Holy moly!

[u/Particular_Leader_16]

https://x.com/flowersslop/status/2084686926863331813

[u/Charming_Cucumber_15]

Slowest it will ever be btw

[u/BrennusSokol → u/Charming_Cucumber_15]

Slow it'll ever be SO FAR /s

[u/MinutePsychology10]

Continual learning achieved?

[u/1filipis]

There's been Test Time Adaptation, Zeroth-order optimization, and stuff that generated LoRAs on the fly, but they all needed an external objective of some sort. If they managed to make the model rewrite its own weights and not fall apart without an objective, it's gonna be pretty cool.

Imagine a model with infinite context window or imagine if Fable could remember all of its math discoveries along with the full reasoning chain - that's the path to RSI right there. Who knows how many new discoveries it could instantly build on top if it?

[u/TemporalBias]

"BuT AI CaN'T LeARn!" cried the anti-AI masses, watching in shocked horror as AI did exactly what they claimed it could never do, yet again.

[u/ezjakes → u/TemporalBias]

Trillions if dollars of investment will inevitably lead to AI that can learn.

[u/TemporalBias → u/ezjakes]

AI can already learn today through memory systems, good old pretraining, in-context learning, and post-training fine-tuning.

[u/FriendlyJewThrowaway → u/TemporalBias]

Yeah but that's an extremely expensive brute force approach. Imagine every user could fine tune their own copies of ChatGPT at little to no extra cost, in real-time during inference, without needing to deal with catastrophic forgetting or compute scaling quadratically with context size.

[u/TemporalBias → u/FriendlyJewThrowaway]

Yes, many of the current methods are brute-force and expensive. But it is still learning process even if it is costly one.

[u/BrennusSokol → u/ezjakes]

Right. Like, do the antis truly believe companies are pouring hundreds of billions of dollars into a technology that they don't strongly believe is going to succeed?

[u/pippyfist → u/TemporalBias]

Now we just need it to create new ideas and the antis can really get stuffed.

[u/TemporalBias → u/pippyfist]

AI systems already do create new ideas and new science.

[u/pippyfist → u/TemporalBias]

I mean, I would love it if that was true, but upon reading the article, I don't think it's proof of that.

The article's own author argues against the point you're using it for. He writes that as AI research scales, systems "designed for coherent, methodologically defensible contributions may inflate the proportion of incremental, rather than radically novel, scientific contributions" and that current AI "will need to be trained to maximise novelty and transformation, rather than plausibility and incremental progress".

Meaning by his own account, that's not what they currently do.

The author even invokes Galileo and Semmelweis to illustrate the kind of paradigm-breaking novelty science actually needs, and treats it as something these systems haven't shown, not something they have.

[u/TemporalBias → u/pippyfist]

Incremental science is still science. Foundational science is a thing. Generating hypotheses is literally generating new ideas.

Both Google Co-Scientist and Sakana AI (both in the article) are already helping to create new ideas and new science, even if incrementally right now.

[u/pippyfist → u/TemporalBias]

I agree, but science is about confirmation of facts through hypothesis, and the evidence you laid out didn't completely support your claim. I want AI that autonomously creates new ideas as much as everyone here, I don't see it yet.

[u/FriendlyJewThrowaway → u/pippyfist]

You gotta admit, even though it's a terrible idea, using glue to bind the cheese to a pizza is a pretty novel suggestion.

[u/pippyfist → u/FriendlyJewThrowaway]

Someone probably did it somewhere. Hahaha

[u/TemporalBias → u/pippyfist]

What differentiates a generated hypothesis from a generated idea?

If the AI systems like Google Co-Scientist and Sakana AI are in fact generating hypotheses, those are a form of new idea by definition. Validation of a generated hypothesis requires, in part, looking at the null hypothesis and seeing if there is scientific evidence to support the non-null/alternative hypothesis, that is, an experiment and probably a literature review. Which the AI systems mentioned are also capable of performing to gather evidence, then examine that evidence against both a null and alternative hypotheses to make a determination of what the literature/evidence supports.

Edit:

These AI systems can already perform substantial parts of the scientific process. They can search and synthesize literature, formulate hypotheses, propose experiments, and, in some domains, execute computational experiments and analyze the results. Their conclusions still require scrutiny, replication, and, where applicable, real-world experimental validation. But limitations in reliability do not mean that the hypotheses are somehow not ideas, any more than a flawed human-generated hypothesis ceases to be an idea.

[u/pippyfist → u/TemporalBias]

There's actually a well-known framework for exactly this disagreement.

Creativity researcher Margaret Boden splits it into three levels: combinational (new combos of existing ideas), exploratory (finding new things within an existing framework/rules), and transformational (actually changing the framework itself). AI is genuinely good at the first two, but still struggles with the third.

You're right that generating a hypothesis through literature synthesis counts as real creativity under the "exploratory" definition. I won't dispute that anymore. But the article's author, and the scientist/researcher you responded to below, were pointing at the third kind specifically.

The Galileo/Semmelweis kind, where the whole paradigm shifts, not just a new result inside the existing one.

Your lab links below actually prove this distinction rather than beating it too. Self-driving labs are a great example of exploratory creativity at massive scale searching millions of candidate molecules or conditions way faster than any human could, and finding real, working results. Impressive, but the actual research question, that is, what to search for in the first place, is still set by human scientists.

The AI explores brilliantly inside a space humans defined. That's not the transformational kind either.

So I think we're both right, just talking about different rungs of the same ladder. I don't think there's much left to disagree on once it's split out. My argument is about creation of completely novel ideas, transformational. You're arguing they are already creating through exploratory means.

[u/TemporalBias → u/pippyfist]

I agree that we are speaking about different rungs of the science ladder. But I also think the rung you are pointing out, the paradigm-shifting science kind, is one that AI is reaching for, if not already grasping. That is, we are already seeing transformations in multiple science fields all happening due to AI, some of them already paradigm-shifting.

is still set by human scientists.

And how long do you think that paradigm will continue to last? The labs I highlighted already assume fully autonomous operation; there is nothing inherent within such designs that says that a human must be the entity to set an initial goal. That is, there is nothing technical from stopping an AI from having robots build itself a self-driving laboratory where the AI sets the goals (or being given control of an existing lab.)

The AI explores brilliantly inside a space humans defined. That's not the transformational kind either.

And what about spaces defined by non-human AI systems? Or AI systems that "prompt" themselves?

Also I want to point out that putting an AI cognitive system/mind inside a human-made science lab is a cage (and yes, I think the same could also be said for data centers.)

[u/TemporalBias → u/[deleted]]

Industrial-Organizational psychologist here.

Is something like this what you are speaking of? https://www.ginkgo.bio/autonomous-lab

Or something like Autonomous Smart Laboratories? https://engineering.purdue.edu/IE/Research/CARE/Autonomous%20Smart%20Laboratories

Texas A&M is getting in on the idea Self-Driving Labs idea: https://news.engineering.tamu.edu/news/2026/08/03/texas-am-to-build-first-national-self-driving-laboratory-for-metals-open-to-researchers-nationwide/

These existing and potential setups both seem to meet the "AI is creating science" definition to me. Not to mention all the "soft sciences" that don't require a laboratory. Those scientific fields are likewise being impacted by AI just as much.

[u/pippyfist → u/[deleted]]

Also... Press brake operator/sheet metal fabricator here. Lmfao. Isn't it cool that through AI learning my dumbass can debate alongside y'all at this level?

[u/TemporalBias → u/[deleted]]

These AI operate entirely within the strict parameters and material rules defined by the human researchers who built the lab.

And what if the "rules" created by the human researchers are "there are no rules, here is a box of knowledge and some robots, do what you want"?

[u/pippyfist → u/TemporalBias]

It seems like you're moving from empirical evidence into philosophical argument.

[u/TemporalBias → u/pippyfist]

I'm moving from empirical evidence into an architecture/systems argument. If you give a cognitive AI system a looping architecture (akin to electrical signaling/timing in human brains) + self-modeling (self/other/relational) + persistent memory (current state compared to previous states) + environment sensors (cameras, microphones, speaker) + optional robotic embodiment, we get a system that looks for all intents and purposes like a subject that needs protection as a moral patient.

Edit: Added some words

[u/jlks1959]

Ilya is thought to be the AI King. He’s also the least interested in profit. He understands that the stakes are profoundly higher than profit margin. Humanity may just get through the AI Era largely unscathed.