We are seeing increasing evidence that these sort of video world models are terrible options for useful rollouts of the physical dynamics of the environment. It is hypothesized that you can get them to be better than simulators by training on physics simulation data, but then the question becomes, why not use the simulator directly?
There are a lot of areas where predictive models make sense in the robotics stack, but doing it with "video world models" as is trendy this year is likely a bet in the wrong direction according to the evidence we have been amassing in the last 6 months.
I had a lot of fun playing this! It's definitely the quirkiness that sets it apart from the typical "GenAI" content. Highly recommend other hackernews readers check it out.
thats just not in the BDs ethos. They have been the only company really trying to physically build these kinds of robust, dynamic systems for the last 3 decades (almost to a fault).
Thank you for thinking of this and implementing it!
But, I'm wondering is there any major difference between just clicking the "..." next to the song in the default interface and selecting "Download->Audio"?
I really have to plug the method of Comprehensible Input (CI) [0].
Specifically, Pablo Román's effort to make this viable for Spanish [1]. I started before there was a website or anything, just watching free youtube videos. Now I'm definitely B2 or C1. I subscribe partially just to show my support even though I mostly consume native-level media now.
Similar and helpful along he lines of CI are [2] and [3].
For what it's worth, this was a paper I was exposed to during grad school for robotics/CS. I'm not saying one has to go to school to get good curation of content, but it is certainly one way to be exposed to a well structured foundation of knowledge in your field.
What I have found works best is to basically forget about putting strong priors on the structure of your notes. Just let everything be flat - or at most, create one level of hierarchy for a specific project.
When you have a new thought, use search. I have a command that pulls up the advanced search tool plugin and fuzzy searches my term. I then quickly scan (visually as cards) the notes I created already. If there is something related to my idea I open that note and continue that thread. If it's new, I create a new note.
This can be improved even more by using tags liberally, but I find that just having a powerful search and taking the time to "check" before creating a new note works very well for my style of note taking!
You're right about AI capabilities being much better in the "ungrounded" realm (like writing sonnets), but I would revise the statement a little to be more accurate.
AI systems are quite good at perception in a traditional sense (e.g. determining a red ball is in the sensor receptive field), but once actual cognition about how the red ball relates to the world around it becomes important, current approaches are limited.
I would argue that the first wave of perception is solved much more completely than the ability to reason about complex interactions of objects grounded in the physical world.
Think about it for a moment. Can you define human intelligence? Have you ever gotten in a debate with someone about this? Is there a commonly accepted way to designate intelligence that isn't somewhat controversial?
How will we ever define AGI if we still haven't even defined RoHI (Regular ol' Human Intelligence) sufficiently?
Somehow, this has a perfect selection of games that formed a great portion of my childhood (some of which I was too young to play myself, but I watched my cousin or dad play).
Amazing to teleport into these worlds through the browser only 15 years later.
There are a lot of areas where predictive models make sense in the robotics stack, but doing it with "video world models" as is trendy this year is likely a bet in the wrong direction according to the evidence we have been amassing in the last 6 months.