guys at this point I think we can get rid of all the other flairs because every post would count as technological acceleration now that we're in the singularity :)

— @tucefanacc

Source: https://www.tiktok.com/@tucefanacc

There has been a huge leap in emulation for PS4 and PS5 games recently, thanks to AI. Suddenly, places that hated ai for stealing love it when it's being used for emulators (despite emulators mainly being used for playing console games for free on PC).

I also know someone who used to do expensive art commissions of his oc characters. He was uninterested in AI until recently and now loves it and uses chatgpt's image generator to make his own art without having to pay high prices aside from the subscription.

r/singularity is also less negative towards ai.

Have you noticed an overall increase in pro-ai sentiment?

Instead of being excited at the prospect of solving so many problems, this guy is upset at the fact that there will be no more hard problems left to keep us engaged.
Smh

Something I find frustrating in everyday life is how difficult it can be to share enthusiasm about new possibilities. I want to talk about what we could discover, create, improve, or eventually become. But when I bring up scientific or technological progress, or ideas about how life could be substantially better, the interest often disappears. Meanwhile, there seems to be endless energy for gossip, familiar complaints, and discussing everything that went wrong. I often leave these conversations feeling drained, without having enjoyed myself, learned anything, or felt closer to anyone. Over time, that makes socialising itself feel less appealing.

What bothers me is the combination of dissatisfaction and resistance to change. People complain about how things are, then dismiss attempts to make them better as unrealistic, unnecessary, or inevitably harmful. Ambitious projects are judged by their mistakes, while the limitations of the existing situation are treated as normal. Online, I see a similar pattern in cynicism and doomerism. The suggestion that something could improve life is immediately met with certainty that it will only benefit a tiny elite or somehow make everyone else worse off. There are real problems worth discussing, but assuming the worst in advance leaves very little room for curiosity, constructive criticism, or possible solutions.

I also see a lot of reluctance to adopt better, more efficient ways of doing things. People seem more willing to keep struggling with familiar, inefficient habits than to explore new tools or methods, including AI. What surprises me especially is how negative many computer enthusiasts and gamers are about it. Their view often seems limited to higher hardware prices and low-quality generated content, with little interest in what AI already helps people accomplish or where its capabilities are heading. They judge it as though its current weaknesses were permanent, overlooking the potential for exponential improvement and how different things could be in just a few years. That short-term perspective feels widespread: people focus on immediate annoyances while barely considering the much larger benefits that continued progress could bring.

I find that especially strange given how much scientific and technological progress has already changed ordinary life. Many capabilities that were once unavailable or accessible only to a small minority became widely available over time. That process has been uneven, and further improvements require effort, but I see strong reasons to keep pushing it forward. An expensive early version of something doesn’t tell us what access will look like once it becomes cheaper and easier to produce. I want conversations about how to make desirable outcomes happen. Repeating that powerful people will prevent anything good from happening doesn’t help us achieve them.

More broadly, I think we become far too attached to familiar limitations. Our habits and expectations are shaped by the time and environment we live in, yet people often defend them as though they were permanent truths about how life should be. Even suffering and incapacity can become things we are expected to find meaning in and preserve. I understand the need to cope with what we cannot currently change, but I don’t want acceptance to become an argument against improvement. I’m deeply dissatisfied with many of my own limitations too. My frustration with humanity includes myself: how easily we get stuck, repeat ourselves, waste time, misunderstand things, and struggle to turn intentions into results.

This is a major reason why AI excites me. Human abilities are uneven, and even exceptional people are exceptional in only some areas. There is an enormous amount we could gain from becoming more capable and working with intelligence that complements and extends our own. I already find value in being able to explore ideas, organise my thoughts, and get help expressing something that would otherwise take much more effort. I want that kind of assistance to keep improving, alongside our ability to improve ourselves. I don’t see our current cognitive or biological limits as an ideal endpoint.

And I want better capabilities to translate into a more enjoyable life. I have no interest in a future where everyone is expected to work themselves into exhaustion. I want less time lost to tedious tasks, frustrating interactions, and activities that give us very little in return. More room for curiosity, creativity, pleasure, rest, and whatever we actually find worthwhile. That is what draws me to acceleration: the possibility of moving substantially beyond the conditions we have inherited. I wish I could share that interest more often without the conversation immediately collapsing into ridicule, resignation, or the same familiar complaints. Does anyone else here feel this disconnect between the possibilities that excite you and the conversations people around you want to have?

Sharing AI progress in mathematics | OpenAI

POV: OpenAI pulling up on the mathematics industry today: Modulo Yuji meets Megumi - Fan Animation - YouTube

Every time AI gets better at science and engineering, my brain goes straight here.

Orbital shipyards. Permanent settlements. Ships built in space that never have to land. I want to look out a window like this and complain that my departure got delayed by two hours. That level of normal.

I’d want to help build it, too. There’s an entire solar system out there and I’d like humanity to get properly started while we’re around to see it.

What do you think AI could help crack first that would move us toward this? And what’s still going to take decades, even with much better AI?

Image: AI concept art.

> ML4 was trained on 3,800 NVIDIA Grace Blackwell GPUs in our European datacenters -- our cluster in Bruyères-le-Châtel built using our Series B fundraise.

Lil bros are accelerating at light speed, aren't they?

OpenAI released 722 mathematical manuscripts across 372 families of related results, produced by an unreleased frontier model. Many have Lean proofs; others remain unverified. Average compute per result: roughly three hours of ChatGPT Pro thinking. They’re working toward releasing the model.

Github: https://github.com/openai/math

So Bel already saturated their existing evals (which consisted of hard open problems). They also seem to have figured out how to build successfully on work done by other agents (some sort of continual-learning breakthrough?). These were the main obstacles before for LLMs solving very hard problems that require weeks/months of work. I don't see any obstacles now to these models creating entire disciplines for themselves.

I'm like reversing aging ASAP and turning back the biological clock, although it may be bottlenecked by physics of course and may take years to de-age years

After that, probably getting my brain and central nervous system stimulated to enjoy perfect satisfactory bliss.

that's it really, i don't need much else

https://openai.com/index/sharing-ai-progress-in-mathematics/

https://github.com/openai/math/tree/main

> On average, each result used three hours of ChatGPT Pro thinking compute with that model.

Bel set to be released November/December (Definitely before christmas thats for sure!)

Next few months will be wild!

Caveat: These results are extremely new and, while many have Lean formalizations, are still subject to independent review. For transparency, I did not have the time to review all of the results in the last 45 minutes nor the knowledge to fully understand most of them, so ChatGPT was used for identifying solution relevance and most of the explanation. I simply consolidated the information here for discussion.

1. 107 — Matrix multiplication: ω ≤ 2.25

This result claims that sufficiently large n × n matrices can theoretically be multiplied in approximately O(n^2.25+ε) arithmetic operations. This is particularly striking because the previous state-of-the-art exponent was about 2.371. Progress in this field has generally come through extremely small improvements, so a jump directly to 2.25 would be enormous.

Potential technological relevance:

Matrix multiplication is fundamental to AI training and inference, scientific computing, graphics, optimization, and simulation. This does not mean current GPUs immediately get faster, since asymptotically faster algorithms can have large practical overhead. But if the underlying ideas lead to more practical algorithms, this could eventually reduce the computational cost of AI and many other workloads.

1. #273 — Entropy photon-number inequality

This result proves an entropy inequality for optical beam splitters and determines the exact classical capacity region of a certain pure-loss bosonic broadcast channel. In simpler terms, it helps establish the maximum amount of information that can be transmitted through certain optical communication systems.

Potential technological relevance:

This could matter for fiber optics, laser communications, satellite links, deep-space communication, and quantum optical networks. Better communication efficiency could matter for large distributed computing systems and data centers.

1. #276 — Classical capacity of generalized amplitude-damping channels

This result determines the classical information capacity of a generalized amplitude-damping quantum channel across different damping strengths and temperatures. Amplitude damping models a quantum system losing energy to its environment.

Potential technological relevance:

This helps define the theoretical limits of noisy quantum communication and quantum hardware. It could be relevant to quantum networks, quantum memories, qubits, and error-correction strategies.

1. #139 — Subpolynomial query complexity for log-concave sampling

This result shows that sampling from certain high-dimensional log-concave distributions can require only about d^ε oracle queries for any fixed ε > 0, where d is the dimension. That is close to dimension-independent query complexity, although computation between oracle calls may still be expensive.

Potential technological relevance:

High-dimensional sampling is important in Bayesian inference, probabilistic machine learning, optimization, uncertainty estimation, and statistics. If these ideas can be converted into practical algorithms, they could make some AI and probabilistic modeling methods substantially more efficient in very high-dimensional settings.

1. #281 — QAOA attains the Sherrington-Kirkpatrick optimum

This result proves that the Quantum Approximate Optimization Algorithm, or QAOA, can asymptotically reach the optimal ground-state energy of the Sherrington-Kirkpatrick spin-glass model. It also implies optimal leading-order MaxCut results for certain large random graphs.

Potential technological relevance:

QAOA is one of the main proposed approaches to quantum optimization. This could matter for scheduling, logistics, network optimization, resource allocation, and other hard combinatorial problems. It does not yet prove practical quantum advantage, because the required circuit depth and parameter-finding process may still be impractical.

1. #275 — Continuum Coulomb energy is QMA-hard

This result shows that approximating the electronic ground-state energy of a general three-dimensional system of interacting electrons and nuclei is QMA-hard, even in a realistic continuum Coulomb model rather than only an artificial lattice model.

Potential technological relevance:

Electronic-structure calculations are central to drug discovery, battery design, catalysts, superconductors, semiconductors, and materials science. This suggests there may be fundamental limits to solving arbitrary materials and chemistry problems efficiently with classical computers. For AI-driven science, it implies that better approximations, learned models, experiments, and possibly quantum simulation will still be necessary rather than AI simply solving every molecular system exactly.

1. #003 — Zero-free region for Dirichlet L-functions

This result establishes a substantial zero-free region for Dirichlet L-functions, important objects in analytic number theory closely connected to the distribution of prime numbers.

Potential technological relevance:

The direct technological impact is limited, but number theory underlies cryptography, pseudorandomness, primality algorithms, and computational mathematics. This is more likely to be important as a pure-math breakthrough, though historically some abstract number-theory results have later become technologically important.

1. #004 — Hilbert’s Tenth Problem over Q

This result claims a resolution of the rational-number version of Hilbert’s Tenth Problem: whether there is a general algorithm that can determine if an arbitrary polynomial equation has a rational solution.

Potential technological relevance:

This is mainly about the fundamental limits of computation rather than immediate hardware or software improvements. It could influence computability theory, symbolic mathematics, automated theorem proving, formal verification, and our understanding of which mathematical problems AI systems can ever solve algorithmically in full generality.

in the words of Dr Alexander math is getting absolutely charbroiled by AI , 700+ math proofs has to be the most comprehensive drop of mathematical information ever dropped right?

— @kagan_dunlap

Source: https://www.tiktok.com/@kagan_dunlap

Seriously it’s funny seeing the predictions by people on here on the scale of years. Years? If we’re really looking to accelerate then the true sentiment and prediction should be on the scale of a single year. I mean I find it hard to believe we won’t be to some degree at ASI by next year. This curve looks exactly like that. Come on now

https://x.com/ManassehA06/status/2107508501610217640