Far too much marketing speech, far too little math or theory, and completely misses the mark on the 'next frontier'. Maybe four years ago, spatial reasoning was the problem to solve, but by 2022 it was solved. All that remained was scaling up. The actual three next problems to solve (in order of when they will be solved) are: - Reinforcement Learning (2026) - General Intelligence (2027) - Continual Learning (2028) ED…
In my thinking what AI lacks is a memory system
That has been solved with RAG, OCR-ish image encoding (deepseek recently) and just long context windows in general.
From reading that, I'm not quite sure if they have anything figured out.
I actually agree, but her notes are mostly fluff with no real info in there and I do wonder if they have anything figured out besides "collect spatial data" like imagenet.
There are actually a lot of people trying to figure out spatial intelligence, but those groups are usually in neuroscience or computational neuroscience.
Here is a summary paper I wrote discussing how the entorhinal cortex, grid cells, and coordinate transformation may be the key: https://arxiv.org/abs/2210.12068 All animals are able to transform coordinates in real time to navigate their world and humans have the most coordinate representations of any known living animal. I believe human level intelligence is knowing when and how to transform these coordinate systems to extract useful information.
I wrote this before the huge LLM explosion and I still personally believe it is the path forward.
Far too much marketing speech, far too little math or theory, and completely misses the mark on the 'next frontier'. Maybe four years ago, spatial reasoning was the problem to solve, but by 2022 it was solved. All that remained was scaling up. The actual three next problems to solve (in order of when they will be solved) are: - Reinforcement Learning (2026) - General Intelligence (2027) - Continual Learning (2028) ED…
Hasn't RLHF and with LLM feedback been around for years now
Large latent flow models are unbiased. On the other hand, if you purely use policy optimization, RLHF will be biased towards short horizons. If you add in a value network, the value has some bias (e.g. MSE loss on the value --> Gaussian bias). Also, most RL has some adversarial loss (how do you train your preference network?), which makes the loss landscape fractal which SGD smooths incorrectly. So, basically, there's a lot of biases that show up in RL training which can make it both hard to train, and even if successful, not necessarily optimizing what you want.
From reading that, I'm not quite sure if they have anything figured out. I actually agree, but her notes are mostly fluff with no real info in there and I do wonder if they have anything figured out besides "collect spatial data" like imagenet. There are actually a lot of people trying to figure out spatial intelligence, but those groups are usually in neuroscience or computational neuroscience. Here is a summary pap…
> Here is a summary paper I wrote discussing how the entorhinal cortex, grid cells, and coordinate transformation may be the key: https://arxiv.org/abs/2210.12068 All animals are able to transform coordinates in real time to navigate their world and humans have the most coordinate representations of any known living animal. I believe human level intelligence is knowing when and how to transform these coordinate systems to extract useful information.
Yes, you and the Mosers who won the Nobel Prize all believe that grid cells are the key to animals understanding their position in the world.
Far too much marketing speech, far too little math or theory, and completely misses the mark on the 'next frontier'. Maybe four years ago, spatial reasoning was the problem to solve, but by 2022 it was solved. All that remained was scaling up. The actual three next problems to solve (in order of when they will be solved) are: - Reinforcement Learning (2026) - General Intelligence (2027) - Continual Learning (2028) ED…
What do you consider "General Intelligence" to be?
A good start would be:
1. Robust to adversarial attacks (e.g. in classification models or LLM steering).
2. Solving ARC-AGI.
Current models are optimized to solve the current problem they're presented, not really find the most general problem-solving techniques.
Personally, I think the direction AI will go towards is having an AI brain with something like a LLM at its core augmented with various abilities like spatial intelligence, rather than models being designed with spatial reasoning at its core. Human language and reasoning seems flexible enough to form some kind of spatial understanding, but I'm not so sure about the converse of having spatial intelligence derive human reasoning. Similar to how image generation models have struggled with generating the right number of fingers on hands, I would expect a world model designed to model physical space to not generalize the understanding of simple human ideas.
Far too much marketing speech, far too little math or theory, and completely misses the mark on the 'next frontier'. Maybe four years ago, spatial reasoning was the problem to solve, but by 2022 it was solved. All that remained was scaling up. The actual three next problems to solve (in order of when they will be solved) are: - Reinforcement Learning (2026) - General Intelligence (2027) - Continual Learning (2028) ED…
Combinatorial search is also a solved problem. We just need a couple of Universes to scale it up.
I'd imagine Tesla's and Waymo's AI are at the forefront of spatial cognition... this is what has made me hesitant to dismiss the AI hype as a bubble. Once spatial cognition is solved to the extent that language has been solved, a range of applications currently unavailable will drive a tidal wave of compute demand. Beyond self driving, think fully autonomous drone swarms... Militaries around the world certainly are and they're salivating.