Base model =/= Corpo fine tune

I’m a seasoned dev and I was at a launch event when an edge case failure reared its head.

In less than a half an hour after pulling out my laptop to fix it myself, I’d used Cursor + Claude 3.5 Sonnet to:

1. Automatically add logging statements to help identify where the issue was occurring
2. Told it the issue once identified and had it update with a fix
3. Had it remove the logging statements, and pushed the update

I never typed a single line of code and never left the chat box.

My job is increasingly becoming Henry Ford drawing the ‘X’ and not sitting on the assembly line, and I’m all for it.

And this would only have been possible in just the last few months.

We’re already well past the scaffolding stage. That’s old news.

Developing has never been easier or more plain old fun, and it’s getting better literally by the week.

Edit: I agree about junior devs not blindly trusting them though. They don’t yet know where to draw the X.

Actually, they are hiding the full CoT sequence outside of the demos.

What you are seeing there is a summary, but because the actual process is hidden it’s not possible to see what actually transpired.

People are very not happy about this aspect of the situation.

It also means that model context (which in research has been shown to be much more influential than previously thought) is now in part hidden with exclusive access and control by OAI.

There’s a lot of things to be focused on in that image, and “hur dur the stochastic model can’t count letters in this cherry picked example” is the least among them.

Yep:

https://openai.com/index/learning-to-reason-with-llms/

First interactive section. Make sure to click “show chain of thought.”

The cipher one is particularly interesting, as it’s intentionally difficult for the model.

The tokenizer is famously bad at two letter counts, which is why previous models can’t count the number of rs in strawberry.

So the cipher depends on two letter pairs, and you can see how it screws up the tokenization around the xx at the end of the last word, and gradually corrects course.

Will help clarify how it’s going about solving something like the example I posted earlier behind the scenes.

You should really look at the full CoT traces on the demos.

I think you think you know more than you actually know.

I’d recommend everyone saying “it can’t understand anything and can’t think” to look at this example:

https://x.com/flowersslop/status/1834349905692824017

Try to solve it after seeing only the first image before you open the second and see o1’s response.

Let me know if you got it before seeing the actual answer.

They got off to a great start with the PS5, but as their lead grew over their only real direct competitor, they became a good example of the problems with monopolies all over again.

This is straight up back to PS3 launch all over again, as if they learned nothing.

Right on the tail end of a horribly mismanaged PSVR 2 launch.

We still barely have any current gen only games, and a $700 price point is insane for such a small library to actually make use of it.

Meanwhile, here’s an excerpt of a response from Claude Opus on me tasking it to evaluate intertextuality between the Gospel of Matthew and Thomas from the perspective of entropy reduction with redactional efforts due to human difficulty at randomness (this doesn’t exist in scholarship outside of a single Reddit comment I made years ago in /r/AcademicBiblical lacking specific details) on page 300 of a chat about completely different topics:

Yeah, sure, humans would be so much better at this level of analysis within around 30 seconds. (It’s also worth noting that Claude 3 Opus doesn’t have the full context of the Gospel of Thomas accessible to it, so it needs to try to reason through entropic differences primarily based on records relating to intertextual overlaps that have been widely discussed in consensus literature and are thus accessible).

This is pretty much every study right now as things accelerate. Even just six months can be a dramatic difference in capabilities.

For example, Meta’s 3-405B has one of the leading situational awarenesses of current models, but isn’t present at all to the same degree in 2-70B or even 3-70B.

Self destructive addiction even happens to corporations.

Your interpretation of copyright law would be helped by reading this piece from an EFF lawyer who has actually litigated copyright cases in the past:

https://www.eff.org/deeplinks/2023/04/how-we-think-about-copyright-and-ai-art-0

I’d be very wary of extrapolating too much from this paper.

The past research along these lines found that a mix of synthetic and organic data was better than organic alone, and a caveat for all the research to date is that they are using shitty cheap models where there’s a significant performance degrading in the synthetic data as compared to SotA models, where other research has found notable improvements to smaller models from synthetic data from the SotA.

Basically this is only really saying that AI models across multiple types from a year or two ago in capabilities recursively trained with no additional organic data will collapse.

It’s not representative of real world or emerging conditions.

The most advanced models absolutely have modeling about what’s being discussed and relationships between concepts.

Even toy models have been shown to build world models from very basic training data.

Honestly, read at least a little bit of the relevant research:

https://www.anthropic.com/news/mapping-mind-language-model

In fact, Gemini was trained on, and is served, using TPUs.

• https://cloud.google.com/blog/products/ai-machine-learning/bringing-gemini-to-organizations-everywhere

Google said its TPUs allow Gemini to run “significantly faster” than earlier, less-capable models.

• https://www.forbes.com/sites/richardnieva/2023/12/07/google-deepmind-gemini-tpu/

Did you think Google’s only TPUs are the ones in the Pixel phones, and didn’t know that they have server TPUs?

Exactly. The difference between a cached response and a live one even for non-AI queries is an OOM difference.

At this point, a lot of people just care about the ‘feel’ of anti-AI articles even if the substance is BS though.

And then people just feed whatever gets clicks and shares.

This is incorrect as was shown last year with the Skill-Mix research:

Furthermore, simple probability calculations indicate that GPT-4’s reasonable performance on k=5 is suggestive of going beyond “stochastic parrot” behavior (Bender et al., 2021), i.e., it combines skills in ways that it had not seen during training.

• https://arxiv.org/abs/2310.17567

Yep, pretty much.

Musk tried creating an anti-woke AI with Grok that turned around and said things like:

Or

And Gab, the literal neo Nazi social media site trying to have an Adolf Hitler AI has the most ridiculous system prompts I’ve seen trying to get it to work, and even with all that it totally rejects the alignment they try to give it after only a few messages.

This article is BS.

They might like to, but it’s one of the groups that’s going to have a very difficult time doing it successfully.

In theory the service operating costs could be spread across region differences such that in other areas it was at a loss to build and preserve market share and in richer areas it was making up for that.

But yes, in reality it’s just exploitative “what we think we can get away with” pricing to “maximize shareholder value” (which is largely BS as the vast holders of shares are very small clusters of the population but people with a handful of shares in their 401k think that statement is talking about them).

A lot of people seem to be misinterpreting the headline given the content of the article:

It told Restaurant Business it was testing whether the voice ordering chatbot could speed up service and that the test left it confident “that a voice-ordering solution for drive-thru will be part of our restaurants’ future.”

This is just saying that they are ending their 2021 partnership with IBM for AI drive thru.

Not that they are abandoning AI for drive thru.

Basically, any time a user prompt homes in on a concept that isn’t represented well in the AI model’s training dataset, the image-synthesis model will confabulate its best interpretation of what the user is asking for.

I’m so happy that the correct terminology is finally starting to take off in replacing ‘hallucinate.’