Then its reasonabel to expect someone who is not using LLMs to have an edge in their cognitive abilies. Or will it be overshadowed by the shear magnitude of bruteforcing that LLMs are capable of. I tend to side with the former. If that would be true, then not using LLMs would give an edge in solving novel problems. But we have been dependent on tools, cognjtive and physical, since forever. We cant imagine a world without tools. Why would LLMs be discriminated as a tool
Somewhere I read that the 8B llama2 model could be undertrained by 100-1000x. So is it possible to train a model with 8B/100 = 80M parameters to perform as good as the llama2 8B model, given enough training time and training tokens?
>We made several new observations on scaling behavior during the development of Llama 3. For example, while the Chinchilla-optimal amount of training compute for an 8B parameter model corresponds to ~200B tokens, we found that model performance continues to improve even after the model is trained on two orders of magnitude more data. Both our 8B and 70B parameter models continued to improve log-linearly after we trained them on up to 15T tokens. Larger models can match the performance of these smaller models with less training compute, but smaller models are generally preferred because they are much more efficient during inference.
Can someone experienced please explain this. Does this mean, a lean model with more training time and/or more (or better) training data will perform better than a fat model?
Is this generating videos as streaming content e.g. like a mp4 video. As far as I can see, it is doing that. Is it possible for AI to actually produce the 3d models?
What kind of compute resources are required to produce the 3d models.
Lets say that the owner of the capital wants to fund hardworking individuals like "Shuji Nakamura". At the same time, most individuals seeking capital for research are just pretending to work hard, how does capital-owner identify the true warriors.
The 10-q is there in the SEC website[1]. The summery is a quick read[2]. Just read it like you would read a textbook. Read the same for APPL, GOOG, AMZN. Do the same for two other sectors. In total about 20 companies. Very quickly you see the patterns emerge.
In the context META, here is a quick summary
Besides the sequential increases of MAU, DAU, DAP, MAP, One thing that stands out to me is:
"We anticipate our full-year 2024 capital expenditures will be in the range of $30-35 billion, with growth driven by
investments in servers, including both non-artificial intelligence (AI) and AI hardware, and data centers as we ramp up
construction on sites with the new data center architecture we announced late last year."
Thats a lot of investment mostly in NVDA hardware. Expect NVDA to rise. This also means good for other suppliers, AMD, INTC, TSMC. The benefit for META will be apparent in a few quarters.
About a decade ago, when facebook(after IPOing at 38) came down to 20, I was in a bus speaking with a friend about buying a bit and holding it forever(like Buffet). He laughed at me. A lot of folks in the bus gave me strange looks.
So far it has been the best decision(investment wise) for me.
The only effort I did was to read their IPO prospectus and the latest 10q(at that time).
Stop listening to anything else other than your own. Just read the 10Q. After cutting the fat, its about 8-10 pages, every 3 months. Lets say you follow 20 companies. That about the size of one book every three months. Not an entertaining read, but definitely enriching one.
Folks with Tesla FSD, please find an empty parking lot and drive all you want. It helps Tesla's mission. Please don't test it on real roads. At least not on busy roads
I am in the minority here. But for me, google's presentations were always like this. The presentation always seemed so rehearsed with artificial excitement.
This is not a comprehensive answer. Please DM me if you are interested in a longer answer. But the idea was to map the video into a 2d grid(25x25). Map the 2d grid into a half dome 3d model(25x25 vertices). Then divide the flat video into textures that fit into each to the smaller rectangles/triangles on the 3d half-dome.
Once you have the 3d model, manipulate it using OpenGl or metal primitives.