Resistance training should be a form of stretching. If you're taking the loaded muscle through full ranges of motion, as you should be, then that also means ranges where the muscles are fully stretched. Unfortunately many people cut the range of motion short so they can add more load, which is counterproductive for both flexibility, injury risk and progress.
We're already doing those experiments. Tailor made mRNA vaccines targeted to one's own specific cancer mutations can be bought right now. The techniques are getting more sophisticated and more targeted every year. Our ability to predict what happens at these levels is improving by leaps and bounds too thanks to AI like AlphaFold. Anyone can be reasonably confident that no AI or teen can do this now or in the next few years, but are you really so confident what might be possible in 10 years?
I worked in immuno-oncology: cancer vaccines work in melanoma where many things work because of the neoantigen abundance, they've generally been very mixed in efficacy. And even BionTech's BNT111 failed in melanoma! We have hope for these approaches but the reality of this stuff is way more nuanced than you think it is.
Alphafold can't reliably predict thermal energy landscapes or make functional predictions - and how could it? It wasn't trained on anything that could capture structure - function relationships.
Again, most people just have no idea how hard - fundamentally hard - molecular physics is to predict, and how necessary experiments are for any development of biological systems.
> We have hope for these approaches but the reality of this stuff is way more nuanced than you think it is.
Sure, everything has more nuance. The point is this stuff is available now; this isn't some future sci-fi, it's only going to get better, it's not the only research on gene targeting, and AI is starting to help with this research. By the time AGI is actually here, consider the breadth of knowledge and capabilities that will be at its disposal.
> Alphafold can't reliably predict thermal energy landscapes or make functional predictions - and how could it? It wasn't trained on anything that could capture structure - function relationships.
If your point is that the only reason an AI like AlphaFold can't make functional predictions is that we haven't trained an AI to do that, then unless you're arguing we can't or won't ever do that, I'm not sure how that's supposed to be an objection to the argument that AI will be able to make use of this information without doing all of the experiments people seem to think would be necessary.
Like I said, we're already going to be doing these experiments because it's useful to us, and we will train AIs to make these predictions, again, because it's useful to us. Stop imagining what an AGI has access to now, and start thinking what it will have access to with the inevitable march of progress that we're already on.
Edit: and of course, this doesn't even take into account the fact that an AI could acquire resources to pay people to do this research. The internet provides ample opportunities like this now.
We're doing that science right now. We're using AI to do it. By the time AGI truly spreads, we'll have a lot of the knowledge you say will be needed. This is simply not as far fetched as you seem to think. Next few years, unlikely. 10-15 years? Increasingly possible. 20+ years, I'd hazard even likely.
> The said user (Tristan Buckmaster) didn't solve the millennium problem. He didn't really accuse that OpenAI stole his research either. The beef came from the fact OpenAI asked him to remove another mathematician, who works for Anthropic, from the credit.
Not quite accurate, Buckmaster was taking an approach that nobody else was, and this new proof uses this same approach just weeks after he saved those results to OpenAI workspaces. He asked OpenAI if they used chat logs for training the new model, and they did not confirm or deny.
Asking to remove his collaborator is also totally over the line though.
Wow, this sentence is doing a lot of work in that tweet: "While unlikely, we cannot rule out that de-identified data derived from their usage of our products helped improve our models."
Yes, both the output should be "milestones" of sorts, like lemmas and theorems in math. Important plateaus that serve as a launching pad to the next phase. Regurgitating every thought potentially degrades signal:noise ratio.
Actually, how does chain of thought work? Is the LLM actually creating the tokens and then re-reading them, or is the there still a full hidden state under the hood and then the UI just prints that projection?
Can't tell you what's happening in a closed model, but in case of open ones it's just a text stream, same "take all previous tokens, compute next token" mechanism applies. Thinking vs Response is just a state change like between a system message and a user message.
Closed models probably do the same thing internally. What is shown externally is different though: you get a summary of the chain of thought, not the thoughts itself. This is done to prevent distillation.
The latest look we had at a frontier chain of thought is probably in the Huggingface incident report - I haven't actually read it yet but I saw the BlackHat talk, and it included some snippets. The thoughts look like they are approaching neuralese. The words are still understandable but the grammar is weird, simplified. In comparison, Qwen 3.8 27b thinks in valid English.
It's not easy to test as there is no formal definition or formal criteria for AGI, only exclusionary criteria like "not X". That's why he phrased it that way, he's saying it's going to be clear with hindsight once we have a better understanding of things that this time and/or this model will be the inflection point of AGI.
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