Zero chance private github repos make it into openai training data, can you imagine the shitshow if GPT-4 started regurgitating your org's internal codebase?
Automatic kernel fusion (compilation) is a very active field, and most major frameworks support some easy-to-use compilation (e.g. jax's jit, or torch.compile which iirc uses openai's triton under the hood). Often you can still do better than the compiler by writing fused kernels yourself (either in cuda c++ or in something like triton (python which compiles down to cuda) but compilers are getting pretty good.
edit: not sure why op is getting downvotes, this is a very reasonable question imo; maybe the characterization of kernel compilation as "AI" vs. just "software"?
New hires' comp is much higher than existing employees', especially if you've hit your cliff. 7 figures for E6 can happen if you joined recently, have good counter-offers, and negotiate. It's not super uncommon but it's also not the median E6 comp.
> the company pushed back against a proposed amendment to the AI Act that would have classified generative AI systems such as ChatGPT and Dall-E as “high risk” if they generated text or imagery that could “falsely appear to a person to be human generated and authentic.” [...] The company argued that it would be sufficient to instead rely on another part of the Act, that mandates AI providers sufficiently label AI-generated content and be clear to users that they are interacting with an AI system.
This sounds pretty reasonable? I don't think it's hypocritical to be talking about the doom of humanity and also arguing that GPT-3, a 3-year old model, should not be classified as "high-risk" in that sense.
Even if you disagree, questioning Altman's leadership and calling him an "empty soul" over this kind of regulatory detail is not adding substance to the discussion imo.
Andrej's "Building Makemore" series is exactly this, it includes a wonderful lecture where he computes all the gradients for a simple network by hand and compares them against the values produced by torch's autograd.
minGPT prioritized being understandable above all else, and was not very fast. This repo includes several optimizations, but it still much more understandable than probably any other open source implementation.
Anecdotally, chatgpt seems much worse to me than Google for getting correct answers. Like orders of magnitude worse. Tells me the wrong timezone for a city kind of bad. No doubt it will be much better in the future, and they've definitely found PMF with the interface, but I would not trust it right now with anything even slightly important to me.
Signal boosting in case anyone from notion is reading: please address performance, it is practically the only thing I care about. I love the product but it is almost unusably slow with my tiny amount of data. Cache more stuff on my device, avoid blocking network calls where possible, etc, please!
The "scandal" that prompted this article is referenced with sources under the heading "Why XCheck is in the news". Neither the article nor the comment you're replying to says anything about data breaches.
It's a position paper, academics publish them all the time, they're very much a part of scientific discourse. Just different than an experimental results paper.
The author is a skilled research scientist in a very competitive space with some high-profile publications (e.g. Gumbel Softmax). He is absolutely an outlier, but not a unicorn -- AI researchers with good publications and reputation will attract a lot of interest from companies with lots of money to spend. Low 7-figures for an ~L7 research scientist with competing offers from FAANG research labs is not crazy.
Is there any evidence that GPT-3 responses are edited/filtered before being returned to users? My understanding is that some GPT-3 responses are annotated post-hoc, and this data is used to fine-tune later versions of GPT-3 (InstructGPT). This article seems extremely misleading.
As someone who has had to reproduce others' research results: it is much much better to release the unclean, unorganized code that actually produced your results than it is to release nothing. Even if it doesn't run (e.g. it depends on a hardware system that the user won't have access to) it's still better for people to be able to read your code and understand some tricky part that isn't fully explained in your paper.
I always assumed we'd keep the summer hours year round. I don't even think about DST during the summer, it's during the fall where we suddenly lose an hour of evening sunlight that I complain. Unless I'm misreading, your comment is just pointing out that keeping the winter hours year-round is sub-optimal (I agree) but not arguing in favour of switching the clocks twice per year.
I think it's very reasonable to hold that view (I do as well). But to propose a solution, it's necessary to understand the facts of the current situation, which is what I'm trying to learn.
Interesting. Resetting cost basis upon inheritance does seem like a massive tax avoidance loophole, I can't quickly think of a reason why this should be allowed. I agree also that this would probably be a popular (and sensible) modification to the tax code.