Just skimmed this but it immediately gives up on meshes :( i actually met with a startup, backflip.ai, that is doing mesh generation and their results are very impressive. But it’s a commercial thing
There were a few other agent demos around 2017-2020ish that also were faked. It's poisoned the well for me at this point so I just have to wait and see
I counted very quickly but 78? I learned arabic in kindergarten, im not sure what your point was. There are arabic spelling bees and an alphabet song just like english
The comment you replied to was saying LLMs trained on english cant count letters in english
This is a really cool paper, reminds me of the simple exercise Karpathy goes through in his NN vid series with a bigram predictor. Looks like in practice there’s still some grounding issues when attempting to use them for instruction-tuned applications, but clever direction to explore!
Well, when one group disenfranchises you via military conquest without such a Noble Distinction, and you cannot defeat them in a Noble Battle, you expand the available choices. And it seems to have worked out for most independence movements.
Yes, generally people side with their family and loved ones over principles. My fathers family had their house aerial bombed by the french, so a few tram bombs just sounds par for the course of an independence uprising. But your main point is correct - violence is always ugly, and we have to be careful when rationalizing it to achieve pragmatic goals.
This seems really low. If we could throw 3.4b at a new car company and get the awesome suspension tech and price points of the BYD cars then that’d be huge. If every country could do this domestically, even better!
That evaluation would be an amazing addendum or engineering blog post! I know it’s not as sexy as announcing a product, but from an engineering perspective the process matters as much as the outcome :)
Im currently waiting on the motors for this, but my bambu p1s printed out the parts with minimal stringing in like 90 mins. Really to try it out for cooking experiments
I see, I agree with you. But I would imagine the useful metric to be “error rate below X GB memory”. We really just need memory and/or compute reported when these evaluations are performed to compile that. People do it for training reports since compute and memory is implicit based on training time (since people saturate it and report what hardware they’re using). But for inference no such details :\
Many applications dont want to host inference on the cloud and would ideally run things locally. Hardware constraints is clearly important.
Id actually say its the most important metric for most open models now, since the price per performance of closed cloud models is so competitive with open cloud models, so edge inference that is competitive is a clear value add
you're right, but people predict the future all the time, if there's a pre-established model for that prediction. I just threw a ball in the air and predicted how long it'd stay there </akshually>
so I think this comes down to how the predictions were arrived. One way to do this is to ask individuals who both have similarly high predictive scores and bet on the same types of events to explain some of their past predictions, and if their methods are similar then you've learned a new predictive tool.
I think youre kind of right, its not really meaningful to know “im right about things that happen 5% of the time!”. But if a lot of things you bet on structurally happen to be low probability and of the same type (eg you bet on one type of catastrophic event like pandemic, crop disease, or war etc) then its useful