We are miles away from the fundamental constraint. We know that our current training methodologies are scandalously data inefficient compared to human/animal brains. Augmenting observations with dreams has long been theorized to be (part of) the answer.

I might be misunderstanding your comment so sorry if so. Robots have sensors and RL is a thing, they can collect real world data and then processing and consolidating real world experiences during downtime (or in real time), running simulations to prepare for scenarios, and updating models based on the day's collected data. The way I saw it that I thought was impressive was the robot understood the scene, but didn't know how the scene would respond to it's actions, so it gens videos of the possible scenarios, and then picks the best ones and models it's actuation based on it's "imagination".

It's feasible you could have a personal neural net that fine-tunes itself overnight to make less inference mistakes in the future.

Any idea how humans do it? Where do they get novel information from?

I actually think you can.

The LLM has plenty of experts and approaches etc.

Give it tool access let it formulate it's own experiments etc.

The only question here is if it becomes a / the singularity because of this, gets stuck in some local minimum or achieves random perfection and random local minimum locations.

Humans are dependent on their input data (through lifetime learning and, perhaps, information encoded in the brain from evolution), and yet they can produce out of distribution information. How?

There is an uncountably large number of models that perfectly replicate the data they're trained on; some generalize out of distribution much better. Something like dreaming might be a form of regularization: experimenting with simpler structures that perform equally well on training data but generalize better (e.g. by discovering simple algorithms that reproduce the data equally well as pure memorization but require simpler neural circuits than the memorizing circuits).

Once you have those better generalizing circuits, you can generate data that not only matches the input data in quality but potentially exceeds it, if the priors built into the learning algorithm match the real world.

Humans can learn from visualising situations and thinking through different scenarios. I don't see why AI / robots can't do similar. In fact I think quite a lot of training for things like Tesla self driving is done in simulation.

This is definitely one of the potential issues that might happen to embodied agents/robots/bodies trained on the "world model". As we are training a model for the real world based on a model that simulates the real world, the glitches in the world simulator model will be incorporated into the training. There will be edge cases due to this layered "overtraining", where a robot/agent/body will expect Y to happen but X will happen, causing unpredictable behaviour.I assume that a generic world agent will be able to autocorrect, but this could also lead to dangerous issues.

I.e. if the simulation has enough videos of firefighters breaking glass where it seems to drop instantaneously and in the world sim it always breaks, a firefighter robot might get into a problem when confronted with unbreakable glass, as it expects it to break as always, leading to a loop of trying to shatter the glass instead of performing another action.

Approximately speaking, you have a world model and an agent model. You continue to train the world model using data collected by the robot day-to-day. The robot "dreams" by running the agent model against the world model instead of moving around in the real world. Dreaming for thousands of (simulated) hours is much more efficient than actually running the physical hardware for thousands of wall clock hours.

It is possible - for example, getting a blob of physics data, fitting a curve then projecting the curve to theorise what would happen in new unseen situations. The information constraints don't limit the ability to generate new data in a specific domain from a small sample; indeed it might be possible to fully comprehend the domain if there is an underlying process it can infer. It is impossible to come up with wildly unrelated domains though.

The benefit of these AI-generated simulation models as a training mechanism is that it helps add robustness without requiring a large training set. The recombinations can generate wider areas of the space to explore and learn with but using a smaller basis space.

To pick an almost trivial example, let's say OCR digit recognition. You'll train on the original data-set, but also on information-preserving skews and other transforms of that data set to add robustness (stretched numbers, rotated numbers, etc.). The core operation here is taking a smallset in some space (original training data) and producing some bigset in that same space (generated training data).

For simple things like digit recognition, we can imagine a lot of transforms as simple algorithms, but one can consider more complex problems and realize that an ML model would be able to do a good job of learning how to generate bigset candidates from the smallset.

AlphaGo would seem to be a conceptually simple counter example.

  > What's with this insane desire for anthropomorphism?

Devil's advocate: Making the assumption that consciousness is uniquely human, and that humans are "special" is just as ludicrous.

Whether a computational medium is carbon-based or silicon-based seems irrelevant. Call it "carbon-chauvinism".

Yes, and an object in OOP isn't really a physical object. And a string isn't really a thin bit of rope.

No-one cares. It's just terminology.

AI has traditionally been driven by "metaphor-driven development" where people assume the brain has system X, program something they give the same name, and then assume because they've given it that name it must work because it works in the brain.

This is generally a bad idea, but a few of the results like "neural networks" did work out… eventually.

"World model" is another example of a metaphor like this. They've assumed that humans have world models (most likely not true), and that if they program something and call it a "world model" it will work the same way (definitely not true) and will be beneficial (possibly true).

(The above critique comes from Phil Agre and David Chapman.)