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Comment by Rainymood | Hacker News Reader

I, literally just 15 minutes ago, had a chat with a friend of mine exactly about how what we are doing right now with computer vision is all based on a flawed premise (supervised 2D training set). The human brain works in 3D space (or 3D+time) and then projects all this knowledge in a 2D image.

Here I was, thinking I finally had thought of a nice PhD project and then Deepmind comes along and gets the scoop! Haha.

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I don’t think this is a novel idea, but it is still a great topic for a PhD. While the results in this paper look impressive, my suspicion is that the system doesn’t generalize particularly well. (I suspect this from experience with similar, albeit simpler, ideas, as well as from looking at the datasets.) If you can make a system that generalizes to new environments and objects, or a system that works with real-world natural image/video data, that would be a tremendous accomplishment.

> I don’t think this is a novel idea, but it is still a great topic for a PhD.

Novelty is usually a requirement for a PhD project.

For a result not a project, and certainly not a topic!

Generalization is a more fundamental problem, and (imho) should be tackled first at a more fundamental level.

For example, if you have a classifier that can recognize cats, it doesn't mean it will work for cartoon cats. You'd have to train the system all over again with cartoon cats. Instead, you want the system to learn more like humans, where only a small number of examples is necessary to make the connection between real and cartoon cats.

It is possible that the problems are related—-it may be that, to achieve human-like generalization, neural nets need to learn in a human-like environment, instead of from a folder full of images. But time will tell.

This has been said many times in different ways over the years. To achieve human-like intelligence one needs a human-like body operating in a human-like environment. It's the first of the E's in: embodied emergent extended enactive. https://plato.stanford.edu/entries/embodied-cognition/

Maybe in trivial examples, but bleeding edge has for quite some time adopted 3D convolutions (either 3D space or 2D + time), and combined them with RNNs for more power.

Here is an excellent talk by Geoffrey Hinton on exactly that topic https://www.youtube.com/watch?v=rTawFwUvnLE

Indeed. Here is a recent realization of those ideas by him and his team: https://openreview.net/forum?id=HJWLfGWRb

The trick is to then to have one agent try to take on the perspective of another agent, then change its behavior accordingly (depending on task goal). I.e. theory of mind.

Hide and seek

There you go. A good task.

Would probably make for more interesting game AI and pathfinding behavior for sure.

"Spatial memory" https://en.wikipedia.org/wiki/Spatial_memory

It may be splitting hairs, but I think the mammalian brain, at least, can simulate/remember/imagine additional 'dimensions' like X/Y/Z spin, derivatives of velocity like acceleration/jerk/jounce.

Is space 11 dimensional (M string theory) or 2 dimensional (holographic principle)? What 'dimensions' does the human brain process? Is this capacity innate or learned; should we expect pilots and astronauts to have learned to more intuitively cognitively simulate gravity with their minds?

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