【Night Announcement】 Dear Patreon subscribers: This month's rewards will be given to you later. Because I am trying to set the content of onlyfans recently. Also secretly released a photo for your reference. In the future, such works will be in onlyfans! Patreon will still be biased towards Cosplay or special modeling works. Hope you all like it! 【深夜公告】 各位親愛的Patreon訂閱者 這個月的獎勵會晚一些時間給大家>"< 因為近期在努力的設置onlyfans的內容 也偷偷釋出一張照片給大家參考 之後這類的作品都會在onlyfans哦！ Patreon還是會偏向Cos或是特殊造型的作品 希望大家都會喜歡^^

How can blockchain privacy and security evolve together? Join us for CertiK Spotlight EP2: Decoding Privacy with @MidnightNtwrk. Hudson Jameson and Alejandro Munoz will be joined by Midnight's Gareth Christian-Lim and Karmel Elshinnawi to explore: → Privacy's role in mainstream adoption → Balancing innovation with protocol security → Threat modeling and permissionless deployment → What builders should know before deploying on Midnight 📅 October 22 | 10 AM ET Set a reminder ↓

Every investment decision I've ever made has been informed by a Excel spreadsheet. A financial model that serves as a business simulation and a translation layer between research & insight. The model ultimately is a place to quantify my variant perception and quantify the downside if I am wrong, the two critical pillars of any good public market investment thesis. One of my PM friends says his analysts spend all day with "their noses in models", identifying and researching the hinge variables in a business. This is the game of alpha generation, simple not easy. A cornerstone of the hedge fund interview process is the modeling test, and hedge funds routinely hire ex bankers & private equity for this skill. (A certain hedge fund founder is rumored to have fired analysts who tried to repurpose sell-side models as their own.) When you peel back the onion on Tier 1 fundamental research process, four years since the Chat GPT 3.5 moment, AI adoption is slow & uneven with the median investment process far from transformed (despite an exceptional amount of tokens burned...). Why is that? To me, it's simple. The "bar to impress" a public equity investor with AI has to start with the financial model architecture as the nerve center of the investment process. Almost every AI workflow for investors I've seen is a "hey this is sort of interesting", but not a "you will take this from me over my dead body" workflow. An agent that can summarize your morning inbox well is convenient but won't bend the curve of alpha. An automated, auditable, full-stack research system built around an architecture of Excel models that can help me do the rigor and depth of investment research in 3 days that used to take me 3 weeks? That changes everything. Financial modeling as the foundational layer in that workflow, the critical unlock to truly enact a full end to end research & risk system. Yes, it is productivity, but ultimately it is more about speed to insight and surfacing the right insight to the decision maker at the right time (this is what great analysts do). "But judgment will still sit with the PM, right?" What is investment judgment if not gathering and weighing as many unique insights about a business as possible then continuously comparing that view to market implied expectations? "Brett, but won't modeling move to JSON?". I've heard selective instance of that. Perhaps eventually. But Excel is just this beautiful, auditable, sharable, trusted substrate on Wall Street. It won't die a quick death. The challenge investors have of reinventing their investment process while running risk is materially different than software engineers adopting coding agents. Until September 22nd when Opus 5.5 was released, the technology wasn't there. It's here now. For this simple reason, I believe we are entering a period of accelerated adoption of AI in Finance, particularly in public equity investment process. I am having conversations with firms who are interested in co-developing and enacting this vision for their investment teams. If this resonates with you, please DM.

Honored to be named one of the 100 most influential people in AI by @TIME right on the heels of the public launch of our company Accelerated Understanding @accelerated_u Bringing the power of AI+Science together has been the focus of my life for the past decade. I believe the greatest impact AI can have on science and engineering is its ability to massively accelerate simulation and understanding of the physical world. We started this journey with the invention of Neural Operators at @Caltech to have a powerful foundation for AI modeling physical phenomena at multiple scales. Together with my team at @nvidia and support from @JensenHuang himself we built FourCastNet, the first high-resolution AI-weather model that is tens of thousands of times faster than existing systems. Following up on this success we have applied Neural Operators to accelerating simulation of nuclear fusion to detect disruptions before they happen in the real world. Just this week, we announced how Neural Operators can enable density functional theory simulation in quantum chemistry quasi-linear time. We have also used Neural Operators to invent better medical devices like a catheter that reduces bacterial contamination by 100x and we have been able to design better masks for chips and optimized gate layouts for quantum dots. More recently we have been asking ourselves what would happen if we aggressively scale our models and put multiple areas of physics in the same model, teaching it physics in full 4D (3D space + time). This requires massive scale and that is exactly what we have been able to do at Accelerated Understanding. We have pre-trained models up to 1 Trillion parameters, and we are able to train at 4D context lengths of up to 1 Trillion and run inference at 5 Trillion context. I am particularly excited about self-improvement: a limiting factor for scaling physical AI so far has been the availability of high quality training data. As the old saying goes: your model is only as good as your data. But that no longer holds true: for our models we have the laws of physics themselves that let us measure and improve the quality of our outputs exceeding what was present in the training data. But the improvement loop doesn’t stop at the models themselves. We can also use our models and their ability to understand physics and give directional feedback to break down one of the biggest barriers to innovation: the reliance on lab experiments as a bottleneck in the improvement loop. As intelligence gets more abundant this bottleneck is only increasing in importance. Putting our models and their physical simulation capabilities in that loop instead and taking advantage of their directional feedback means we only need the lab all the way in the end to double check. Excited to see new inventions and discoveries this will unlock! #TIME100AI#

A new and possibly controversial perspective: In this video, I explain the sense in which generative AI trained by supervised learning is incapable of making novel discoveries. The text of the speech: AI Creativity and Discovery Good day ladies and gentlemen. I regret that I am unable to be with you all today to engage in a back-and-forth discussion, but I am nevertheless pleased to be able to share with you, via this recording, some high-level thoughts about the current and future state of artificial intelligence, and in particular about AI’s relationship to science and mathematics, which is, as I understand it, the central focus of this meeting and of the SAIR Foundation. I would like to start with an old joke; I am sure you have heard it before. It is the one about the researcher whose work is being evaluated, and the review comes back, and says “This work is both novel and good. Unfortunately, the parts that are good are not novel, and the parts that are novel are not good.” My first point about AI is that this assessment applies exactly to large parts of AI as we know it today. Not all of today’s AI, but a large part of it. Pretty much all of what we mean by “Generative AI”---which includes large language models, and the images and video models, and even the new methods for learning world models. All of these AIs take large numbers of examples and produce a “model” which behaves similar to the examples, that is, which generates text like people, or images like artists or nature, and videos like we find on the internet. Don’t get me wrong, Generative AI can be extremely useful. No doubt about that. But the assessment of the joke still applies. These systems can produce output that is both novel and good, but not at the same time. In many ways this is just absolutely not a problem. When we ask an AI for an answer from the internet, or to summarize a document, we don’t want it to be novel. We are happy if the quality of the answer, the goodness, comes from the source material—from the people who wrote the document or the articles on the internet. If the AI’s answer is novel it means it is going beyond the source material, adding something beyond it. This is what we call “hallucinations”. In most cases, we don’t like it when the AI makes something up, when it adds something novel. One exception, of course, is when we are looking not for facts or reality, but for fiction and entertainment. We might ask for a bedtime story for a child, or an image based on existing images on the internet but which is nevertheless different and distinct from them. In these cases, it is never easy for us to know how creative the AI is actually being, as we do not know how close the AI’s story, poem, or image is to the source material. In a real practical sense we can not know this because the internet is too big, the possible sources that the AI may draw upon are too numerous. When we ask for a fiction or novelty, the AI can give it to us because its processing is in part stochastic. Every decision can go multiple ways and will go different ways and produce a different trajectory every time. The trajectory can be random—and thus novel—or it can be based on the training data—and thus “good” because the training data is good, sourced from people or reality. Thus, the trajectory is either novel or good—based on randomness or based on data—but never both at the same time. Really, I think it is okay if the output of Generative AI is never good and novel at the same time. For the researcher in the joke this is a devastating criticism, but for most things it is not, and for Generative AI it is not. Generative AI is meant to be a mimic. This is what supervised learning is for. Generative AI can be extremely useful, even when it just mimics, if it is faster, or cheaper, or smaller, or more customizable, or more copy-able, than the thing being mimicked. It is okay if Generative AI cannot be both novel and good at the same time. It is still a transformative technology. But it is a limitation. And remember we are here to use AI for science and mathematics, and for these areas the assessment of the reviewer in the joke is devastating. For these areas we need true creativity and discovery. Generative AI—or Mimicking AI—will never get where us there. For these we need something more, and indeed we have something more in other parts of AI. We have many AI systems which can give us more. We have AlphaGo with its world-changing move 37, or AlphaZero with its brilliant original chess-playing style. We have GT-Sophy that drives simulated racecars better than any human. We have AlphaFold and AlphaProof and Claude-Code, which have brought true advances in science, mathematics, and programming. We have RL-Lyft which optimizes the assignment of cars to passengers in the ride-hailing business. All these systems have found things that are both novel and good. And, truth be told, some language models have been augmented in ways that make them more than Generative AI based on supervised learning. All these systems have some additional features that make them capable of true creativity and true discovery. It is important for us to recognize what this is—and that it is not present in ordinary, garden-variety Generative AI. It is something that can not come from just supervised learning, from learning from examples. What is it? Well, it is a simple thing, a commonsense thing. It is not new. We have many names for it, but unfortunately none of them are very good names. I will call it Discovery. Basically, Discovery is just the idea of trying many things and seeing which of them work, then keeping those that worked the best. Evolution by natural selection works this way. The scientific method works this way. And just ordinary life and learning works this way. We try things and remember what works. What could be more obvious? In this behavioral case, psychology has two names for it— “instrumental learning” and “operant conditioning”—and in machine learning it is what we mean by “reinforcement learning”. We also see the idea of Discovery in planning and combinatorial search—anything that involves the idea of “generate and test”. The essence of Discovery is to combine three steps: 1. Variation, 2. Evaluation, and 3. Selective retention. Of course, I am not the first to say this. I am not the first to point out that this combination of steps is key to science, to evolution by natural selection, and to animal behavior. I think particularly of papers by Donald Campbell, by Daniel Dennett, and by Gary Cziko. What is new in my remarks is to directly relate the idea of Discovery to modern AI to help us see that it is not present in supervised learning or Generative AI—in particular, that Discovery is not present in backpropagation or gradient descent. Let me say explicitly what is missing from Generative AI. As we have remarked, these systems do have a stochastic aspect, so they do generate a variety of trajectories and behavior. What is missing is the Evaluation step. The generator was pre-trained by supervised learning, leaving no way at runtime to Evaluate what it generates. And of course without Evaluation there can be no Selective retention, and thus no Discovery. The variation can bring novelty, but without evaluation there is no Discovery, and arguably, no creativity. That is, I would say that creativity requires that the new things generated be Evaluated. Without evaluation, and retention of the best, there is nothing created. The novelty flickers into existence but, if its value is unrecognized, it flickers away and is lost. In many cases, Evaluation is done by people to make a discovery. As when we have Generative AI make many pictures for us, and then we pick the one that we like the best. The human+AI system completes the discovery. In many other cases, the Evaluation comes from a clear objective. Some moves lead to checkmate, some steps lead to a proof, some actions result in high reward, some genotypes make more copies, some theories explain the data better. Some prefer the Variation step to be called Blind variation, where “blind” here means that it is uninformed, a shot in the dark. It does not need to be completely uninformed; a good scientist does not select theories to test at random. But neither can it be completely informed and determined. There must be some uncertainty about where the answer lies in order for there to be a discovery. In practice, the variation is partly informed and partly blind, but it is the blind part that corresponds to the discovery. Now let us briefly go all the way to modern deep learning, to the backpropagation algorithm. At first it might seem that backpropagation is incapable of discovery because it is deterministic and thus incapable of variation. But this is not correct. The weight updates of backprop are deterministic, but the weights are initialized to small random values. The random initialization is often downplayed, but in fact it is a necessary form of variation; it must be done properly to get good performance. In backprop this Variation is done once, at network initialization, so its effect is temporary, and later the network may lose its ability to learn. This is the weakness of deep learning that is alleviated with a new algorithm that my group presented in Nature a couple of years ago. Our “continual backpropagation” made one small change: every so often a less-used neuron would be re-initialized to small random weights. This allows the variation to continue and plasticity to be retained. Although there is much more to be said about Creativity and Discovery, this is the key point: they are more than supervised learning, more than pattern recognition, more than prediction, and more than world modeling. Those things are important, but they alone will not bring us to discovery. Discovery requires Evaluation from a person or from an explicit goal, and only in the latter case will we attain full autonomy. So that is my call to arms. If we want the full power of AI scientists, then we should share the goals with them so they can create, evaluate, discover, and in these ways fully participate in achieving the goals. Let’s be bold! Let’s fully automate Creativity and Discovery!

Eyewitness states: I recorded this video from Medellín, Colombia, exactly at the same time the double earthquake was hitting Venezuela. While reviewing the footage of that moment of tension, I noticed this strange object in the sky. I am sharing it here to get the community's insights and opening a discussion about what this could be, especially given the timing of the seismic events Time: June 24, 2026 Location: Medellín, Antioquia, Colombia

🇺🇸🇨🇴 At least eight people were hospitalized after severe turbulence hit a flight to New York, forcing an emergency landing in Miami. The Avianca plane left Medellín, Colombia, late Thursday bound for JFK. It diverted about three hours in and landed around 3:30 a.m. Friday, with ambulances waiting on the tarmac.

am i the only one that thinks it’s quite funny that we’ve seen very little from the big chinese labs in a while 🤔 (i do hope that changes, the big 2-3 american labs need better competition..)