I think they spin up a container if there isn't one running. Usually there isn't, so you have to wait a minute or two. Then it works slowly, but it is simpler than downloading the model.

In this paper the HuggingGPT system uses a bunch of local models, and calls on the HuggingFace API for the rest. So they try to run their own tool-models, at least a few of them because HF is so flaky.

I think this paper is pretty significant. It expands the OpenAI Plugin concept with AI-plugins. This is great because you can have a bunch of specialised models combined in countless ways, chatGPT being the orchestrator. It's automated AI pipelines. If nothing else, it could be used to generate training data for a multi-modal model like GPT-4. Could be a good business opportunity for HuggingFace too, their model zoo is impressive.

If you stop the regular people from using AI then only criminals and government will use it. How is that better? And you can't stop it because a good enough AI will run on edge/cheap hardware.

To be practical about disinformation it would be better to work on human+AI solutions. Like a network of journalists flagging stories and then AI extending that information to the rest of the media.

You should see the problem of disinformation as biology, the constant war between organism and viruses, the evolving immune system. Constant war is normal state, we should have the AI tools to bear the disinformation attack. Virus and anti-virus.

HuggingFace is the GitHub of AI. It hosts 166,392 AI models and 26,787 datasets. It has implementations for all the models in its own framework and is usually the starting codebase for research papers. You can also interact with many models right on their website in the "spaces" section.

You can also see it like an App Store for AI, you can shop for models and then include them in your project with 5 lines of code.

No, it's a serious paper. They can orchestrate hundreds of models from HuggingFace through chatGPT. That's like AI plugins for AI chat.

People affectionately call it "Brad"

You don't understand, even a model well tuned by OpenAI to be safe, if it gets in the hands of the public, will be fine-tuned to do anything they want. It doesn't matter what politicians do to regulate the big players.

The only solution to AGI danger is to release it everywhere at once, to balance out AGI by AGI. For example the solution to AI generated spam and disinformation is AI based detection, humans can't keep up with the bots.

I think the social component of AI is picking up steam. What I mean is the culture around AI - how to train, fine-tune, test and integrate AIs in applications, how to mix and match the AI modules, this used to be the domain of experts. Now everyone is assimilating this culture and we see an explosion of creativity.

The rapid rate of AI advancement is overlapping with the rapid rate of social adoption of AI and making it seem to advance even faster.

12h later edit: This paper comes out HuggingGPT: Solving AI Tasks with ChatGPT and its Friends in HuggingFace . AI is orchestrating AI by itself. What can you say?

> The next phase shift happens when artificial systems start doing science and research more or less autonomously. That's the goal. And when that happens, what we're currently experiencing will seem like a lazy Sunday morning.

At CERN in Geneva they have 17500 PhD's working on physics research. Each of them GPT-5 or higher level, and yet it takes years and huge investments to get one discovery out. Science requires testing in the real world, and that is slow and expensive. Even AGI needs to use the same scientific method with people, it can't theorize without experimental validation. Including the world in your experimental loop slows down progress speed.

I am reminding people about this because we see lots of magical thinking along the lines of "AGI to ASI in one day" ignoring the experimental validation steps that are necessary to achieve this transition. Not even OpenAI researchers can guess what will happen before they start training, scaling laws are our best attempt, but they are very vague. They can't tell us what content is more useful, or how to improve a specific task. Experimental validation is needed at all levels of science.

Another good example of what I said - the COVID vaccine was ready in one week but took six months to validate. With all the doctors focusing on this one single question, it took half a year, while people were dying left and right. We can't predict complex systems in general, we really need experimental validation in the loop.

I'd rather fine-tune the LLM on company documentations than feeding it through retrieval. Does anyone have experience with fine-tuning GPT-3 on a new text? Can it answer questions or freely use information from this text?

Often it seems that the model can't properly synthesise information from a bunch of snippets, it lacks the context of those snippets, so it will combine the information incorrectly or hallucinate an explanation.

Retrieval + loading data in the context is far from solved.

> its own output is its only working memory

All the fantastic feats LLMs can do are thanks to context conditioning.

ML people spend all day thinking about model limitations and errors, it's only normal that we are not so easily swayed by a non-peer reviewed paper declaring first contact with AGI. Especially from MS who owns 50% of OpenAI

Let the critics critique, it's better to have an adversarial take for everything, when you take a survey you get better calibration that way.

He's angry for the forced Gallactica retraction, followed by chatGPT success. Both models had hallucination issues but his model was not tolerated well by the public.

In short, build around LLMs and with LLMs, but don't compete directly with them.

> Generalized 😓

This paper scared me more than any other ML paper. I hoped we have 2-3 more years until what they show in there.

> Does this mean developers/humans don't have AGI?

The intellect of our species isn't universal, we're merely experts at self-preservation and propagation. Take, for instance, chess – it isn't our forte, and even a small calculator could outperform us. Our minds are incapable of 5-D visualization, and we struggle to maintain over 10 unrelated items in our immediate memory. Generally, we falter when addressing problems where the initial move relies on the final steps, or situations that don't allow for linear progression, such as chess or mathematical quandaries. It took us centuries to decipher many of these enigmas. Our specialization lies in tackling human-centric challenges, rather than all-encompassing ones. Evolution simply hasn't had sufficient time to adapt our cerebral cortex for mathematical prowess.

The original title of the "Sparks of AGI" paper was "First Contact With an AGI System" (line 8). If you carefully read the paper it suggests GPT-4 is stronger than what seems to be our consensus.

Doesn't autoregressive decoding cache the states for the previous tokens when decoding a new token?

> Perhaps get augmented with some sort of LSTM architecture where state can be built up from a theoretically infinite amount of input

That would be sweet, infinite input. Does RWKV do it?

You're mistaken, Olympiad problems require bespoke tricks that don't generalise from problem to problem. It's not a problem of breadth of knowledge, they don't test memorisation.

> I am saying we don't know what consciousness is because we're missing information and we don't know what information we're missing

I take a practical definition - without it we can't even find the mouth with the hand to eat.

What about data generated from Alpaca, is that unrestricted?

OpenAI has first hand RLHF data. Alpaca has second hand. Wondering if third hand is good enough and free of any restrictions.