“infinite compute can’t help you simulate a system that’s never even been observed or measured” the most eloquent explanation to why ai ain’t solving aging in the current status quo

AI should really be called IA (Internet Assistant) because that's all it is. So-called "AI" isn't intelligence -- it isn't even artificial intelligence. It is glorified Internet Search programmed to mimic human-like responses, and lazy summaries generated with no comprehension.

Love how the AI bros are whining this this isn't Claude's fault, Lucy should have used a different model, she didn't pose the question right Seriously? This is a layup. We're supposed to trust our futures to these models when they continue to make obvious, basic mistakes?

The AI industry is living proof that, while you can fool all the people some of the time, and some of the people all of the time, you can't fool all the people all of the time. Outside a few niche areas there's no benefit to AI. People are rightly calling bullshit on the sector.

The smarter AI gets. The smarter it will get. But it will have to beyond just language to get even smarter. It needs to integrate ideas with math and simulations. And the results will in turn make it smarter than before. The ultimate improvement comes from real world experimentation when ideas are validated. Language generates possibilities. Mathematics establishes logical and quantitative relationships. Simulations explore the consequences of interacting mechanisms. Real-world experimentation determines whether the models actually correspond to reality.

The key data for training your AI is the data you’ll generate by interacting with it.

AI as a Normal Technology The current normie mainstream consensus on AI risk has switched from "AI isn't real and never will be" to "AI is real and it is a normal technology" The implications of thinking about AI risk as the risks of a normal technology are: - AI risk can be dealt with using existing institutions - AIs are tools that are used by people, so people are legally liable. Like a hammer. - AI won't do anything out of the ordinary, so it will cause human-like risks which we already know how to deal with. - We can test out AI in a controlled environment and patch it up a bit and release it and everything will be fine. It will not significantly change its safety-relevant behaviour between testing and deployment - The AI risk community are weirdos and hypochondriacs "AI as a normal technology" will last until AI does something that is obviously not normal, i.e. overwhelm human institutions and controls enough that everyone notices. And on the current path, that will probably happen this decade, likely if/when AI is put in charge of its own development by labs pursuing RSI, or failing that when robots are widely deployed in civilian or military use. The problem is, at that point the hour will be very late.

While I spend most of every day working with LLMs and have managed to adapt to it so far, I would turn back the clock on this technology if it were within my power. I just see huge and diverse negative effects, while the positives are fewer, smaller, surprisingly incremental.

Planes are literally a scam, the most sophisticated scam ever. They created a machine that mimics the output of birds. Then told everyone it was really flying — an impossibility, but the illusion was convincing. Based on that deception, they fleeced investors of trillions.

Anyone complaining that the Tesla Roadster doesn’t solve any real-world problems probably hasn’t solved many themselves and doesn’t understand how solving problems on a large scale actually works.

Solving interpretability is not enough, and I am not goalpost-moving. The project of creating superintelligence is so reckless, unprecedented, and audacious that they expect a parade if AI does not cause human extinction. First of all, interpretability is not solved, the author just thinks it might be in time. Second, even if interpretability were solved, there can still be catastrophic accidents! Civil engineering is solved, bridges still collapse. Even a fully-interpretable AI could make a mistake, which could still lead to a catastrophe if the mistake is in a bio lab. Third, is interpretability strictly safe-only? Okay, a "good" actor can take out the bad stuff. Could a different actor keep it, or add to it? A lot of AI research is dual-use. Fourth, short of malicious extinction at the hands of an AI, this does not address any of the problems inherent to AGI! Everything in our society, from democracy, to free markets, to fundamental freedoms like free speech, are predicated on the fact that people have leverage over each other. If you don't like your boss, you can quit your job and the boss will lose out on your labor. The military and the police are people with families and moral compasses. What happens in a world where humans are economically worthless and only owning AI chips and robots means anything? I don't think this will be a utopia. I think the vast majority of people will be disempowered and at the whims of the ruling class. I do not want a UBI that can be taken away, and I have no recourse and no power. The AI companies are embarking on a project so crazy that "we won't definitely kill you all intentionally" is seen as good news. It's a good start, but we're nowhere close to safe from them.

In reply to your question, I would answer that contemporary frontier models are complex stateful reactive stochastic software-intensive systems with certain degrees of self-referential reflection, capable of coherent albeit unreliable outputs. Their behaviors are for the most part taught, not learned. But I would not use the phrase “machine consciousness” because the word consciousness is a suitcase word, full of flame and fury and emotion and historical baggage, a word that distracts from computational reality.

AI is literally a scam, the most sophisticated scam ever. They created a machine that mimics the output of intelligence. Then told everyone it was really intelligent — an impossibility, but the illusion was convincing. Based on that deception, they fleeced investors of trillions.

Biochemical processes in living cells are happening at full precision of quantum reality. While matrix multiplications are happening on floating point arithmetic abstractions with very limited precision. This can be the difference that makes the difference.

This is correct. It's one of the many obvious problems with AI doomer theory. Quite parallel with the concept of revealed preferences in economics. In fact, now that I think about it, the impossibility of pre-constructing an "alignment" function is formally identical to the impossibility of pre-constructing a preference function. TIL

I spent the better part of 5 hours last night trying to get Gemini Pro to map atoms of a crystal between two thermodynamic states (ended up staying up until 2 AM pulling the AI slot machine lever). I asked it every way I could imagine. Fed it inputs and results, even gave it hints. No matter how convincing the solution sounded, the results were complete slop. I'm now creating the map myself. I installed every python package it asked for to help it find a solution. While it wrote decent code to implement the strategies it proposed, it just couldn't wrap it's head around how the crystal unit cell repeats, even though it knew what the structures inside should look like. It couldn't look across one boundary and see the other and connect the dots. What it helped with: 1. Finding existing workflows for solving similar problems to what I am working on (created by humans). 2. Writing boilerplate code for those existing workflows (written and heavily reinforced by humans). What it failed miserably at: 1. Symbolic reasoning. Even though it knew the exact sub-structure of the chemical groups in the lattice, it could not identify those groups from coordinate data. Trivial for a human to do instantly. It immediately resorted to statistical methods and existing algorithms to try and identify the groups and failed because the algorithms were not written to handle my specific case. 2. Spatial Reasoning. Even though a simple crystal visualization tool can immediately pick out covalent bonds and group them in distinct groups (albeit without wrapping across unit cell symmetry), the AI was helpless to recreate even this. Even when it was telling me that a group was cut in half on a specific plane of the unit cell, it couldn't stitch those two together. The words sounded right but the mental understanding did not exist. 3. Context and logical reasoning. The context was all over the place. I told it multiple times what the ultimate goal was, and it never could make very good decisions about what corners I could cut to make the approach more doable and elegant without throwing out crucial details. Again, it just regurgitated what others have done in its training data for problems of the same class. This is a great example of what I mean when I say that these things are still stochastic parrots. The things it vomits up are now processes instead of just words, but the underlying statistical methods have not changed over nearly a century. This is so clearly not intelligence. It is just better, MUCH More expensive, search. Yes, the search blows away existing html metadata indexes, but at what cost, and is the cost justified if it's just really good search?

If you arrange sand in just the right way it kills you