Can you explain this “world model” concept to me? How do you actually interface with a model like this?
Why is this idea of a world model helpful? Because it allows multiple interesting things, like predict what happens next, model counterfactuals (what would happen if I do X or don't do X) and many other things that tend to be needed for actual principled reasoning.
The capabilities of LLMs are limited by what's in their training data. You can use all the tricks in the book to squeeze the most out of that - RL, synthetic data, agentic loops, tools, etc, but at the end of the day their core intelligence and understanding is limited by that data and their auto-regressive training. They are built for mimicry, not creativity and intelligence.
https://www.youtube.com/watch?v=l-OLgbdZ3kk
In this video we explore Predictive Coding – a biologically plausible alternative to the backpropagation algorithm, deriving it from first principles.
Predictive coding and Hebbian learning are interconnected learning mechanisms where Hebbian learning rules are used to implement the brain's predictive coding framework. Predictive coding models the brain as a hierarchical system that minimizes prediction errors by sending top-down predictions and bottom-up error signals, while Hebbian learning, often simplified as "neurons that fire together, wire together," provides a biologically plausible way to update the network's weights to improve predictions over time.
Training on 2,500 hours of prerecorded video of people playing Minecraft, they produce a neural net world model of Minecraft. It is basically a learned Minecraft simulator. You can actually play Minecraft in it, in real time.
They then train a neural net agent to play Minecraft and achieve specific goals all the way up to obtaining diamonds. But the agent never plays the real game of Minecraft during training. It only plays in the world model. The agent is trained in its own imagination. Of course this is why it is called Dreamer.
The advantage of this is that once you have a world model, no extra real data is required to train agents. The only input to the system is a relatively small dataset of prerecorded video of people playing Minecraft, and the output is an agent that can achieve specific goals in the world. Traditionally this would require many orders of magnitude more real data to achieve, and the real data would need to be focused on the specific goals you want the agent to achieve. World models are a great way to cheaply amplify a small amount of undifferentiated real data into a large amount of goal-directed synthetic data.
Now, Minecraft itself is already a world model that is cheap to run, so a learned world model of Minecraft may not seem that useful. Minecraft is just a testbed. World models are very appealing for domains where it is expensive to gather real data, like robotics. I recommend listening to the interview above if you want to know more.
World models can also be useful in and of themselves, as games that you can play, or to generate videos. But I think their most important application will be in training agents.
My thinking is that such world models should be integrated with LLM like the lower levels of perception are integrated with higher brain function.
I have a disorder characterised by the brain failing to filter own its own sensory noise, my vision is full of analogue TV-like distortion and other artefacts. Sometimes when it's bad I can see my brain constructing an image in real time rather than this perception happening instantaneously, particularly when I'm out walking. A deer becomes a bundle of sticks becomes a muddy pile of rocks (what it actually is) for example over the space of seconds. This to me is pretty strong evidence we do not experience reality directly, and instead construct our perceptions predictively from whatever is to hand.
You're quite correct that our "reality" is in part constructed. The Flashed Face Distortion Effect [0][1] (wherein faces in the peripheral vision appear distorted due the the brain filling in the missing information with what was there previously) is just one example.
[0] https://en.wikipedia.org/wiki/Flashed_face_distortion_effect [1] https://www.nature.com/articles/s41598-018-37991-9
Also do you get comorbid headaches with yours out of interest?
Thankfully I don't get comorbid headaches – in fact I seldom get headaches at all. And even on the odd occasion that I do, they're mild and short-lived (like minutes). I don't recall ever having a headache that was severe, or that lasted any length of time.
Yours does sound much more extreme than mine, in that mine is in no way debilitating. It's more just frustrating that it exists at all, and that it isn't more widely recognised and researched. I have yet to meet an optician that seems entirely convinced that it's even a real phenomenon.
I actually suspected 5HT2A might be involved before that study came out, since my visual distortions sometimes resemble those caused by psychedelics. It's also known that both psychedelics and anecdotally from patient's groups SSRIs too can cause a similar symptoms to visual snow syndrome, I had a bad experience with SSRIs for example but serotonin antagonists actually fixed my vision temporarily - albeit with intolerable side-effects so I had to stop.
It's definitely a bit of a faff that people have never heard of it, I had to see a neuro-ophthalmologist and a migraine specialist to get a diagnosis. On the other hand being relatively unknown does mean doctors can be willing to experiment. My headaches at least are controlled well these days.
https://www.vox.com/podcasts/467048/unexplainable-hearing-au...
https://en.wikipedia.org/wiki/2022_University_of_Idaho_murde...
Direct Realism is the idea that reality is directly available to us and any intermediate transformations made by our brains is not enough to change the dial.
Direct Realism has long been refuted. There are a number of examples, e.g. the hot and cold bucket; the straw in a glass; rainbows and other epiphenomena, etc.
Is he advocating for philosophical idealism of the mind or does he has an alternate physicalist theory?
your whole reasoning is neither here not there, and attacking a straw man - YLC for sure knows that human experience of reality is heavily modified and distorted
but he also knows, and I'd bet he's very right on this, that we don't "sip reality through a narrow straw of tokens/words", and that we don't learn "just from our/approved written down notes", and only under very specific and expensive circumstances (training runs)
anything closer to more-direct-world-models (as LLMs are ofc at a very indirect level world models) has very high likelihood of yielding lots of benefits
The mental model this person has of this feed of words is what an LLM at best has (but human model likely much richer since they have a brain, not just a transformer). No real-world experience or grounding, therefore no real-world model. The only model they have is of the world they have experience with - a world of words.
Some world models can also be updated by their respective AI agents, e.g. "I, Mr. Bot, have moved the ice cream into the freezer from the car" (thereby updating the state of freezer and car, by transferring ice cream from one to the other, and making that the context for future interactions).