manas96

I agree, low-level highly specialized technology roles within game companies do pay well.

Interesting. Any (in)famous UMA vote debates? Or interesting Unknown/50-50 outcome resolutions?

Just curious but how are bets arbritated on these website?

Meaning who decides if an outcome was yes or no? Answers to things like "Who will win the next Best Picture Oscar?" are fairly obvious and binary.

Can we make bets whose answers are not binary yes/no?

What about "Will celebraty X and Y break up?"? Does Polymarket go to X and Y to confirm if they broke up or something :D

Thanks, I'll try using the "Following" tab. I have a lurker account but never really used it because I only ever saw crap in "For You".

I did prompt additional times insisting on realistic physics..

I think modelling accurate articulated body dynamics is feasible but when you add deformation (muscles) it gets much harder.

Indeed, it entirely depends on which axis you want to focus on. A loose trade-off chart would be speed, stability and accuracy. You can only have two of these in a simulator.

Robotics folks probably want speed and accuracy. I'm from the video game industry so I generally look for speed and stability.

Note: This is a loose analogy and recent techniques are already blurring the lines between these axis.

Thanks for the additional reading. I've often thought about LLMs and their ability to represent the physical world with its laws. And always concluded it is not really possible to do so with "just" text tokens and their relations in a latent space. It looks to me there are different approaches being taken to tackle this:

* You could instruct your LLM to interact with a simulator to run experiments and infer behaviour

* You could edit the transformer model and inject spatially relevant data rather than text as is done in above paper

* You could change the architecture to be more condusive for representating a world state. I.e., LeCun's JEPA world model.

* You could further enhance some of the above by using a differentiable physics engine (eg. NVIDIA Newton) to calculate losses directly.

But at the end of the day if a model has any hope to always produce realistic physics, it HAS to learn the laws of nature in some form or other. It looks to me that the next big leap could be achieved by combining the last two approaches.

P.S.: I like discussing such topics. If anyone knows a forum or discord with like-minded people, please let me know :)

it was the first link I got after googling free video hosting sites

so in essence is it trading memory for speed?

200% agree. If you decide to use this power you must accept the tiny risk and huge consequences of it going wrong. The article seems like it was written by AI, and quoting the agent's "confession" as some sort of gotcha just demonstrates the author does not really understand how it works...

LLMs are not deterministic per my understanding. A program always produces the same output for the same input and instructions (ignore FP accuracy for now). How is determinism achieved here?

I think the diagrams look very similar to what Keenan Crane uses in his papers, perhaps they used that tool. I think his students have now fleshed it out for general use.

One esoteric route would be to try and specialize in an area where talent is scarce. There's a lot of gameplay programmers, few engine programmers, fewer graphics programmers, and very few physics programmers (in my experience at least).

As such you could try to specialize in this area (collision detection, ray queries, rigid body simulation, constraints, solvers, softbody sim, fluid sim etc.). Of course this isn't for everyone as it requires skills and interest in: low level concurrent programming, maths/linear algebra and physical behavior intuition. If you do find these topics fascinating and can demonstrate some ability in them, your skills will certainly be in demand.