Another example of a vaccine that could prevent even more misery and death if more got it. The spread of science misinformation causes parents to make poor health choices.

It’s getting busy up there. Most of those are Starlink satellites. A composite image was built from 578 individual 8-second exposures taken over nearly two hours at Mount Taranaki, New Zealand, featuring an estimated 14,450 satellite trail appearances from about 1,400 distinct satellites Photographer and credit to: Brendan Larsen Photography

I have frequently posted about the serious problems we will face long after Trump is a distant painful memory. I have focused primarily on how far too many Americans read at a 5th grade level, suffer from civic illiteracy, and can't engage in any critical thinking. So what fills

Wow. I woke up to an impressive number of upset people who apparently don’t understand how LLMs work and can’t cope well with me pointing it out. An LLM is a statistical next-token prediction model. That’s how it works. It’s an extraordinarily sophisticated calculator performing math you could theoretically do by hand (if you were sufficiently masochistic). Math doesn’t know what it’s calculating. Pi doesn’t spend its evenings contemplating the nature of circles, and c doesn’t ponder the relationship between matter and energy. That doesn’t make the results any less accurate, impressive, or useful. Between inference requests, an LLM isn’t sitting around thinking. It isn’t reconsidering its answers, wondering whether it made a mistake, contemplating its existence, or questioning whether it’s conscious. Its weights remain fixed (it doesn’t learn), and computation resumes when it receives input. We know this because we designed these systems. The architectures, mathematics, implementations, and research are extensively documented. You can study them yourself. Of course, some people immediately lean into comparisons of biological intelligence or philosophical debates over consciousness because that is what they know. But biological intelligence and artificial neural networks are fundamentally different systems. Similar outputs don’t establish equivalent internal experiences. I experience consciousness directly. I infer that other humans do too because we share biological structures and processes. We don’t have equivalent evidence for an LLM, and nothing about its known operation gives evidence of conscious experience. Which brings me back to my original point. To me, understanding is conscious comprehension. It means being aware of information, recognizing its meaning, and experiencing that comprehension. If I wasn’t conscious I could not understand. We can perform actions unconsciously. We can produce correct results without knowing why they’re correct. But I distinguish those capabilities from consciously understanding what we’re doing. An LLM can solve complex problems, explain difficult concepts, and produce remarkably accurate information. I give it full credit for those capabilities. But producing an answer that we rationalize as understanding isn’t the same as experiencing understanding. You don’t have to agree with my definitions. We can debate functional understanding versus conscious comprehension all day, but that won’t establish whether an LLM actually experiences anything. The right debate is this… Could machines exhibit something different from human consciousness, something measurable, meaningful, and worth studying? I think so, absolutely! I’m entirely open to discussing “machine consciousness” in terms of observable, testable computational properties. We built these systems, we can instrument them, examine their internal mechanisms, and investigate what they’re capable of. That seems far more productive than endlessly arguing over whether a machine experiences life the way humans do. So here’s my question: How would you define machine consciousness, and more importantly, how would you measure it?

Look at these gorgeous, dynamic, orange mitochondria surrounding the blue nucleus. A view of life from beneath a cell monolayer in a parti dish. Whatever angle you look at it, life is always dynamic and pretty special.

I block liberally & often preemptively. If I block you you didn’t “win”. You’re just DQ’d. I try to walk thru questions interactively with folks. It requires their wanting to learn & good behavior. When it’s clear one or both characteristics are lacking, the “pig” rules kick in.

Clearly written. Concise. Specific. Thanks to @Gianl1974 for sharing

Wonder why the Trump administration (who invented MAHA) didn’t buckle under to the September Ultimatum from 150 MAHA anti-vax influencers? Could be they’re not as crazy as they want to appear.

This explains a lot about Trump’s success at getting himself elected. But there’s one more thing: his pussy-grabbing blustering bully rich bad boy persona that some aspire to, others are attracted to, and no one alive now can replicate. The line of American Hitlers ends with him.

Do we live in a world where AIs can be wildly superhuman at every single task? Or a world where different tasks have different inherent limits, and AI’s superhuman performance at cybersecurity and math won’t translate to many consequential tasks? This is a key crux that distinguishes the normal technology worldview from the superintelligence worldview, and it has nothing to do with the pace of AI progress — it's about our different assumptions about the world. Here's an intuition pump for why I think it's the second world: different tasks have different fundamental limits. *Category 1: Some tasks are like making a building taller*: there is no ceiling to how well you can do. More compute and better algorithms can lead to wildly superhuman performance. The entire history of computing is the history of building tools that allow us to be “superhuman” at computational tasks. For example, compilers are superhuman assembly code writers, and fuzzing tools are superhuman vulnerability detectors. AI builds on a long line of such improvements: solving math problems and finding exploits in code are two recent examples. *Category 2: Some tasks are like making a ball rounder*: there is only so much room for improvement, and the best AI systems can’t wildly exceed humans. Take persuasion. Some people believe AI can persuade anyone of anything, even convincing military commanders to launch nuclear weapons. In fact, “superpersuasion” is one of the pathways through which many catastrophic risk scenarios unfold. But the AI as Normal Technology thesis is that there are many consequential tasks that are limited by external factors, so such wildly superhuman performance is not possible. This is especially true against humans who use simple computational tools. Tasks like persuasion, or predicting the outcomes of social events (such as elections), fall into this category. Other tasks, like writing, can’t be ranked on a single scale; different people find different writing styles compelling. AI writing also quickly wears thin: even if a user is impressed by AI writing at first glance, after reading thousands of words of the same type of prose, it becomes mundane and irritating. All of these are different from Category 1 tasks like playing chess, where even expert humans can’t do much better than deferring to an AI system at each turn. *Category 3: Some tasks require experiments in the real world*, and the bottleneck isn’t the computational task; it’s the data collection. One example is Moderna’s Covid vaccine: it took two days to design the vaccine, but almost a year to test and authorize, even during a record-breaking vaccine-development effort! This is why a “superintelligence” won’t cure cancer from inside a datacenter: the bottleneck to many of AI’s most sought-after real-world impacts (such as developing cures to diseases) is in the real world, and requires experiments that we won’t (and shouldn’t) hand over to AI. People concerned about AI risks often assume that we live in a world where almost everything fits into Category 1, and we might soon see AIs with wildly superhuman performance at all tasks. I agree it would be scary to live in that world. One reason @random_walker and I are more optimistic about our ability to remain in control is that we don’t think we live in the Category 1 world.

Maintaining herd immunity is not just a public health goal - it is a moral obligation. Measles requires at least 95% 2-dose coverage to protect newborns, immunocompromised people and others who cannot be vaccinated. Quite frankly our choices determine whether others remain safe.

This platform is worthless for confronting vaccine denialism. I need to look someone in the eyes when I tell them they’re a blithering idiot for believing the vaccine lies or a monster for knowingly spreading them.

The irony: every one of today’s National Medal of Science recipients is an immigrant. This administration celebrates their achievements while restricting green card pathways for skilled workers and discouraging international students from coming here.

Steve Kirsch continues to mangle data like a wild horse sitting in front of a mainframe. Parent poll shows that recall, ascertainment, attribution, selection, and temporal biases are alive and well. It says NOTHING ABOUT vaccines and autism.

RT @PeterHotez: Up until the collapse of the Soviet Union in 1991, the USSR maintained an extensive bioweapons capability. Ken Alibek (aka…

Interesting that around here (western NY on Canadian border) not one Republican candidate indicates their party affiliation in TV ads, mail outs or lawn signs.

Vaccine disinformation has a purpose. Discouraging vaccination has a measurable cost in lives, as well as on the economy & on functioning democracies. If you don’t believe this will impact Canada, just look south to see what it’s done to my home country

Two years ago the reflecting pool was reflecting, the East Wing existed and was welcoming visitors to the People’s House, and the Kennedy Center was financially stable and artistically thriving. Gasoline was semi-affordable and USAID was saving human lives all over the world. Go vote. We don’t have to live like this.

1/ There is a persistent claim that COVID vaccination produced “mass casualties”—sometimes wrapped in rhetoric about a future “Nuremberg 2.” There is a straightforward way to test the underlying premise: What actually happened to the risk of hospitalization and death among vaccinated versus unvaccinated people? And then a second question: Did misinformation measurably influence whether people accepted vaccination? The answers are considerably better documented than social media arguments often suggest. A thread on what the evidence actually shows.

Just watched The Unabomber flick. Spoiler alert: Ted was a Libertarian.

His face on a $3 bill would be more appropriate.