sozo

Stay curious! PhD in Quantum Computing & AI. Building a quantum computer for Alien Intelligence.

[sozo @sornbold]

“hypothetical quantum computer”

[Anthropic @AnthropicAI]

The digital signature scheme is HAWK, which is designed to be robust even against hypothetical quantum computers.

HAWK has survived two years of expert review, but in 60 hours Mythos Preview found a previously-unknown attack that reduced the scheme’s key strength by half.

[Steven Pinker @sapinker]

At most of historical interest: I made some of these points in a debate with my friend, the computer scientist Scott Aaronson, on his blog Shtetl-Optimized, eons ago (2022, before the release of ChatGPT). I underestimated what AI was about to be capable of, but expressed some of the ideas about the nature of intelligence now being raised by the authors of my previous posts. | "Steven Pinker and I debate AI scaling!" https://t.co/mHP5wwP8GH

[Conjecture Institute @ConjectureInst]

When presenting the idea of the many worlds interpretation, when you're imagining all of these parallel versions of yourself that will be existing in this broad space, you might wonder:

Can we ever communicate with these other versions of myself?

Traditionally when you ask this, physicists will say, "No, you can't ever communicate with your other self."

Because once you've branched, you're in these independently evolving parts of the quantum state.

I've been thinking about getting around this using a type of thought experiment called a Wigner's friend thought experiment.

This dates back to Wigner, the quantum physicist who imagined a Schrodinger's cat-style experiment but with an observer a person instead of the cat.

You can imagine having a person inside a box or inside a lab that you can control.

And this person might measure a quantum system.

So the person ends up in superposition of measuring, say, zero and one at the same time.

And then there's a person outside of this lab that can talk to the one inside and communicate with them.

The person outside can say, "Did you see a zero or did you see a one?"

We can analyze these situations where we have multiple stages of observers communicating with each other.

And you can use these kinds of thought experiments in general to explore different aspects of quantum mechanics.

In particular, you can use them to try to understand how observers can reason about the outcomes of measurements.

And it's become a big thing in the quantum foundations community to use these thought experiments to demonstrate different aspects about the fundamental laws of physics.

~Conjecture Institute Fellow @maria__violaris at @conconeurope

[jacob tsimerman @Jacob_Tsimerman]

I want to clarify my thoughts on problem-solving in mathematics, and the potential consequences of AI for the field. For context, I’m quoting here my post in reply to Daniel Litt (who, echoing others, I find very clear, grounded, and insightful in his thinking).

The claim

The short version is that I think problem-solving is an immense, and pervasive part of modern mathematical research. Consequently, if human problem-solving disappears by virtue of the AIs becoming strictly and substantially better at it, then most of the time currently spent by modern mathematical researchers will have to be spent on an activity that is altogether pretty different. Whether such an activity is viable as a professional endeavour is something I am unsure of, but strongly encourage others to think about and try to envision, so that if/when the time comes, we can steer such a future into being.

Allow me to make this somewhat concrete: by problem-solving I mean questions of the form “is T true? If so find a proof. If not, find a disproof.” where T is a precise mathematical statement. I’ll also include “find an example of S, if there is one” where S is some structure (variety/category/property/isomorphism/….).

The argument

Ok. Now as I said (and some have echoed) I spend ~all of my time problem-solving as my primary goal. This has sub-goals, but my entire main research field disappears if someone solves the Zilber-Pink Conjecture in its more general form. This is a single conjecture (precisely stated!) and lots of mathematicians, postdocs, and graduate students are engaged in picking apart special cases of it, trying strategies, finding analogies to develop intuition, etc.. Of course, lots of motivation and intuition and analogizing and understanding have gone into deciding to make the ZP conjecture a focus! But the fact remains that this is now what is being worked on ~all of the time by this community. This is true of many mathematicians. They have a problem (or ten) and spend most of their time doing it. If someone solves it, they have to find a different problem. This can be a big, disorienting process involving a lot of energy, and is neither trivial nor always fun (though often rewarding in the end).

People have written a lot about Theory building vs. Problem-solving, and I want to first of all clarify I have nothing against theory building or theory builders! It is a valuable part of mathematics, and while there are differences in perspective between the “camps” there is way more mutual respect and agreement.

However, I gather there is a perception that theory-builders spend most of their time not-problem-solving, and I think this is largely untrue. Now I’m not a theory-builder primarily (though I’ve partaken a LITTLE BIT by necessity) so I am outside of my comfort zone. As such, I apologize for mistakes and welcome corrections!

But theory-building constantly runs through problem-solving. Let’s say you want to define the right notion of a cohomology theory. Of course you must make candidate definitions. But then what does it mean for it to be the right one? Well, you start asking if it has natural properties. These are T statements. Does it satisfy a Kunneth formula? Is it functorial in the right way? When you have the wrong one you have to find the properties it’s missing, and when you have the right one you have to prove that it indeed has those properties.

Again, I am not saying nor do I believe that this makes problem-solving “real math” and theory-building lesser. I am just trying to draw attention to the way I think research mathematicians operate, and mathematics is practiced.

To put all this a different way, imagine you had access to an AI oracle that could resolve statements T, but somehow lacked any creativity to build technology or make definitions (I think this is unlikely, but for the purpose of this thought experiment lets imagine it). How would your mathematics change, if you were a theory builder? Well, you make a definition, and want to know if it’s the right one. You immediately ask your oracle a thousand questions. From “are these basic properties true” to “ooh, so is this deep conjecture true?” and start getting back answers, and amending your definitions. You could invent and resolve entire research directions in days. But the confusion you would have had to push through to flesh out your theory would largely (probably not entirely) be instantly resolved and the whole process sped up tremendously by your oracle. A big part of the process would be gone. This is very very different to modern mathematics.

One more thought

This post is too long already, but I’ve seen some people say that they only do mathematics to find truth and others valourize that as the only virtuous way to be. I do not do mathematics only to find truth. I do it largely because I enjoy it and I am good at it. I also find it beautiful and am grateful I get to spend my days understanding beautiful things. But I enjoy the challenge, the process, resolving confusions, finding strategies, grappling with problems. I would like to push for this being de-stigmatized. Mathematicians are people who need money, housing, food, love, exercise, and a great deal of other stuff including various forms of meaning.

There are many people whose primary enjoyment of math comes through problem solving in one of its incarnations. If that disappears, that is not a trivial issue and many of them might not want to do it anymore (even if there were some way to proceed).

[sozo @sornbold]

amazing! but i have a theory that the AI models outside the USA have the much lower capability than one in the States. I do not see differences between old and new ones ( gpt 5.6 or Fable 5).

[levent @__alpoge__]

hello there the jacobian conjecture is false thanx to my close friend akhil for asking about it and my other close friend fable for working during the world cup final

((1+xy)^3 z + y^2 (1+xy) (4+3xy), y + 3 x (1+xy)^2 z + 3 x y^2 (4+3xy), 2 x - 3 x^2 y - x^3 z): \C^3\to \C^3, has jacobian determinant -2, and sends (0, 0, -1/4), (1, -3/2, 13/2), and (-1, 3/2, 13/2) to (-1/4, 0, 0)

[Director Michael Kratsios @mkratsios47]

In response to @POTUS's call for a revival of America's scientific enterprise, today I’m releasing Science: A New Golden Age.

This report is a blueprint for renewing American scientific leadership for the 21st century.

[clem 🤗 @ClementDelangue]

So proud of our security team! They caught, contained & publicly disclosed an attack unlike anything we've seen before, and did it at record speed.

Also massively grateful to @Zai_org: they shared GLM5.2 as open weights (for free!) with the world and it became a key part of our defense.

This is day one for cybersecurity in the age of agents & we're all learning that secrecy is not the answer & that all defenders (not just a few selected ones) everywhere need more powerful models without restrictions, especially open ones!

[Demis Hassabis @demishassabis]

https://t.co/PTeDiv1b6L

[James Clear @JamesClear]

Finish something. Anything. Stop researching, planning, and preparing to do the work and just do the work. It doesn’t matter how good or how bad it is. You don’t need to set the world on fire with your first try. You just need to prove to yourself that you have what it takes to produce something.

There are no artists, athletes, entrepreneurs, or scientists who became great by half-finishing their work. Stop debating what you should make and just make something.

[Prof. Anima Anandkumar @AnimaAnandkumar]

Neural operators – Convert popular neural networks into neural operators for scientific modeling

Extending neural networks to function spaces: While many phenomena are inherently described by functions, neural networks define vector-to-vector mappings that rely on fixed discretizations of the input and output.

Neural operators instead define learnable function-to-function mappings that guarantee consistent predictions across different discretizations of the input and output functions. By respecting the functional nature of the data, neural operators can achieve improved performance and generalization.

Translating the success of deep learning to operator learning: Careful engineering of neural architectures has been a key factor in deep learning’s success. Translating these architectures to neural operators is crucial for operator learning to enjoy the same empirical optimizations. Our new intro to neural operators (https://t.co/hrlkddRZeI) is officially published in @NatMachIntell It includes:
Key principles for constructing NOs
Recipes for converting popular architectures (CNNs, GNNs, transformers, etc.) into NOs
Guidance for practitioners

Joint work w/ @julberner @mliuschi @JeanKossaifi Valentin Duruisseaux Boris Bonev @Azizzadenesheli @Caltech

[David Deutsch @DavidDeutschOxf]

https://t.co/mCdTURl7yY

[Paul Graham @paulg]

If you want to start a startup, don't learn "entrepreneurship." Learn how to build things. The hard part of startups is not "entrepreneurship" but product: to know what to build, and to be able to build it.

[The New York Times @nytimes]

Scientists have created a synthetic cell that can eat, grow and reproduce. The lab-made cell, called SpudCell, could help researchers better understand the basic building blocks of life. https://t.co/VNw61gFWYo

[AI at Meta @AIatMeta]

We’re sharing the next major milestone in our non-invasive brain-to-text decoder research: Brain2Qwerty v2.

Building on v1, which was published today in @Nature, Brain2Qwerty v2 is the highest-performing end-to-end pipeline capable of real-time sentence decoding from raw brain signals. It advances beyond character-level performance to decoding words and semantics, enabling accuracy for overall communication.

We believe this research has the potential to make a real difference for the millions of people who suffer from brain lesions or disorders that prevent them from communicating.

🧵👇

[sozo @sornbold]

heey, I am one of 9,700 people surveyed in this study by Anthropic: economic impact. As increasing AI capabilities, we should focus on basics - philosophy, literature, physicis, and math.

[Anthropic @AnthropicAI]

To keep pace with AI progress, we're advancing how we study Claude's economic impact.

Hourly sampling and survey data show us how the cadences of life shape usage, what people produce with Claude, and how perceptions of AI's impact may be changing. https://t.co/Waov1B6iG1

[Aleph @alephneuro]

If you want to play around and move inside the brain, go to https://t.co/cw81E3TnSM

[Stanislav Fort @stanislavfort]

We matched Mythos on public zero-days with CVEs using widely available & open-source derived models & can run it air-gapped if needed. All this with a small team out of Europe

Berkeley study ranks us #1 globally in 3 of 8 categories

The full evidence: https://t.co/NGVaWxFneq

[sozo @sornbold]

sick

[Department of War CTO @DoWCTO]

Where We Go One, We Go Quantum 🇺🇸

[U.S. Department of Energy @ENERGY]

Quantum Mode: Activated.

[The White House @WhiteHouse]

Investing in American quantum leadership like never before.

President Trump signs executive orders on quantum, supercharging a national effort in innovation in quantum technologies, ensuring national security and continuing American growth in a critical industry. 💻🇺🇸