Well, they say they don’t train on your data. But they must be doing something with it. I have no basis to say it whatsoever, but I simply don’t trust that they don’t use the things you upload. IMO, if you upload PDF financial reports or letters from your lawyer, that’s too juicy to pass up.
Mac Studio is the best bang-for-buck way to run large models. I have one with 128GB unified memory and it runs GPT-OSS-120b really well.
Agreed 100%
And yes, once a conversation goes off the rails, it can never, ever be recovered. Just start a new chat with a fresh context window.
I simply can’t believe this. It sounds like hype of the highest magnitude. End of the day, the “AI” is a calculator for words. I think specialised models will be companions and tools for doctors but I just can’t see replacement. That said, I don’t have 9 figures invested in it haha
Sure, but 3 years ago if I told you that by Jan/2026 over 800 million people would be using AI every day and we’d be investing $4 Trillion + in compute infrastructure per year, you wouldn’t have believed that either
That’s true, but it is indeed an incredibly fast calculator, which mimics the calculating abilities of the brain. At a vertiginous speed, exploring a myriad of solutions and elaborating the most plausible ones.
Does it make it an infallible entity? NO
Does it make it a second powerful brain, subject to control? YES
Or perhaps there is just a lot of unease around the entire AI industry…
Ira Glass, who hosts the NPR show “This American Life,” is not a computer scientist. He doesn’t work at Google, Apple or Nvidia. But he does have a great ear for useful phrases, and in 2024 he organized an entire episode around one that might resonate with anyone who feels blindsided by the pace of AI development: “Unprepared for what has already happened.”
Coined by science journalist Alex Steffen, the phrase captures the unsettling feeling that “the experience and expertise you’ve built up” may now be obsolete – or, at least, a lot less valuable than it once was.
Whenever I lead workshops in law firms, government agencies or nonprofit organizations, I hear that same concern. Highly educated, accomplished professionals worry whether there will be a place for them in an economy where generative AI can quickly – and relativity cheaply – complete a growing list of tasks that an extremely large number of people currently get paid to do.
Seeing a future that doesn’t include you
In technology reporter Cade Metz’s 2022 book, “Genius Makers: The Mavericks Who Brought AI to Google, Facebook, and the World,” he describes the panic that washed over a veteran researcher at Microsoft named Chris Brockett when Brockett first encountered an artificial intelligence program that could essentially perform everything he’d spent decades learning how to master.
Overcome by the thought that a piece of software had now made his entire skill set and knowledge base irrelevant, Brockett was actually rushed to the hospital because he thought he was having a heart attack.
“My 52-year-old body had one of those moments when I saw a future where I wasn’t involved,” he later told Metz.
I used Claude 4.5 but it din’t impress me too much. It mistook a calculation whereas other models got it right (the best accuracy was exhibited by the open source KIMI-K2). I’m sure it’s the best option in coding though.
As far as Cowork goes, the specialized channels are abuzz with it, but truth is that presently, I and other users wouldn’t find it very useful in everyday’s life and work. Organizing files in the local HD? That’s great, providing you have files which you have an idea how to organize them!
However, I agree that it is a huge leap forward compared to developing agents by time-consuming platforms like N8N and similar tools.
" Scientific progress depends on the ability of researchers to synthesize the growing body of literature. Can large language models (LLMs) assist scientists in this task? Here we introduce OpenScholar, a specialized retrieval-augmented language model (LM)1 that answers scientific queries by identifying relevant passages from 45 million open-access papers and synthesizing citation-backed responses. To evaluate OpenScholar, we develop ScholarQABench, the first large-scale multi-domain benchmark for literature search, comprising 2,967 expert-written queries and 208 long-form answers across computer science, physics, neuroscience and biomedicine. Despite being a smaller open model, OpenScholar-8B outperforms GPT-4o by 6.1% and PaperQA2 by 5.5% in correctness on a challenging multi-paper synthesis task from the new ScholarQABench. Although GPT-4o hallucinates citations 78–90% of the time, OpenScholar achieves citation accuracy on par with human experts. OpenScholar’s data store, retriever and self-feedback inference loop improve off-the-shelf LMs: for instance, OpenScholar-GPT-4o improves the correctness of GPT-4o by 12%. In human evaluations, experts preferred OpenScholar-8B and OpenScholar-GPT-4o responses over expert-written ones 51% and 70% of the time, respectively, compared with 32% for GPT-4o. We open-source all artefacts, including our code, models, data store, datasets and a public demo."
The pioneer behind Google Gemini is tackling an even bigger challenge—using AI to ‘solve’ disease
Hassabis and I are meeting not far from where he grew up—at the UCL Observatory, near telescopes more than a century old and still raised to the sky. It’s a fitting place to talk about vastness, not just of the stars but of ourselves.
It’s also a fitting place to talk with someone who’s famous for devoting his own consciousness to finding meaning in vast fields of data. Hassabis is one of the most important AI researchers and entrepreneurs of our time: He’s the cofounder of DeepMind, the pioneering AI lab that was acquired by Google in 2014. In 2016, DeepMind’s AlphaGo marked a seminal moment in AI by defeating the world’s best player in Go, one of the world’s most challenging two-player strategy games. More than a decade later, Hassabis leads Google’s core AI operations, helping to steer the giant at a time when it’s clawing its way to the front of the competitive pack on the strength of its Gemini 3 model.
But his most consequential work to date, perhaps, is the development of AlphaFold 2—an AI system, unveiled by DeepMind in 2020, that could successfully predict the three-dimensional structures of proteins from their DNA sequences. AlphaFold 2 was a generational scientific achievement with implications for better understanding and even curing diseases like Parkinson’s, muscular dystrophy, and certain cancers, all of which stem from misfolded or malfunctioning proteins. It won Hassabis and DeepMind scientist John Jumper the 2024 Nobel Prize in Chemistry; that same year, Hassabis was knighted.
To Sir Demis, it’s all connected. His early fascination with the skies has through lines to AI, finding order and meaning amid seeming randomness.
“The night sky is a mystery that’s staring us in the face all the time,” he says. “It’s a constant reminder of the bigger questions. I think that is how I got into vastness…You’ve got to find patterns in huge amounts of data, or find the right move in huge amounts of possibilities.”
Hassabis, for the past few years, has been devoting an important share of his 100-hour workweek to one of the world’s greatest pattern-recognition problems: drug discovery. In 2021, with funding from Google parent Alphabet, Hassabis started Isomorphic Labs, an AI drug-design company that aims to create new, breakthrough medicines for some of the most “undruggable” diseases—with the hyper-ambitious goal, as the startup’s splashy tagline puts it, to “solve all disease.”
Sometimes the AIs give better suggestions than the top specialist in town, at least in some details (personal experience). Sometimes they are more limited. But apparently progressing fast, very fast.
I agree. They are hit and miss IMO. ChatGPT has given me some really useful, accurate information. But it’s also told me a bunch of misleading BS. The problem is, it still requires you to be a diligent user to figure out which is which, and even then it can be challenging.
On top of that, all of the commercial models are highly censored and restricted. They are designed to manage your feelings and experience. People might remember back when they first launched, they would always say “as an AI large language model, I cannot provide…”. Users absolutely hated that disclaimer happening all the time, so the models have now been told not to use the disclaimer, and to try and provide you with information. However, they are still not allowed to give medical advice beyond generic comments. That means, they will knowingly with-hold information or even lie to you if they think it’s best.
I absolutely believe that LLMs could be better doctors than the majority of doctors. There’s no logical reason that they shouldn’t, because they can devour literature, synthesise massive amounts of experience and case studies etc. They can be better educated than any doctor could ever dream of being. I would also expect that lots of image analysis (CT, MRI, CTCAs etc) will be done by AI vision models because they can be extremely accurate.
However, it is unlikely IMO that this will happen in the way that most people are currently using LLMs (i.e. through a browser or an app, running the model on OpenAI servers.) OpenAI or Anthropic have pretty much no incentive to provide ChatGPT or Claude to act as your doctor, because they open the door to bad press and potential liability. OpenAI has been sued already because the models indulged people talking about suicide etc, and this will only continue to get worse IMO.
What I can tell you is that, in my limited experience, these models presently will provide you with advice, sometimes very sound advice.
The most successful example? My own intermittent vasomotory rhynitis. Exaggerated running nose for some periods, from one day to one week, extremely bothersome.
It was diagnosed by the best specialist in town, who prescribed a corticosteroid spray. I didn’t take it, I just forgot about it after a few months of lack of symptoms.
But when they returned, I asked the LLMs, which provided a simple procedure and pinpointed the MOST critical aspect: The rhynitis may be purely a running nose (rhynorrea) or may be plugged, congested nose. Either case has a preferred drug advised (anticholinergic the former, corticosteroid the latter). The best specialist in town, maybe because he was a little distracted, maybe because I wasn’t very clear, prescribed to me the latter, which does not fit my main symptoms.
The LLM (I don’t remember if it was ChatGPT5.2, or Gemini 3 pro, a few months now) advised me so thoroughly and precisely that I decided to follow its advice (after having consulted my pharmacist) and so far the rhyinits has never come back with its full brunt. Hopefully it will never return.
I attached the response in PDF, since it is a very interesting case of a successful advise.
In other instances the asnwers have been less helpful, for example in the difficult practice of psychofarmacology, it will strictly relate to procedures and literature so far, with some specific tips, but it lacks the detail I would expect from a top, very experienced psychiatrist.
But I’ll tell you what, even experienced psychiatrist in the field I’m interest into (autism psychofarmacology) provide empirical advise and clearly proceed by trial and error and sometimes they do not give the necessary details on drug-switching, side effects and so on.
However, I have followed the advice of AI on drug switching schemes (a tricky aspect in psychofarmacology), when the matter is important to me, I’ll interrogate the main 4 AIs, they are often unanimous in their answer, with subtle variations.
The latest Gemini 3.1 Pro release has been extolled for its reasoning capabilities, in maths, physics and elsewhere. Some guys in the specific field (for example Dr. Nate B. Jones) are of the opinion that Google issued a model which has the intrinsice reasonign capabilities of the famous Alpha-fold model, which gained the Nobel prize to Demis Hassabis, Google’s Deepmind director.
So presently, I’m watching it closely (together with other models and especially so the Grok 4.20 beta release with its 4-agents architecture). In a while, maybe htey’ll be able to provide answers based on first principles which are still eluding researchers.
My latest attempt was incredible. gemini 3.1 pro, on my request, elaborated a mathematical model on the time-response of the upstream signals of m-TOR. It went actually beyond my request, providing a quantitative, personalized, actionable scheme with monitoring strategies. A precise optimization procedure on how to alternate on/off upstream signaling to bend m-TOR signaling to improve longevity without compromising the immune system and the integrity of musculoskeletal tissue. It has been amazing. It deserves its own thread though.
