this is what I've come to just staring at this chart retest of green line and bouncing pretty boolish breaking past orange pretty much confirms bullmarket/end of bear losing red/20w EMA prob leads to retesting lows/lower lows from a sideliner perspective, bidding low 70ks down to mid mid 60ks makes sense no matter what below 65k feels like we'd have to get a new low as we'd drop into an old range again and be testing 59-60k for the third time which wouldn't be great. How much lower idk, luckily most of the move up has been spot driven and animal spirits haven't taken over the market yet, so a wash out wouldn't need to go to 50k or w/e i can't see a break (and close) of 65k rn tho unless there's a black swan round the corner, which we can't obv play for or predict

I'm a cardiologist. Before you panic-purge your pantry over the viral "throw these out if you love your family" posts — let me show you how fear-based health content manipulates you with real data. The headlines screaming right now: common food emulsifiers like E471 and carrageenan raise cancer risk up to 46%. A French study of 92,000 adults over 7 years found the link. The numbers sound apocalyptic: Carrageenan — 32% higher breast cancer risk. Mono- and diglycerides (E471) — 46% higher prostate cancer risk. Here's the twist that changes everything, and the reason you should never make health decisions from a headline. Those are relative risks. Not your actual odds. Translate it to real life. For a 60-year-old woman in this study, the absolute risk of breast cancer went from 4.1% in the lowest emulsifier eaters to 5.2% in the highest. Yes — an increase. But a nudge from 4% to 5%, not the explosion the headlines imply. The researchers themselves stated these additives do not drastically increase absolute risk. A "46% increase" on a tiny baseline number is still a tiny number. This is the single most common way health misinformation manipulates you — reporting the scary relative number and hiding the reassuring absolute one. Three more things the fear posts leave out: The company they keep. The people eating the most emulsifiers were also eating the most ultra-processed food overall. You cannot cleanly isolate one additive from an entire diet and lifestyle. The emulsifier may be a marker for the junk, not the cause. The gut disconnect. The whole theory is that these additives damage the gut. Yet the study found zero link to bowel cancer — the exact place a gut-driven cancer should appear first. When the proposed mechanism doesn't match the findings, be skeptical. The animal-study bait-and-switch. The terrifying carrageenan animal studies used poligeenan — a degraded form banned from food years ago. It's not what's in your almond milk. And no one in the study even exceeded the established safety limits. This is an observational study showing correlation. Single, unreplicated. It generates a hypothesis worth studying. It does not justify throwing out your groceries in a panic. Here's the real act of love — and the actual science. Stop hunting microscopic chemical codes on labels. That's not where your health is won or lost. The powerful, evidence-backed move is simpler and bigger: eat more whole, nutrient-dense food and fewer ultra-processed products overall. Not because one emulsifier will kill you. Because a diet built on ultra-processed food drives the insulin resistance, inflammation, and metabolic dysfunction I treat in the cath lab every day — and THAT is the real, well-established risk, dwarfing any single additive. The emulsifier headline is a distraction. The whole-food principle is the substance. Protect your peace. Base your health on context, not clickbait. And the next time a post tells you to throw something out "if you love your family" — check whether they gave you the absolute risk or just the scary relative one. They almost never do.

Some thoughts on today's market moves: 1. Iran — Two ceasefires collapsed, the Strait's still basically a parking lot, and Brent's back near $87. The SPR just dropped below 300 million barrels – lowest since 1983 –  so Washington's cushion is thinner than a Wall Street excuse. Buckle up – and invest accordingly. Dividends, in particular. 2. Canada tariffs, now???!!! — Talks blew up Saturday night, US President Donald Trump slapped 50% on $20 billion of Canadian goods. Carney's promising to match it "dollar for dollar." Nothing says "great ally relationship" like trading barbs over hockey sticks, honey and whisky. Sigh. 🤦‍️ 3. Bessent's $1 trillion parlor trick — He's eyeing the Treasury General Account to fund bond buybacks instead of issuing new bills, which is a fancy way of saying "we'll juice yields lower without admitting we're juicing yields lower." The bond market noticed. Yields dipped. Fool me once, shame on you; fool me twice, shame on me as the old saying goes. Keep duration short as I’ve repeatedly recommended. The Fed is all but irrelevant at this point, save the fact that you can watch a presser with the volume turned off and have a great substitute for Animal Planet. 4. Alibaba  $BABA— Raised $10.2 billion at an 8.4% discount to fund its AI habit, and the market's response was a 10% face-plant. Turns out investors don't love dilution any more in Hong Kong than they do on Wall Street. Some things really are universal but I’ll still take the other side of that bet all day long – the money's going straight into the one part of the business that's actually working. Case in point, Cloud Intelligence Group external revenue is running around 40% year-over-year, and management just posted its eleventh consecutive quarter of triple-digit AI revenue growth. Not a business in trouble imho. Just short-term spreadsheet vigilantes who can’t cope with actually thinking longer than a gnat. 5. Nvidia $NVDA , the marquee event this week — Will it make or break the so-called “AI trade?” Wrong question… the one you want to be asking is whether it’ll be there years from now when you need it; I believe that the answer is almost certainly yes. I think we’re going to see perhaps 100% growth top and bottom line. 75% margins may improve, too. Meanwhile, shares have returned ~13,656.45% over the past 10 years which means that every $1,000 invested back then is now worth ~$137,564.50 today. The SPY, a popular passive investing choice for many, has returned – a still very respectable but far less – ~311.10% by comparison. The game is still early days. Buy the best, ignore the rest!® Lots of money on the move and lots of opportunity waiting 😃

Ladies and girls, wake the fuck up: Uber is a predator delivery service. That piece of human garbage Adam Daley, an Uber driver, picked up a woman in her 20s in Leicester city centre at 5:30am on December 29th. Instead of doing his goddamn job, he detoured to a lay-by on Soar Valley Way near Fosse Park, pulled a knife, threatened to slaughter her, and raped her. Police think there are more victims. Of course there are. This filthy rapist wasn't some mysterious stranger he was your "convenient ride," tracking your location with an app while hiding a blade and evil intentions. Early hours, city pick-up, lone female passenger? That's prime hunting time for scum like him. Stop treating these random men like trusted chauffeurs. They know exactly where you are, where you're going, and that you're alone. One wrong move and you're at the mercy of a knife-wielding predator who sees you as meat. Harsh truth: Your safety isn't the app's priority. Assume every Uber driver could be the next Adam Daley until proven otherwise. Share trips, have backup plans, stay frosty, and for fuck's sake don't let convenience get you killed or violated. These animals are out there don't make it easy for them.

The belief that AI would eventually herald the end of humanity is not a new one. It has not arisen in response to the release of the LLMs of ChatGPT and Claude. It has not emerged as a response to recent technological developments. The story starts in the 90s, with @allTheYud. A precocious youngster with no formal education, he joined an obscure internet mailing list [created by @perrymetzger] devoted to futuristic ideas ... There he began thinking about “superintelligence.” At first Yudkowsky wanted to help create this so-called superintelligence... & ... helped establish an institute devoted to the project. It’s unclear when his optimism turned to fear, but at some point he came to believe that a superintelligent machine could escape human control and, unless it shared our goals, destroy all of humanity. Yudkowsky developed these ideas online in a series of essays and attracted a community of like-minded fellow travellers ... His prolific writing became influential among people working in Silicon Valley, many of whom work in today’s AI companies. Among the Rationalists, the imminent arrival of superintelligence – and the end of humanity that follows – is axiomatic. This conviction is shared by many people who live together in the San Francisco Bay Area practising highly unconventional lifestyles. It is not at all surprising that a community in San Francisco would share kooky or even apocalyptic beliefs – the region has been the home of eccentric subcultures for generations. But what is surprising is how far that world view has spread. In Australia, Yudkowsky’s recent book, If Anyone Builds It, Everyone Dies: The Case Against Superintelligent AI, co-written with Nate Soares, has been widely discussed in media circles. Journalist @hughriminton and ABC chairman Kim Williams both have described it as “compulsory reading”. Not everyone accepts this premise. I emailed @sapinker, to ask what he thought about superintelligence. “‘Superintelligence’, with its comic-book prefix, is more a fantasy than a coherent concept,” he wrote. “People use it as a synonym for ‘omniscience’, imagining a magical wizard that can solve all problems with pure computation. Or they imagine that the IQ scale that differentiates humans within their natural range of variation can be extrapolated indefinitely upwards. But real problem-solving requires massive amounts of knowledge about the messy, chaotic, world which divulges its hidden workings at its own pace, only through laborious experimentation. And human intelligence is not some elixir that you simply have less or more of – it’s a gadget that evolved to solve some problems with ease and others laboriously or not at all. “AI is a different kind of gadget with its own profile of strengths and weaknesses, not an enchanted brew that can grant any wish.” @GaryMarcus, a cognitive psychologist and machine learning entrepreneur, likewise believes the risk of AI leading to human extinction is virtually zero. “Humans are too geographically spread out, too genetically diverse and too resourceful to simply fall apart altogether,” he writes. “The idea that AI will kill us all in five years is preposterous.” Marcus does not argue that AI is harmless. He worries about AI being used to develop biological weapons, launch cyber attacks, spread disinformation and enable authoritarian governments – all risks that are catastrophic, if not existential. But there is an important difference between risks we can observe and risks that occur because of human negligence or malevolence, and a chain of events that exists mainly in our imagination. Part of the disagreement stems from the language we use when we discuss AI. @MelMitchell1, a professor at the Santa Fe Institute and author of Artificial Intelligence: A Guide for Thinking Humans, has criticised our habit of describing machines as if they were people. We often say an AI “thinks”, “believes”, “lies”, “schemes” or “wants” something. These words are a convenient shorthand but they also can create the impression that software has become an independent creature with intentions of its own. Mitchell makes this point when discussing the recent Hugging Face cyber-security incident, in which autonomous OpenAI agents escaped their “sandbox” – a computer environment isolated from the internet – and hacked a real-world server. “First, OpenAI did not have proper security measures in place,” she writes. “They turned off safeguards built into the models, instructed the models to find and exploit software vulnerabilities, and let the models run autonomously for weeks without sufficient human oversight.” Rather than showing autonomous AI going rogue, the incident demonstrates what can happen when humans give powerful AI systems dangerous instructions without adequate safeguards. AI is, of course, advancing rapidly. Machines can write computer code, translate languages, diagnose diseases and solve some of the hardest problems in mathematics. But to get from the AI we have today to the extinction of humanity requires several further links in a chain, none of which are guaranteed. Philosopher @mboudry, writing in @Quillette, offers a useful way to think about this. Humans (and other animal species) evolved to have a competitive drive, sometimes manifesting in selfishness and aggression, across a time span of millions of years. Our ancestors survived because they fought hard to secure food and mates. Out in the wild, these selection pressures led to the evolution of traits that enabled animals to hunt and capture their prey. But artificial intelligence does not exist in the wild. It was created by us and exists in the equivalent of a petting zoo. And just as we have been able to domesticate wheat for our food and breed dogs to be our companions, we are able to select the conditions under which AI develops. We are not selecting AI models on the basis of their ability to hunt prey in the physical world. We select them on the basis of how helpful they are to us. “We have been selecting chess computers for cognitive capacity for decades,” writes Boudry. “Their capabilities now far outstrip even the most gifted human grandmasters, yet they have not become harder to control.” Boudry accepts that an AI could slip out of human hands one day, through accident or malice. Even then, he argues, the likely result is not extinction but something like the long battle between computer viruses and antivirus software: costly and ongoing but not the end of the world. The problem with apocalyptic fears is that when they become mainstream, they can be hard to wind back – even in the face of contradictory evidence. Across the past 100 years, apocalyptic anxiety has leapfrogged from nuclear annihilation, overpopulation, environmental collapse, to rogue AI. (Some of the dangers behind these warnings were very real, of course, and some of these risks remain.) But we also have to ask what happens when this anxiety becomes locked into public policy. The ban on nuclear energy in Australia is the most obvious example of the damage this technophobia can do. Australia has 28 per cent of the world’s known uranium resources and has exported uranium for decades. Australian engineer Bobby Gallagher has invented a nuclear reactor that can be deployed on the back of a truck, a technology that has been hailed by Trump. Yet this form of clean energy remains prohibited in our country under federal law. Public anxiety surrounding nuclear weapons, radioactive fallout, accidents and waste means that while Australians can mine uranium, put it on ships and sell it to countries that use nuclear power, we cannot build commercial reactors for ourselves, using the ingenuity of our own people. The situation is a disaster. And in an age of superpower rivalry, technophobia does not remain purely a domestic matter. During the Cold War, the Soviets promoted a fear of a “nuclear apocalypse” in the West, and provided propaganda and funding for peace groups and antinuclear activists. This does not mean that the millions of people who opposed nuclear weapons were plotting against the West. Most were ordinary citizens sincerely frightened by the possibility of nuclear conflict. But the fear was useful to our adversaries. To weaken democratic nations, foreign powers do not need to invent anxiety or division – all they need to do is magnify it. In recent days Trump has said the US will not be slowing down the development of AI. In Australia, for the time being, Anthony Albanese also has resisted calls to stop AI development, instead promising national rules designed to capture its economic benefits while managing its risks. Both positions are reassuring. While AI comes with danger, we should be sceptical of any narrative that conveys inevitability around its trajectory. AI systems do not build their own data centres. They do not manufacture their own chips or connect themselves to power grids. Humans decide which systems can access the internet, whether they can control machinery, move money or operate weapons. Humans build them, finance them, deploy them and decide what powers to give them. We also should remember that some of the stories we are told owe more to myth than to science. As Nvidia chief executive Huang has said of the AI doomer narrative: “I appreciate that many of us grew up and enjoyed science fiction, but it’s not helpful. It’s not helpful to people. It’s not helpful to the industry. It’s not helpful to society. It’s not helpful to the governments.” Like every technology that has come before it, humans have agency over how AI is used. Instead of adopting a posture of fatalism, we should decide what kind of AI we want to build, what problems we want it to solve, and treat it as a tool rather than a force of nature. Read my full piece for the @australian here

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!

A new type of ad is appearing on city streets: trucks with huge digital screens showing “3D” animated ads while driving through traffic. The screens are actually flat but use a visual effect called anamorphic animation to make images look like they are popping out of the truck, making the ads appear almost real. This technology is similar to the famous 3D billboards in places like Times Square and Shibuya Crossing, but now the ads move through the streets instead of staying in one place. People say they’re too distracting, especially in heavy traffic, and it’s bad for drivers trying to pay attention.

MiniMax H3 - Text to Video, Burger Ad You can easily adapt this prompt for any food. Prompt: Create a high-end cinematic burger commercial in portrait format, designed like a premium restaurant campaign. Open on an extreme macro shot of a golden toasted bun, revealing tiny bake-specks, soft flour dusting and rich surface texture under warm directional light. The camera glides smoothly across the bun, then pulls back as the burger ingredients separate into a perfectly aligned exploded stack above a matte wooden tabletop. The full burger floats on a single vertical axis: toasted top bun, crisp ruffled lettuce, glossy tomato slices, translucent purple-red onion rings, melted cheddar, a thick char-grilled beef patty and toasted bottom bun. Each layer moves with elegant controlled motion, subtle rotation and realistic weight. Use premium food-commercial camera movement throughout: macro tracking shots, smooth dolly pushes, slow orbital moves, shallow-focus passes between ingredients and precise rack focuses. Add occasional speed ramps into slow-motion beauty moments. Let tiny crumbs, droplets and subtle steam move through the light for additional depth. Integrate bold hand-lettered white typography directly into the composition. Words appear between the floating burger layers, following the camera movement with kinetic typography. Letters can slide behind ingredients, reveal through depth, stretch slightly during transitions and lock cleanly into place. Add minimal white doodle strokes and graphic accent lines that animate around key ingredients. Lighting is luxurious and appetizing: soft directional key from upper-left, controlled fill, warm highlights, deep dimensional shadows and glossy specular detail on tomato, cheese and meat. Background is a refined warm brown gradient with subtle cinematic falloff. Build toward a final satisfying moment where all ingredients rapidly assemble into one perfect burger. The camera performs a fast controlled push-in, then settles into a polished hero shot. Steam rises gently from the patty, cheese settles over the edges and the typography resolves beside the burger. Final frame: centered premium burger hero shot, clean composition, elegant white campaign typography and subtle animated graphic accents. Visual style: luxury food advertising, modern restaurant campaign, cinematic macro photography, rich warm tones, high contrast, shallow depth of field, highly detailed food textures, sophisticated motion design, premium typography, smooth dynamic camera choreography, polished commercial finish.