I really doubt a "mirror life" bacteria would be "hidden" from the immune system. Antibodies should have enough search space to be able to detect and neutralise such bacteria?
The adaptive immune system is highly capable and uses a range of techniques to identify and eliminate molecules it has no prior experience with.
In one example, the mirror molecules would likely cause some sort of localized inflammation or other lesion, and then the immune system would be attracted to the area, generating trillions of combinations of randomized protein sequences. If within that trillion there is just 1 antibody that can bind to the mirror life, the immune system will duplicate that molecule many times, and they will bind to the mirror molecules, recruit other proteins that will isolate and degrade them, and then remove them from the body.
Due to the complexity of protein structures, antibodies can bind to mirror molecules.
Every time I show a computer scientist this system: https://en.wikipedia.org/wiki/V(D)J_recombination they remark, "Wait, nature uses self-modifying code? It generates trillions of random combinations, shuffles them around, finding the best ones, and then amplifies them? You know that sort of sounds like this CS technique called genetic algorithms" at which point you just stare at them until the exclamation point above their head goes "BINGO"
Most of the key molecules of life are chiral, existing in one of two mirror-image structures; life uses only one. Receptors and enzymes using one cannot generally recognize the other. Most 'wrong-handed' chiral chemicals are like having a key cut in a mirror image where the index slot and the pins are cut on the wrong side; it won't even fit in the lock.
It could plausibly turn out there is so little ability for the different chiral forms to interact that 'mirror life' would not be a threat. It could also turn out to the extent they could interact, regular life could also detect and evolve how to counteract the novel pathogens in time.
And even if the interaction is low so they aren't highly infectious, the lack of ability to recognize the 'mirror-life' means regular life cannot attack it so mirror life grows unchecked and consumes resources needed for all life.
But there are so many unknowns, and so many unknown unknowns, and such absolutely catastrophic consequences for life on earth, that relying on ALL of those possible success modes working is stupidity beyond insanity.
This is probably the biggest example of "just because you can does not mean you should".
"A sufficiently stereochemically alien pathogen could, in principle, place essentially all of its important antigenic determinants outside the effective recognition repertoire of a conventional adaptive immune system."
With perfect knowledge of the immune system and the capability to create the pathogens of our dreams it would most likely be possible to create this pathogen of our dreams.
Making an entire self replicating mirror organism that would both have this property of "being invisible to the specific immune system" and AND being an effective pathogen is far from a solved problem though. It's further away as useful quantum computing!
We don't even have self replicating mirror cells, just some synthetic Frankensteins.
I find it really hard to believe that you can make a whole organism that doesn't have any molecules that can be targeted by an antibody. And if you can do that then why go through mirror life anyway?
A reasonable argument is that mirror proteins will evade the usual degradation+antigen presentation pipeline and this will make efficient adaptive response more difficult, or that the innate immune reaction will not be enough to recruit adaptive response. But surely it's not impossible. And if we ever get to this point I'm very optimistic that we would have a good way to vaccinate against this.
> I find it really hard to believe that you can make a whole organism that doesn't have any molecules that can be targeted by an antibody.
We just don't know yet, but I want to believe because I think it's exciting. Protease failure, transport machinery failure, MHC binding failure, they are all stereospecific after all... So it's possible.
> And if you can do that then why go through mirror life anyway?
Because our body can produce antibodies against all "classical" forms of life. It's probably impossible to produce classical pathogens that are fully invisible to the immune system. That's the whole point and danger of mirror life in the context of pathogen creation. Plenty of classical pathogens were our antibodies (usually) don't help already exist though: HIV for example, where only very few people are lucky enough to be elite neutralizers. If we wanted to do biological warfare, it would probably easier to enhance those viruses.
> And if we ever get to this point I'm very optimistic that we would have a good way to vaccinate against this.
I am not so sure about this, if the bodies molecular machinery is unable to produce antigens for those pathogens, no classical or mrna vaccine is gonna trick the body into producing them, it's just technically impossible. You'd need to "update" the whole immune system to be able to manufacture the mirror antibodies and from that we are an eternity away. The only remotely feasible cure in such a world would be through infecting the body with another mirror-pathogen that produces mirror-antibodies or directly injecting mirror-antibodies...
Care to elaborate? Are you saying that because banks are loaning to data centers, the rest of the people are experiencing an increase in mortgage rates?
If you don't like what humans are doing to each other using 20th century warfare methods, consider what they might do with superintelligent AI?
Worst places to live in are usually countries fighting civil wars.
If youre going to spend 10 million dollars on 10000 agents trying to solve some important maths problem I think it's reasonable to ask for some grants to help digest whatever they came up with.
Or you could hire mathematicians and do it in-house, but I guarantee you grants to PhD students are cheaper than silicon value salaries.
They aren't buying tokens, more likely it costs them 1m to run the 10000 agents for 88 hours. Still leaves room for the PhD ofc.
I'm trying to map it on other fields and cant help but think it is absurd. If some company spends 1m developing a new alloy, should metallurgists be upset when they publish the recipe?
Not for now, but it's AI is getting better. A big step is "refactoring" a very long proof into a few intermediate lemas and theorems that are more inteligible and useful for other proof. It may take a few years or decades in some cases.
Anyway, I expect AI to be better at "refactoring", but for now a centaur is better.
Actually, for what you are mentioning, it is getting worse. There was a sweet spot somewhere around the release of GPT-o3, and ever since, the LLMs have been getting more accurate at solving problems, but worse at explaining how, and to hone in on what is interesting. This isn't surprising, as RL strategies shifted from RLHF to RLVR, so priorities during learning changed. I don't expect AI labs to reverse course on this. We can expect AI proofs to become increasingly incomprehensible over time.
I still remember Garry Kasparov vs. Deep Blue. Now, Magnus vs Stockfish is not even funny. I've seen AlphaGo and AlphaStar, and how their communities reacted...
I remember when Mathematica only could tell the answers and you had to type the formula correctly. Now there are apps that solve the exercise from a photograph with all the intermediate steps. We reminded the T.A. to be more alert during the midterms becuse we already had problems. I'm very worry about the magical glasses now, but it's important to be not overreact and be polite with the students.
Back to refactoring unintelligible long math proof: Let's talk again in 2031.
The paper is secondary to developing a general understanding of new ideas. The AI is not going to do that. All of the AI bros in this thread are going to be addicted to super AI Netflix and the math community will be dead.
The paper, both writing it and reading it, are a means by which we develop our understanding of new ideas. We can surely come up with a better means if we try, but "stop trying and leave actual understanding to machines" ain't gonna cut it.
For every important match problem solved by AI, without mathematicians we wouldn't know about the existence and importance of the problem.
Famous mathematical conjectures are social constructs, formed by decades of even centuries of attention given to them by members of the math community. Without it, the danger is that future math "progress" will be reduced to generating tables of Lean statements and a probable/unprovable bit generated by AI.
Could just be a sampling bias. Humanity had something like 3000 years to make famous conjectures, whereas AI mathematicians have been around for a month or so. Give them time, I'm sure they'll start formulating highly consequential unsolved problems soon enough.
Maybe tiring, but that is the reality. Note that mathematicians are only upset now that "have you tried this on the latest model" works for so many of their problems now, but didn't for the model before that.
Those are the two extremes, both are just as bad. Don't excuse shortcomings of the current models with promises of future improvements. But also don't demand literal miracles in 21 business days.
In a sense, how do you know whether the problem your AI has just solved is important? A simple proxy is to just check whether humans have thought it's important.
That's also why famous open problems are a good benchmark or proxy: you don't need to convince the rest of the world that the problem your lab's new AI just solved is actually useful or hard.
Not all of the 'famous' open problems 'mathematicians have failed to solve for decades' are actually that famous though. In some cases they've remained open because no one cared or had even heard of them. Those outside the mathematics community seem to think that all mathematicians can, and do, work on essentially all problems (for example, most mathematicians have in mind the millennium problems as a goal), but this is very far from the case. Most serious problem statements are not even particularly understandable to most mathematicians, let alone workable on.
There's also a difference between important and hard. There are important problems that turn out to be easy, and hard problems that turn out to be useless.
As usual, I think everyone would agree that something like "curing cancer" would be both hard and important!
I think the AI labs' current obsession with showing off mathematical results to uninformed outsiders is a cheap trick. If they were really interested in 'enabling human flourishing' (rather than just wowing, by any means possible, investors with more money than sense), they'd be showing off cures to diseases rather than solving obscure problems in combinatorics only previously considered by three Russians fifty years ago and then declaring that The Singularity is here.
> AI mathematicians have been around for a month or so.
LLMs have been around for years, and they're explicitly trained on the entire history of human mathematics (without which they'd be unable to do anything).
That is what I'm saying. There's a gold rush to extract what can be done with LLMs, but no indication that it'll continue. Maybe it will, but the evidence for its continuation seems to be entirely hype based on the fact that we've suddenly thought of new things to try. It's not because 'the capabilities are improving at a staggering rate omg the singularity is here guys'; it's mostly because no one bothered to spend enough time and money (and currently absurd amounts are required) to give it a serious shot until recently.
I like the test proposed by Demis Hassabis. Something like: train an LLM on pre-Einstein physics and see if it can rediscover what Einstein did. Despite the incessant noise online, we seem to be no closer to this. If there's material evidence that we are, I'd sincerely like to hear about it!
LLMs have improved at a staggering rate over the years. From spewing incoherent gibberish to barely managing low digit arithmetic to only tackling high school level problems.
2 years ago, a DeepMind system achieved IMO Silver. This system was not a pure LLM, nor were the problems solved end to end in language. In fact the role of the LLM was to translate the problem into lean and generate strategies that could be auto verified. Some problems took up to 3 days to compute.
1 year ago, both OpenAI and DeepMind announced internal models had achieved IMO Gold. Unlike the cobbled together Alphaproof and AlphaGeometry, these results were purely LLMs, solved end to end in language, with any lean proofs generated by the LLM afterwards. They achieved this result in the time limits alloted to humans (4.5 hours)
Today, we have publicly available models capable of solving well known and open standing conjectures. And in less than the time it took Alphaproof/Geometry to attain IMO Silver, internal models can even solve a millenium prize problem.
Comments like yours just make it plain you aren't paying any attention at all. The indication that it will continue is the fact that it has continued despite some assuring us we're at the precipice of some plateau at every single moment. You think it's because of 'breathless hype'? Only if you're blind.
> internal models can even solve a millenium prize problem.
OpenAI stole the work of Tristan Buckmaster to do it, and refused to explicitly deny it.
What do you think has enabled the 'staggering rate' of improvement? Scaling up? More data? It seems like, since LLMs do not solve problems from first principles and make crucial use of human work (without which they'd have nothing), once all the data has been eaten, some sort of plateau will indeed be reached. Anthropic are already reportedly buying up all of the world's second-hand books and destructively (their words) scanning them in a desperate rush for ever more training data, so I don't think this theory is unfounded.
What do you think of the Hassabis Test? Until it passes, I don't see how humans are replaced.
>OpenAI stole the work of Tristan Buckmaster to do it, and refused to explicitly deny it.
No they did not. Tristan (with heavy aid from LLMs btw) solved a sub-problem via a path Open AI's full solution didn't take (forced vs unforced). To say they stole his work would be silly.
And they explicitly say - “After investigating, we can say with full confidence that no user inputs past July 3rd could have influenced this system in any way.”
>What do you think has enabled the 'staggering rate' of improvement?
Compute and data
>It seems like, since LLMs do not solve problems from first principles and make crucial use of human work (without which they'd have nothing), once all the data has been eaten, some sort of plateau will indeed be reached.
Data used to train the models has become increasingly synthetic. The reinforcement learning getting them superhuman at math isn't tied to human data. If this is what you're betting on to make things stall, best to move on.
>Anthropic are already reportedly buying up all of the world's second-hand books
That is not what they are doing.
>What do you think of the Hassabis Test? Until it passes, I don't see how humans are replaced.
There's not enough pre-einstein text digitzed to make a fair shot at such an experiment so neat idea but kind of a waste of time. And it's kind of meaningless. We're already in the midst of AI becoming superhuman in one domain. We don't need any what ifs for something that is happening right now, in front of us. If some want to stick their hands in the sands, shouting 'la la humans are special' then there's nothing to do about that.
There is an important asymmetry, though: Buckmaster is a mathematician in it for fun (as almost all mathematicians are, because there certainly isn’t much money in it), and OpenAI is a company desperately scrambling for anything they can present as evidence that the singularity is here and you must invest your money now. Buckmaster abides by the unwritten code of mathematicians (alluded to in said interview); OpenAI abides by absolutely no such code other than ‘move fast and break things’.
I don’t think it’s a stretch to say that each side’s claim isn’t equally trustworthy.
But there is strong ego involved on part of Mr Buckmaster, egos tend to cloud judgement and bias a person. I would not speculate on either side and I would be careful to avoid people who simply take one side for granted.
Listening to him, I didn’t get that impression. I get that academia at large is full of egomaniacs (or so I’ve heard and read online), but the mathematics community is a weirdly non-toxic oasis of reasonable enthusiasts — genuinely, surprising though that may be to those with no experience!
So I have my reasons to weigh his statements more heavily. I certainly don’t take anything for granted.
I don't mean he must specifically be an egotistic person. But if you feel slighted and feel a problem you have been working hard for months taken from under you (quite regardless of the truth of that statement), any person would have a egotistic bias.
You make a good point about LLMs being (so far) great at identifying solutions to complex puzzles but not yet coming up with their own theories, questions, etc. The business of finding interesting questions to answer rather than answering them is the essence of what scientists do. Good science identifies more questions than it answers.
With Fermat's theorem, the genius was in the original theorem. Which then caused generations of mathematicians to break their heads over trying to prove it correct. Fermat didn't write down a proof. His theorem was famously just a scribble in a side line of a book. Probably it was something that he had an hunch about that he couldn't quickly falsify.
I don't think AI is being used much for coming up with new problems yet. But I don't see why that would not be possible either. It's the obvious next frontier after AI clears the backlog of existing theorems. I imagine scientists are already using AI to find new interesting problems to work on and generally explore the problem space. But fundamentally, the reflex of asking or imagining "if this is true, what else could be true" is something that distinguishes people from AIs. For now at least.
I think they are trying now to to bake CI awareness into Claude Desktop, didn't use it yet.
But meanwhile we also have the scripts - one script to watch CI, one script to fetch comments (without dumping raw graphql into the agent), etc etc. Can't wait for this phase to end already
I'm still so mad about Intel and AMD and Microsoft colluding together to remove sleep mode.
Horrible for battery life, unfixable, but Microsoft says they need to be able to wake up your laptop every hour.
They just sheepishly copied apple... with the single difference, that theirs is an open platform, where anybody can be a negligent actor, and they also don't put as much focus on the polish. Apple having a single vertically integrated ecosystem, with a narrow set of devices to support can and wills to do this properly. Sleep took 2 decades to become reliable over the fragmented PC ecosystem, so it was pretty much a mature, but dated technology. We have only 15 more until S0 sleep will get sorted out.
Or MS, Intel, AMD could apply pressure on vendors who fuck this up, including themselves, but their stuff is bought even so.
I'll also never buy a Mac again after having been tricked by the mass opinion and forced to work on that hellscape of a UX... while MS works hard to f...up windows it is still way better in most respects. Though they keep copying bad ideas from Mac, and this "modern sleep" is one of those also. But Windows has fractional screen scaling, sane keyboard shortcuts, all UI usable with keyboard, all windows can be accessed purely with keyboard. Built in tiling window manager. Machines with trackpoint... the famously good mac palm rejection is still crap, just slightly better than Synaptics... Oh. and Linux Support. Built into Windows, so the half-assed "UNIX" Apple ships is no longer missing. It is just different enough from Linux and NetBSD to always hurt when I use it anything more arcane then just "npm install", which is the main usecase for it according to the fanboys. Also I can put Linux on an oldish machine not supported by Windows 11 and still use it, with all its features.
Back to the topic: who wouldn't want always-on telemetry? :D
It is hilarious to me that in the examples they show side by side Opus 5.5 still uses 4 times more words than it needs to use.
IME, if you eyeball how many words the thing they're trying to say actually needs, and tell them to use only this many words, they become excellent communicators. I assume something about Anthropic's grader for writing just really wants to tick all its tidy tiny boxes of information the models need to cite. It's terrible.
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