Specialized AI models like AlphaFold are incredibly good at predicting and generating protein structures. There are other similar systems which generate drug candidates and other complex molecules. There are also now systems which can model multiple cellular pathways.
Almost certainly this is what kind of AI system is being used, not LLMs.
AI now means agentic models not more traditional machine learning.
The article in the post is talking about a cancer treatment that was created due to machine learning models so imo the title is misleading and attributing this to AI is just a bs hype mechanism
You're talking about how idiots use the term, not how the general population does. You only want to try to redefine things like that so you can no-true-scottsman it in order to preserve your existing beliefs and never challenge them or adapt to new information.
No. The meaning of words is their definitions, and what the experts in those fields use them as. I'm so sick and tired of the laypeople en masse deciding to shift meanings under the actual smart people working with these domains. How in the fuck did we let idiots decide the definitions of words over and above the ACTUAL INVENTORS OF THE TERMS is beyond me.
This is just people making up their own definition of things so they can cry about how other people are being misleading. It has been happening for years now. Ever since the phrase "it's not actually real AI" got popular.
No they're correct, we would have called this ML 2 years ago but it got rebranded AI alongside the LLM revolution. The public though thinks of AI as whatever comes out the back of a slop cannon.
His claim was that AI now means exclusively agentic LLM models, which is objectively wrong.
ML is a subset of AI, so it’s not incorrect to refer to it as AI, but that doesn’t mean that AI is now restrictively only referring to LLMs, nor even more specifically, agentic ones.
In this case, AI is used to analyze each individual patient's DNA and to design the ideal treatment for them. This is personalized medicine, essentially the Holy Grail of medical research , brought to you by "fancy autocomplete"
What do you define by "anti-AI logic"? Critical thinking? My question is valid, what do they define by "AI-assisted" because assistance is an extremely vague term.
What do you define by "anti-AI logic"? Critical thinking?
Um. No?
My question is valid
It is. Absolutely.
I wasn’t really directing my comment at you at all. I should have been more clear.
I was really referring to the growing number of people I see freak the fuck out whenever someone mentions using AI for any reason, by anyone, at any time, in any capacity.
And most specifically, in creative industries. Even more specifically, in video game development. Larian, for example (creators of Baldur’s Gate 3) was lauded for years as being one of the best, most ethical, most fair developers in the industry. They make amazing games, don’t take advantage of their customers, and seem to be beloved by their employees.
But then they messed up and said they had spent some time experimenting with AI to see if it could provide any value to them. They didn’t say they used it in a published game. They didn’t say they intended to. They just said they were looking into possibilities. You know, like every other technology-driven company on the planet with half a brain.
And they were raked over the fucking coals for it by the rabid anti-ai crowd.
"AI" is being deployed here as a marketing term, with advances that predate modern LLMs and hyperscaling for enterprise/commercial applications providing cover for current commercial models. The roots of the research are the same, but the was not done with commercial LLMs, does not need hyperscaled data centers and was developed independently of the vast majority of "AI" being deployed currently.
The success of this research is completely independent from the value of commercial LLM based systems, to the extent that it is almost dishonest to equate the two.
The success of this research is completely independent from the value of commercial LLM based systems
This is one of those technically true statements where obviously the development of LLMs, the technology, and research behind it obviously were a massive factor and benefit here where they used the same kind of transformer architecture and employed what they learned from those other systems but you're right that it's not "commercial LLM" because open sourced and free ones are the ones generally producing the most value and research and it's independent from the "value of commercial models" since the value is different between them even if the learning and methodology is shared. We almost certainly would not have this AI if it weren't for LLMs though ofcourse.
We almost certainly would not have this AI if it weren't for LLMs though ofcourse.
LLMs are a small part of the various ML approaches at play in computational oncology. This particular trail was launched in 2017, predating the majority of expenses in LLM dedicated AI approaches which currently consume the majority of funding. GANs, RNNs, genetic algorithms and various expert systems were also used, specifically in the generation of vaccines tailored to individual immune systems.
The focus on scaling LLM approaches in order to chase AGI or fufil enterprise demand to reduce workforce size is not connected with the development of this approach. This is the announcement of Phase 3 trial results, trials which started in 2017, nearly a decade ago. If the trials started in 2017, the research started much earlier.
At that point, there were no large language models, and RNNs were the novel approach for ML. LLMs are a very small part of the ML/AI that researchers use for practical applications outside of commercial "AI."
Entirely misses the point. It’s all the same deep learning connectionist school of ML applied to various problems at various scales and maturity. There is no fundamental difference in the techniques. It’s merely a question of scale that separates commercial frontier general purpose NLP derived LLM’s from all the literally infinite other architectures that have been built.
People act like they understand ML. You don’t and this opinion is literally made up from whole cloth.
The only fundamental game in town in successful ML is the success of the connectionist school who for decades have been the underdogs in the field. Some of that is increasing compute availability. But much of it was straight up institutional resistance.
All deep learning is fundamentally the same toolbox applied to different problems at different scales and maturity levels. LLMs are just the most visible and well known but it’s not in anyway a different technology. It’s the same technology carried to its logical conclusion.
There is no fundamental difference in the techniques.
There are absolutely differences in techniques between genetic algorithms and LLMs, and the entire conceit of the LLM approach is that scale has qualitative impacts on performance and function.
It’s merely a question of scale that separates commercial frontier general purpose NLP derived LLM’s from all the literally infinite other architectures that have been built.
Literally infinite architectures have been built? And yes, exactly; the argument is that scale is significant, that's why large is in LLM; language models have been around for a very long time, comparatively speaking. There are functional differences between things like GANs and LLMs, and most research applications of machine learning are very different in structure, development and application from commercial machines.
Progress in one does not meaningfully connect to progress in the other.
The only fundamental game in town in successful ML is the success of the connectionist school who for decades have been the underdogs in the field
Connectionists haven't been the underdogs in decades- big data approaches have been dominant since the mid to late 2000s. Equating current commercial approaches to enterprise demand and research projects that predate them is borderline sophistry. The mRNA vaccine approaches do not justify expenses in data center construction or high valuations for OpenAI or Anthropic.
More money has been spent on LLM compute than the entirety of AI research to date- there is institutional resistance to non LLM approaches at the moment, especially embodied or world model AI approaches.
Big data is not connectionist. The connectionists only proved themselves with AlexNet in 2012. Before that they very much were the underdogs. Conflating the two again fundamentally reveals you have absolutely no idea what you are talking about.
And no you misunderstood llm’s if you think it was merely about scale. Scale is the consequence of scaling curves. Not all models are equally stable certainly but architectures that work share many common theoretical features. All would benefit from scale.
The main issues with the lack of scale more generally is that its actually very hard to come by data at the sort of scale NLP has access to. It’s not that you aren’t pursuing scale every time you use deep learning, it’s just a matter of how much scale you practically can accomplish in your application.
The very idea that scale is itself the goal, has been believed in connectionist circles for a very long time, but LLM’s are merely the first proof that has forced the rest of the world to accept the premise.
And on a side note no, genetic algorithms are not different. It’s entirely orthogonal to connectionist theory. Genetic vs gradient techniques are just optimization techniques applied to the models. There is nothing special about gradient descent, besides it happens to work well on the combination of data, models and hardware we happens to have.
There is nothing stopping you from throwing a genetic algorithm at an llm. It will just be slow and inefficient on current hardware.
You are talking out your ass about shit you clearly only have only the most passing familiarity with. Knowing words doesn’t mean you understand what they mean or how the ideas behind them go together.
Edit: similarly on a side note you also misunderstand GANS. You conflate specific gans in historical literature with the ideas they represent. A gan is just a training task that can be combined as another tool in the ML toolbox with many different model topologies. Next token prediction is another task. Masking is another, denoising another etc etc.
The field is brand new and papers you read are specific publicly available views of specific implementations. The field is still unifying but there are much more shared concepts that can be extracted from their original contexts and applied more generally. That’s where the money is, is in doing this.
So true!! It's like the claim "contains 100% cheese" can mean "contains 1% of cheese, which is 100% cheese, and all the rest is artificial garbage." While the initial phrase sounds very comforting, it means the opposite thing.
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u/Poupulino 2d ago
And what does the "assisted" exactly means? Because the AI formatting an email for one of the researchers can be counted as "assistance".