Skinner: "Yes."
Chalmers: "May I see it?"
Skinner: "No."
Perhaps it's worth taking a beat and looking at the incredible progress in that year, and acknowledge that whatever's next is probably "still cooking".
Even Meta is still baking their 400B parameter model.
My guess is he wants OpenAI to become a household name, and so he optimizes for exposure.
We’ll see if they have another flagship model ready to launch. I seriously doubt it. I suspect that this was supposed to be called GPT-5, or at the very least GPT-4.5, but they can’t meet expectations so they can’t use those names.
Skinner (looking up): No, mother, it's just the Nvidia GPUs.
Skinner (looking up): "No, mother, it's just the Nvidia GPUs."
"No, mother, that's just the H100s."
But I am not convinced it will be another GPT-4 moment. Seems like big focus on tacking together multi-modal clever tricks vs straight better intelligence AI.
Hope they prove me wrong!
Multimodal is another way to stave off the inevitable, because these AI companies already are training multiple models on different piles of information. If you have to train a text model and an image model, why split your training data in half when you could train a combined model on a combined dataset?
[0] For starters, most YouTube videos aren't manually captioned, so you're feeding GPT the output of Google's autocaptioning model, so it's going to start learning artifacts of what that model can't process.
I'd bet a lot of YouTubers are using LLMs to write and/or edit content. So we pass that through a human presentation. Then introduce some errors in the form of transcription. Turn feed the output in as part of a training corpus ... we plateaued real quick.
It seems like it's hard to get past a level of human intelligence at which there's a large enough corpus of training data or trainers?
Anyone know of any papers on breaking this limit to push machine learning models to super-human intelligence levels?
Whisper models are better than anything google has. In fact the higher quality whisper models are better than humans when it comes to transcribing text with punctuation.
I would expect they’re using their own t2s which is still a model but way better quality and potentially customizable to better suit their needs
Seems to me that performance is converging and we might not see a significant jump until we have another breakthrough.
It doesn't seem that way to me. But even if it did, video generation also seemed kind of stagnant before Sora.
In general, I think The Bitter Lesson is the biggest factor at play here, and compute power is not stagnating.
"This conversation may be recorded and used for training purposes" now takes on a new meaning.
Can car makers sell info from everything that happens in their cars?
However, I think the most cost-effective way to train for real world is to train in a simulated physical world first. I would assume that Boston Dynamics does exactly that, and I would expect integrated vision-action-language models to first be trained that way too.
You take a bunch of mo-cap data and simulate it with your robot body. Then as much testing as you can with the robot and feed the behavior back in to the model for fine tuning.
Unitree gives an example of the simulation versus what the robot can do in their latest video
Releasing models to users that where users can record video is more data. Users conversing with AI is also additional data.
Another example is models that code– And then debug the code and learn from that.
This will be anywhere, and these models will learn from anything we do/publish online/discuss. Scary.
Pretty soon– OpenAI will have access to
When it comes to multi-modality, then training data is not limited, because of many different combinations of language, images, video, sound etc. Microsoft did some research on that, teaching spacial recognition to an LLM using synthetic images, with good results. [1]
When someone states that there are not enough training data, they usually mean code, mathematics, physics, logical reasoning etc. In the open internet right now, there are is not enough code to make a model 10x better, 100x better and so on.
Synthetic data will be produced of course, scarcity of data is the least worrying scarcity of all.
Edit: citation added,
[1] VoT by MS https://medium.com/@multiplatform.ai/microsoft-researchers-p...
If the other attributes of the model do not improve, sure.
For the skeptical, consider that humans can be trained on material created by less intelligent humans.
For the skeptics, "AI models" are not intelligent at all so this analogy makes no sense.
You can teach lots of impressive tricks to dogs, but there is no amount of training that will teach them basic algebra.
I take the opposite view. I don't think video generation was stagnating at all, and was in fact probably the area of generative AI that was seeing the biggest active strides. I'm highly optimistic about the future trajectory of image and video models.
By contrast, text generation has not improved significantly, in my opinion, for more than a year now, and even the improvement we saw back then was relatively marginal compared to GPT-3.5 (that is, for most day-to-day use cases we didn't really go from "this model can't do this task" to "this model can now do this task". It was more just "this model does these pre-existing tasks, in somewhat more detail".)
If OpenAI really is secretly cooking up some huge reasoning improvements for their text models, I'll eat my hat. But for now I'm skeptical.
With less than $800 worth of hardware including everything but the monitor, you can run an open weight model more powerful than GPT 3.5 locally, at around 6 - 7T/s[0]. I would say that is a huge improvement.
[0] https://www.reddit.com/r/LocalLLaMA/comments/1cmmob0/p40_bui...
I'll reserve judgment until we see GPT5, but if it becomes just a matter of who best can monetize existing capabilities, OAI isn't the best positioned.
Improving the instruction tuning, the RLHF step, increase the training size, work on multilingual capabilities, etc. make sense as a way to improve quality, but I think increasing model size doesn't. Being able to advertize a big breakthrough may make sense in terms of marketing, but I don't believe it's going to happen for two reasons:
- you don't release intermediate steps when you want to be able to advertise big gains, because it raises the baseline and reduce the effectiveness of your ”big gains” in terms of marketing.
- I don't think they would benefit in an arm race with Meta, trying to keeping a significant edge. Meta is likely to be able to catch-up eventually on performance, but they are not so much of a threat in terms of business. Focusing on keeping a performance edge instead of making their business viable would be a strategic blunder.
They have the brand recognition (for ChatGPT) and that's a good start, but that's not enough. Providing a best in class user experience, a way to lock down their customers in some kind of walled garden, building some kind of network effect (what they tried with their marketplace for community-built “GPTs” last fall but I'm not sure it's working), something else?
At the end of the day they have no technological moat, so they'll need to build a business one, or perish.
For most tasks, pretty much every models from their competitors is more than good enough already, and it's only going to get worse as everyone improves. Being marginally better on 2% of tasks isn't going to be enough.
I don't know why they didn't think about doing that earlier, could have been a game changer, but there is still an opportunity to pivot.
I have tried Phind and anything beyond mega junior tier questions it suffers as well and gives bad answers.
probably need routers, RAG, and reranking
I think there is a role for LLM + deterministic code gen as well (https://github.com/hofstadter-io/hof/blob/_dev/flow/chat/pro...)
Something that I can just use as easily as copilot. Unfortunately every single one sucks.
Or maybe that's just how programming is - its easy at the surface/ice berg level and below is just massive amounts of complexity. Then again, I'm not doing menial stuff so maybe I'm just expecting too much.
I don't want to have to copy & paste between applications, just let me highlight some sections and then run some LLM operation on it
i.e. a VS Code extension with keyboard shortcuts
I like LLMs for general design work, but I’ve found accuracy to be atrocious in this area.
yup, this is why an LLM only solution will not work. You need to provide extra context crafted from the language or library resources (docs, code, help, chat)
This is the same thing humans do. We go to the project resources to help know what code to write
As a developer, you would want something like this, which has access to all the languages / libraries you actually use