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Comment by gillesjacobs | Hacker News Reader

The "Foundation models" term has sparked some fiery debate at that workshop and at the core the issue isn't about general or strong AI at all.

The Foundation Models paper is a strategic attempt of the Stanford AI researchers to coopt the field and define a research program for large-scale pre-trained models. Propagating a new term that is slightly hyperbolic is one way to differentiate your work from other's. But it's only marketing.

Watch how all Stanford-adjacent research will now cite the Foundation Models paper instead of the original BERT paper, other surveys of pre-training, or field-specific papers. There are over a 100 authors on that manuscript, it's a cheap and easy way to increase their researcher impact.

Other researchers naturally aren't too happy with this cheap marketing approach.

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This. They want to create hype around large scale model so that they can ask for lots of money to fund this line of research.

Except that I don't think it's targeted at the general public. Rather it's targeted at grant agencies and University administration who are usually extremely myopic and conservative about funding projects that require a large upfront investment (unlike companies like openai).

In fact, I'm sure tons of people in academia are going to be pointing to this paper from now on when writing grants and asking for funding for large scale machine learning projects. Especially because of the credibility lent to it by Stanford and the people who put their names on the paper.

So it's more like a research manifesto for the field.

Taking the politics of grant funding as an immutable prior, wouldn’t this then be a good thing for researchers?

Speaking of "cheap marketing," did you notice how researchers have become willing to continually mislead the public by calling everything they do "Artificial Intelligence"?

When I was in grad school less than 10 years ago, there was an explicit effort to be specific. Call it machine learning, deep learning, adaptive, etc. The field can be called AI and we can publish in AI journals, but you don't make the mistake of thinking that there is such a "thing" as AI.

All that changed after the dump trucks full of money arrived.

Artificial Intelligence is simply a broad term meaning 'the simulation of human intelligence processes by machines'. The term accurately applies to any subtask no matter how small that involves the automation of human intelligence, even if it is just recognizing dogs vs. cat images. Colloquially there is a false equivalence between A.I. and Artificial General Intelligence (AGI), 'full' or 'strong' AI. AGI specifically denotes a system that can learn any intellectual task a human could learn. And such integrated intelligence is purely hypothetical as of now.

I am active in NLP research and I have not encountered an instance of any academic claiming that their work is AGI. And terms and descriptions are usually domain- and task-specific, so not much has changed since your grad school time. Only those that work on grounding and multi-modality make the claim that their work is a step towards AGI. Which is true, as these researchers usually only claim that it is an exploration, an increment towards AGI.

What you are seeing is commercial entities marketing their solutions as AGI. Mass media has given more exposure to that idea because it sounds scifi. Also often journalists let their imagination run a little when describing papers. I agree that is misleading but academics still have the same rigor.

I like the definition you provide: "the simulation of human intelligence processes by machines"

It would, of course, allow AI to exist that predates the invention of computers. Such as autopilot (1914) or the steam governor (1788), etc.

What is considered AI is definitely on a constantly changing continuum. Take for instance the calculator, when introduced it eliminated the need for a team of human calculators who for difficult computations manually cross-checked each step. Some attributed the first commercially available mechanical calculator with human-like intelligence. Over time, such tools become so wide-spread that it is considered an affront to compare it to human intelligence. So now, we don't see calculators and autopilots as AI.

In your definition (which I like), AI is based on intent to simulate rather than perception. I agree that what is popularly considered AI is a moving goal-post — many don't see machine learning as AI either — e.g., it's just statistics.

The term AI is very vague and misleading. It implicitly suggests a kind of magic. A lot of people believe in the magic, intended or not. I don't think this paper would be an issue at all if it was referring to a new foundation for GANs or transformers or deep learning — or anything more specific.

One could also quite reasonably ask what qualifies as a machine. Does an abacus count? Does pen and paper count?

I think that's a foundational question!

Does the sun count or the earth? Or people?

> Artificial Intelligence is simply a broad term meaning 'the simulation of human intelligence processes by machines'.

Sure, but if so, Machine Learning is not AI, because the way a model "learns" is nothing like how humans learn, and gets radically different results.

> the simulation of human intelligence processes by machines

Because of this the term doesn't really apply to the uses you mentioned. We have no way to know if models are simulating anything or not. And most models, even big ones, fail when they have to adapt on new unseen data. It's completely fair to say that AI should have been used to mean AGI.

This is not new or limited to AI. From synthetic biology to big data, grant seekers know that just referencing the current fads in the proposal can increase the probability of getting funded.

At least those researchers know they're bullshitting. Game developers earnestly throw around the term "AI" as if what they were doing was actually AI, and not just meticulously hand-scripted spaghetti code.

To be clear: The researchers of the Foundation Models paper never claim anything related to AGI. They actually put out a decent survey and research avenues for transfer-learning with large-scale pretraining. AGI is only referred to once in the context of AI safety to make an entirely unrelated point. The only gripe with the original paper is with the attempt to coopt the field under their term(s).

It is the journalist in the OP article that jumps the shark towards AGI.

Besides, there is definitely statistics and an engineering art towards 'hand-scripting' game AI. I wouldn't be so dismissive of video game AI.

Sorry, I didn't mean those particular researchers were bullshitting, or that some games don't use legitimate "AI" techniques, but those are orthogonal to the commonly understood meaning of the term "AI" in the game industry, which is "whatever code controls the non-player characters" (regardless of what programming techniques it uses), as opposed to making the grass wave, rendering triangles, vibrating the speaker, networking, user interface, etc.

Games can certainly use legitimate AI programming techniques, like GPUs with "AI Upscaling", but that's not considered "Game AI" (as seen in job descriptions) since it has nothing to do with controlling NPC behavior.

For example, Civilization AI:

https://civilization.fandom.com/wiki/AI

My point is: if you're worried about the term "AI" being hijacked, that ship sailed a long time ago!

Game AI is a perfectly ok name to describe a very specific software application.

It doesn't really matter how the AI is implemented (the famous lots of "ifs"), just what it does. The only quality metric for it is a subjective one about how fun the AI is to interact with.

'Publish or perish' combined with the need for funding. Academia has become distanced from what it's supposed to be, at least in this area.

Much less than citations, it's a basic empirical observation of SOTA for many NLP+vision tasks that, crucially, supports asking for a _lot_ of funding: $$$$ for hw, and skim $$$ off for a big lab of people for a decent amount of time.

Less cynically... they have to do it for going after low-hanging fruit around the current local maxima. I'd expect similar writing happening in grant proposals at other big AI labs too.

Yes, exactly, the Foundation Models paper is basically a grant proposal of research program. Still many object to coining a new (hyperbolic) term for an established field for own gain.

Personally, I will stick to the established 'larte-scale pre-trained models' instead of 'foundation models' in my publications.

So your saying basically they basically did the equivalent of this colloquialism: "If you can't win, change the rules"

Just to increase citation count of their own papers. That does sound pretty dodgy.

Not primarily to increase citation count only (though it certainly will help them). The Foundation Models paper reads more like a grant proposal instead of a survey paper because it basically spells out the author's research program.

What they are doing is coopting field of methodology under a new name and starting a new lab with that name. This has expectedly left a foul taste in the mouth of many colleagues, hence the controversy.

Arguably as soon as you go beyond English, this term starts making more sense. For many languages - especially smaller ones - having a good universal model pretrained on large quantities of good data is literally quite "foundational"; it's very useful as a foundation to build other things on top of it, and it's sufficiently resource-intensive (and also getting appropriate data is labor-intensive and sometimes legally tricky) to make sense to make such models rarely, not forcing everyone to recreate one from scratch.

Having a GPT-3 clone for a new language isn't really novel, but it's foundational work that helps a lot of other research.

Ironically, google kina did the same thing with BERT.. The original insight came from Jeremy Howards ulmfit. The first transfer learning model for nlp. Tranformers are great an all, but everything after ulmfit has pretty much just been the result of increased model size

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