The current crop of benchmarks might not reflect these gains, by the way.
They are by no means bad, but I am now mostly interested in long context competency. We need benchmarks that force the LLM to complete multiple tasks simultaneously in one super long session.
Anyway here's the sales page. the widget subscription is so premium you won't even miss the subscription fee.
We should still be skeptical because often want to claim to be better or have unearned answers, but I don't think the motive to lie is quite as strong as a salesman's.
It's not peer-reviewable in any shape or form.
That said, doing that is slow and people will need to make decisions before that is done.
It's weird. In the late 2010s it seems like people were wising up to the idea that you can't implicitly trust big tech companies, even if they have nap pods in the office and have their first day employees wear funny hats. Then ChatGPT lands and everyone is back to fully trusting these companies when they say they are mere months from turning the world upside down with their AI, which they say every month for the last 12-24 months.
In the 2000s we only had Microsoft, and none of us were confused as to whether to trust Bill Gates or not...
https://www.wheresyoured.at/pop-culture/
> What makes this interview – and really, this paper — so remarkable is how thoroughly and aggressively it attacks every bit of marketing collateral the AI movement has. Acemoglu specifically questions the belief that AI models will simply get more powerful as we throw more data and GPU capacity at them, and specifically ask a question: what does it mean to "double AI's capabilities"? How does that actually make something like, say, a customer service rep better? And this is a specific problem with the AI fantasists' spiel. They heavily rely on the idea that not only will these large language models (LLMs) get more powerful, but that getting more powerful will somehow grant it the power to do...something. As Acemoglu says, "what does it mean to double AI's capabilities?"
I’m about Zucks age, and have been following his career/impact since college; it’s been roughly a cosine graph of doing good or evil over time :) I think we’re at 2pi by now, and if you are correct maybe it hockey-sticks up and to the right. I hope so.
If LLMs end up being the platform of the future, Zuck doesn't want OpenAI/Microsoft to be able to monopolize it.
> Other companies sell widgets. We have a bunch of widget-making machines and so we released a whole bunch of free widgets. We noticed that the widgets got better the more we made and expect widgets to become even better in future. Anyway here's the free download.
Given that Meta isn't actually selling their models?
Your response might make sense if it were to something OpenAI or Anthropic said, but as is I can't say I follow the analogy.
- flex
- deal a blow to Altmann
But it has nothing to do with LLMs (and interestingly enough they aren't opening their recommendation tech).
I mean, going by their own model evals on various benchmarks (https://llama.meta.com/), Llama 405b scores anywhere from a few points to almost 10 points more than than Llama 70b even though the former has ~5.5x more params. As far as scale in concerned, the relationship isn't even linear.
Which in most cases makes sense, you obviously can't get a 200% on these benchmarks, so if the smaller model is already at ~95% or whatever then there isn't much room for improvement. There is, however, the GPQA benchmark. Whereas Llama 70b scores ~47%, Llama 405b only scores ~51%. That's not a huge improvement despite the significant difference in size.
Most likely, we're going to see improvements in small model performance by way of better data. Otherwise though, I fail to see how we're supposed to get significantly better model performance by way of scale when the relationship between model size and benchmark scores is nowhere near linear. I really wish someone who's team "scale is all you need" could help me see what I'm missing.
And of course we might find some breakthrough that enables actual reasoning in models or whatever, but I find that purely speculative at this point, anything but inevitable.
The problem with this strategy is that it's really tough to compete with open models in this space over the long run.
If you look at OpenAI's homepage right now they're trying to promote "ChatGPT on your desktop", so it's clear even they realize that most people are looking for a local product. But once again this is a problem for them because open models run locally are always going to offer more in terms of privacy and features.
In order for proprietary models served through an API to compete long term they need to offer significant performance improvements over open/local offerings, but that gap has been perpetually shrinking.
On an M3 macbook pro you can run open models easily for free that perform close enough to OpenAI that I can use them as my primary LLM for effectively free with complete privacy and lots of room for improvement if I want to dive into the details. Ollama today is pretty much easier to install than just logging into ChatGPT and the performance feels a bit more responsive for most tasks. If I'm doing a serious LLM project I most certainly won't use proprietary models because the control I have over the model is too limited.
At this point I have completely stopped using proprietary LLMs despite working with LLMs everyday. Honestly can't understand any serious software engineer who wouldn't use open models (again the control and tooling provided is just so much better), and for less technical users it's getting easier and easier to just run open models locally.
OpenAI did a good move with making GPTo mini so dirty cheap that it's faster and cheaper to run than LLama 3.1 70B. Most consumers will interact with LLM via some apps using LLM API, Web Panel on desktop or native mobile app for the same reason most people use GMail etc. instead of native email client. Setting up IMAP, POP etc is for most people out of reach the same like installing Ollama + Docker + OpenWebUI
App developers are not gonna bet on local LLM only as long they are not mainstream and preinstalled on 50%+ devices.
In my opinion, they've found that intelligence with current architecture is actually an S-curve and not an exponential, so trying to make progress in other directions: UX and EQ.
https://nicholascharriere.com/blog/thoughts-openai-spring-re...