One thing to point out is that the threshold of predictor complexity is dependent on the execution pipeline. A very speculative and deep architecture has a bigger need for better predictors, since it has a massive…
Does anyone have experience using these open source models in production?
There is a ton of value. OpenAI having proprietary LLMs single handedly pivoted the entire field to LLMs. A random GitHub repository doesn’t come close to impact.
Because the authors don’t get a large reward for open sourcing the work and they stand to lose future value by lowering the gate to competition. You may want the code, but Google will not care (or it might dislike it).…
They asked Watson of course.
Agreed. Here’s the thing: the authors of that paper got early access to GPT-4 and ran a bunch of tests on it. The important bit is that MSR does not see into OpenAI’s sausage making. Now imagine if you were a peasant…
The APIs were messed up early on, which is a reason TF2 happened. Every team started making their own random implementations of stuff. You had the TF Slim API, you had Keras, etc. The API just got fatter and fatter and…
Nah, TF has had dynamic execution since TF2 and it’s still losing users, it seems. The execution model and API is simply more complicated. What’s a session, placeholder, constant, tensor, …? PyTorch was sold as numpy…
PyTorch examples were also cleaner. torchvision had ResNet training batteries included while TF had role your own or clone some weird Keras repository.
I don’t understand what you mean. Here’s how many applied ML papers work: create a new dataset for a novel problem, download a PyTorch model, point model at dataset directory. Is it novel? By construction. Is the ML…
The story I’ve heard is the economics undergrads can’t get into economics grad school. This is just a rumor but the sentiment is that undergrads get taught a watered down version of economics theory. Economics theory is…
I agree with you but does anyone even recognize the last category outside blue-sky research? People have a tendency to bin other people into buckets. Being a master at 2 things means you can’t be easily placed in a…
But it’s more thoughtful. The purchaser thought long and hard about what place the purchasee can use it in.
When people say you need the third hardware revision to get functional performance, and it still doesn’t work, you should conclude it’s poorly designed. The fact that these chips were being sold with old hardware…
Yeah the issue is you can generate data, but it won’t be good data. Training over random strings won’t make you learn language, but it’s technically data.
The OPs point is that it’s likely impossible to do what is claimed here in general. Imagine the LLM says something like Fermat’s Last Theorem. To verify it, you’d have to either 1) have a proof assistant powerful enough…
Many of those people left though.
There’s actually a few papers already on constrained decoding. I won’t link them but if you go on arxiv and really look you will find a couple in the past year.
Sure, I agree they are useful. My objection is it’s more in the tool category than science, while Alphafold is both. There isn’t convincing evidence that GPTs are pushing what we know; rather, they make it easier to…
Alphafold is open and seems fundamentally transformative in the science space. GPT is nice but it’s a smart meme-generator at the moment. I don’t disagree with the impact on G’s bottom line, though.
OS needs less than 100GB, though, so mixed drives seems more cost effective.
The image is nearly unreadable for me on mobile.
DOTA, Rocket League, CSGO, etc. are e-sports friendly but don’t have this population problem. It’s not about being “hardcore”; it’s about having broad appeal to hit a wide market segment. RTS has struggled with that…
Hardcore “competitive” RTS (StarCraft/Age of Empires) can’t since it’s not competitive enough if you can’t hit a meaningful 300+ actions per minute, which is impossible without hotkeys. Even IF they could, I am…
It looks interesting, honestly. Shame the RTS market is relatively small.
One thing to point out is that the threshold of predictor complexity is dependent on the execution pipeline. A very speculative and deep architecture has a bigger need for better predictors, since it has a massive…
Does anyone have experience using these open source models in production?
There is a ton of value. OpenAI having proprietary LLMs single handedly pivoted the entire field to LLMs. A random GitHub repository doesn’t come close to impact.
Because the authors don’t get a large reward for open sourcing the work and they stand to lose future value by lowering the gate to competition. You may want the code, but Google will not care (or it might dislike it).…
They asked Watson of course.
Agreed. Here’s the thing: the authors of that paper got early access to GPT-4 and ran a bunch of tests on it. The important bit is that MSR does not see into OpenAI’s sausage making. Now imagine if you were a peasant…
The APIs were messed up early on, which is a reason TF2 happened. Every team started making their own random implementations of stuff. You had the TF Slim API, you had Keras, etc. The API just got fatter and fatter and…
Nah, TF has had dynamic execution since TF2 and it’s still losing users, it seems. The execution model and API is simply more complicated. What’s a session, placeholder, constant, tensor, …? PyTorch was sold as numpy…
PyTorch examples were also cleaner. torchvision had ResNet training batteries included while TF had role your own or clone some weird Keras repository.
I don’t understand what you mean. Here’s how many applied ML papers work: create a new dataset for a novel problem, download a PyTorch model, point model at dataset directory. Is it novel? By construction. Is the ML…
The story I’ve heard is the economics undergrads can’t get into economics grad school. This is just a rumor but the sentiment is that undergrads get taught a watered down version of economics theory. Economics theory is…
I agree with you but does anyone even recognize the last category outside blue-sky research? People have a tendency to bin other people into buckets. Being a master at 2 things means you can’t be easily placed in a…
But it’s more thoughtful. The purchaser thought long and hard about what place the purchasee can use it in.
When people say you need the third hardware revision to get functional performance, and it still doesn’t work, you should conclude it’s poorly designed. The fact that these chips were being sold with old hardware…
Yeah the issue is you can generate data, but it won’t be good data. Training over random strings won’t make you learn language, but it’s technically data.
The OPs point is that it’s likely impossible to do what is claimed here in general. Imagine the LLM says something like Fermat’s Last Theorem. To verify it, you’d have to either 1) have a proof assistant powerful enough…
Many of those people left though.
There’s actually a few papers already on constrained decoding. I won’t link them but if you go on arxiv and really look you will find a couple in the past year.
Sure, I agree they are useful. My objection is it’s more in the tool category than science, while Alphafold is both. There isn’t convincing evidence that GPTs are pushing what we know; rather, they make it easier to…
Alphafold is open and seems fundamentally transformative in the science space. GPT is nice but it’s a smart meme-generator at the moment. I don’t disagree with the impact on G’s bottom line, though.
OS needs less than 100GB, though, so mixed drives seems more cost effective.
The image is nearly unreadable for me on mobile.
DOTA, Rocket League, CSGO, etc. are e-sports friendly but don’t have this population problem. It’s not about being “hardcore”; it’s about having broad appeal to hit a wide market segment. RTS has struggled with that…
Hardcore “competitive” RTS (StarCraft/Age of Empires) can’t since it’s not competitive enough if you can’t hit a meaningful 300+ actions per minute, which is impossible without hotkeys. Even IF they could, I am…
It looks interesting, honestly. Shame the RTS market is relatively small.