mirker

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 penalty when there is a misprediction.

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).

Look at GPT-3+, OpenAI gets fame and fortune while people struggle to reproduce their last-gen models.

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 from 1000 AD who was given a car or TV to examine. Could you really be confident you understood how it worked by just running experiments on it as a black box? If you give a non-programmer the linux kernel, will he/she think it’s magical?

Things look like magic especially when you can’t look under the hood. The story of the Mechanical Turk is one example of that.

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 then libraries would make cross dependencies to bake in the API mistakes.

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 with GPU support and it is pretty close to that. JAX is an attempt to approach language simplicity and purity.

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 technique novel? No.

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 potentially very technical and includes game theory and proofs. Even in CS, undergrads take intro theory courses and “bottom out” in their math skills, even though grad-level CS gets much more difficult. Therefore, I’d imagine the primary determinant of this rigor phenomenon is the GPA inflation of the major.

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 typical team structure.

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 revisions also seems anti-consumer.

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.