What if the concepts are already there, but they just need to be scaled up?
- kromem 3y agoAnd perhaps even more pertinent - maybe the problem isn't that we don't have the solutions, but that we are trying to put all eggs in one basket. The brain isn't a single system, it's several specialized systems cooperating as a whole. Maybe no single solution gets us to AGI, but layering several of them together gets us much closer than any individual system alone. The problem here is that the industry is set up more for large resources dedicated competition between models from different companies more than cooperative interoperation, so it may take a while to arrive here.
- abeppu 3y agoThat's an interesting possibility. I think where one stands on this partly depends on how much about the technical approaches to AGI we can learn from the brain. The brain does seem to have a weird mix of specialization and flexible generalization -- e.g. we generally have a bunch of the same specialized areas for various functions -- but also, neuroplasticity seems to show that other areas can often pick up those functions if necessary (the most extreme cases being after something like a hemispherectomy). So we have specialized areas but also at least some of the underlying mechanisms are perhaps relatively general.