peterlk

Can you be more specific about what “unbelievably terrible” means?

Nothing is stopping people from doing that. It’s just more inconvenient than other available options. If I were a politician trying to remove private communication, I would first pass something that allows scanning communications, then pass something that allows e2ee messages to be scanned, then make it illegal to use non-scannable e2ee messaging. People could still do it, but now they could be punished if they’re ever caught

Over Christmas, I spent several minutes trying to debug my beeping dashboard - it only seemed to happen sometimes while driving, so stopping didn’t let me figure it out. Eventually I discovered that it was beeping at me because my eyes weren’t on the road enough. Of course, figuring that out required me to take my eyes off the road to figure out which blinking signal was associated with this particular alarm.

Also, being constantly warned that I was speeding in rural areas where the car missed a speed limit sign caused me to start ignoring the speeding alarm within a few hours of driving the car.

I feel like there’s some lesson here in building to the lowest common denominator, and giving people products rather than tools (tools are more dangerous, but more useful), but maybe I’m just grumpy.

Yeah. I would expect that Samsung or Apple’s bundled AI things (whatever those end up being) will be good enough for consumers, and open models will be the things that actually get used in the products of the future because they will allow technical people to alter models in ways that specialize them for whatever use case they’re building for. They will also be the onramp for engineers/data scientists of the future, and thus will have more available, specialized knowledge over time.

The bitter lesson would claim that we only need one model with more data thrown at it, but I’m a bit skeptical that we’ll end up with “the one true way” of building a model, and I think there will be model tradeoffs that we can pick from as the industry matures.

Over the long term, it seems like open models must win out. This feels like it rhymes with the story of operating systems. Despite the enormous financial contributions of Microsoft and Apple, linux still won because control matters over the long term.

I predict that mech interp and things like Neuronpedia will matter more and more over time, and the frontier providers are disincentivized from providing those tools

The chain of “Why?”s here is really baffling to me.

Why do we have measles outbreaks? Because people aren’t vaccinating their kids.

Why aren’t people vaccinating their kids? Because there seems to be a generalized fear of vaccinations and their side effects

Why does this fear exist? I’m at a loss. Is it because people aren’t less educated now than they were 30 years ago? Is it because deadly diseases have fallen out of public consciousness? Was there a coordinated external effort to psyop the American people into believing such things (now I’m starting to feel like a conspiracy theorist)?

None of these reasons seem super compelling to me. The “diseases fell out of public consciousness” feels compelling at first, but why would there not have been some admonishment from grandparents who knew someone who died from polio? That seems like family lore that wouldn’t get lost.

Why not run a publicly funded ad campaign with images that look like cigarette packaging? Remind people what these now-preventable diseases look like.

It just seems wild to me that this issue seems to be getting worse. It’s like watching car crashes rise while people are cutting their own brake lines and then continuing to cut brake lines (and encourage others to di so) after their friends die. Wtf is even happening?

Yes to diffusion models! Combo pipelines of generative and diffusion models have super interesting potential

AI output reads like homepage marketing content (e.g. the text that fades in when you scroll down an apple product page) expanded to fill some context window size (the paragraphs tend to be about the same size).

What you get is vacuous, choppy, wordy, and hyperbolic. I have found that adding secondary passes for tone and style improve the readability dramatically.

You need to have an Apple developer account. Then you need to submit your app to Apple for review. Then you need to comply with a list of sometimes arbitrary corrections/requirements that they send back (there is a document that specifies what you need to do, but it is not uniformly enforced in my experience). Then, eventually, you can list your app on the app store.

It’s not super onerous, but it is much more annoying than the theoretical alternative of allowing people to install software of their choosing on their hardware (i.e. download the binary and run it)

Any sufficiently specified prompt is indistinguishable from code

I am also not in the market for a Ferrari. The problem is that it looks so pedestrian. Personally, I think the Ioniq has more personality. For a 600k car, it should have some appeal. This just looks like every other EV; it’s generic and boring.

I think they might have had much more success with a strategy like the R32 EV. Take something classic (like the Testarossa) and electrify it. Remind people that EVs are an evolution rather than a capitulation to generic boringness.

Yes they do…? Who cares if they just predict the next token? The outcome is that they can invent new abstractions. You could claim that the invention of this new idea is a combination of an LLM and a harness, but that combination can solve logic puzzles and invent abstractions. If a really large spinning wheel could invent proofs that were previously unsolved, that would be a wildly amazing spinning wheel. I view LLMs similarly. It is just fancy autocomplete, but look what we can do with it!

Said differently, what is prediction but composition projected forward through time/ideas?

This is not a very compelling argument. Things already cost money. We wouldn’t oppose a water tax because we were worried people might refuse to hydrate themselves once water was marginally more expensive. It might marginally exacerbate an existing problem, but the benefit of solving the target problem (funding roads fairly), even if imperfectly, is a much greater good

Bluesky, threads, mastodon, and everything else built on activitypub AT, etc. are still there. You can leave X behind; the only thing stopping you is the other people who could also leave X but are still there because you’re there. There are real problems with the fediverse, but they are solvable and the biggest problem is the social connections/stickiness. So start with that!

I think everyone should avoid talking about consciousness unless someone in the conversation provides a clear definition of it. If no one provides a definition, we can replace the word “consciousness” with the word “spirit”, and basically nothing about the conversation would change. Without a definition, every conversation about AI consciousness devolves into one camp saying that humans are special and consciousness is unique to them, and another camp that waves their hands about consciousness “duck typing”.

For example, we could define consciousness as the ability to communicate claimed internal states. Perhaps there could be a complexity metric that gives us a metric of consciousness.

We could define consciousness as the ability to respond to stimuli in complex ways. This would make a supermarket’s automatic doors slightly conscious.

Personally, I don’t really care how it is defined in any particular conversation, so long as it is defined. Otherwise we’re just flailing at each other in the dark.

Thanks for numberwang today. Probably my favorite meme for board meetings and all-hands presentations

https://xcancel.com/heynavtoor/status/2044433988312560051

Reminds me a bit of the coolest talk I ever got to see in person: https://youtu.be/FITJMJjASUs?si=Fx4hmo77A62zHqzy

It’s a derivation of the Y combinator from ruby lambdas

It’s because they’re playing with monopoly money. If you leverage all the money on itself repeatedly, you can make the numbers look insane. Then, you hand pieces of the leveraged money (stock options) to a bunch of executives who will be watched very closely. You don’t sell stock that’s going to go up in value, so they have to be very careful about when and how much they sell. If they sell too much, then the facade can break, and the leverage evaporates.

To be clear, there is real, underlying value & revenue. But there’s a lot of froth right now

My favorites are the micrograd series by Andrej Karpathy on youtube [0], and “Why Deep Learning Works Unreasonably Well” [1]

The greats on youtube are also worth watching: 3B1B, numberphile, etc.

[0] https://youtube.com/playlist?list=PLAqhIrjkxbuWI23v9cThsA9Gv... [1] https://youtu.be/qx7hirqgfuU?si=8zmrbazuvnz379gk

Attention is all you need…?

The short answer, as far as I’m aware, is that no one really knows. The longer answer is that we have a lot of partial answers that, in my mind, basically boil down to: model architectures draw a walk through the high dimensional vector space of concepts, and we’ve tuned them to land on the right answer. The fact that they do so consistently says something about how we encode logic in language and the effectiveness of these embedding/latent spaces.

Modern AI is a miracle. The math that makes it work is beautiful and really impressive. For example, if you wanted to map all knowledge on earth, how would you do it? AI answers that question by building a high dimensional vector space of embeddings, and traversing that space moves you through a topology of basically every concept that humans have.

Or another thought; why is it that a stochastic parrot can solve logic puzzles consistently and accurately? It might not be 100%, but it’s still much better than what you might expect from a markov model of ngrams.

Openclaw is only sort of interesting. How to vibe code your first product is uninteresting. Claims about productivity increase from model usage are speculative and uninteresting. Endless think pieces on the effects of AI slop are uninteresting. There’s a lot of hype and grift and bullshit that is downstream of this very interesting technology, and basically none of that is interesting. The cool parts are when you actually open the models up and try to figure out what’s going on.

So no, I’m not bored of talking about AI. I’m not sure I ever will be. My suspicion is that those who are bored of it aren’t digging deep enough. With that said, that will likely only be interesting to people who think math is fun and cool. On the whole, AI is unlikely to affect our lives in proportion to the ink spilled by influencers.

I don’t think this problem is that hard to solve, it just requires political will that doesn’t exist. The solution is to make it the platform’s problem. If the platform doesn’t want to deal with fraud, they don’t get to operate in that jurisdiction. Sue them into submission. If they don’t care about that geography, then there is now a gap in the market for a more local business to fill.

You have to read it all the way through. It’s a pretty hefty investment, but the series is truly a masterpiece. I had to read the whole series twice to feel like I was actually starting to understand some of the symbolism. I don’t blame people for not being able to get into it; it’s dense. But it’s so epic and there is so much symbolism and philosophy packed in.

I have quite a bit of family in Germany, and have had several friends move from the US to Europe. Europe absolutely knows that they have an opportunity to capture a ton of talent right now. If you have skills that are in demand, basically any country in the Schengen zone will find a way to get you a visa. For example, if you’re a trans researcher, you will find open arms at academic institutions in Europe.

You could also lie and claim your address as a US address, and then just live in another country. This is obviously illegal, but I’ve met a few people who made it work for a while. But I’m also speaking abstractly on the internet, so maybe I’m just making all this up.

Yes. Other humans are generally accepting of mistakes below some frequency threshold, and frontier models are very robust in my experience

I think this article conflates (at least) two problems.

The first is that very few people (especially rich people) are anonymous. A motivated person who has had a psychotic break can be very dangerous, and if you’re even a little bit famous, the probability of that happening to you goes up substantially.

The second issue is the one that everyone is getting riled up about - wealth inequality.

These are distinct issues, and I think it does harm - in the form of polarization - to not explicitly call them out.

Shameless plug. We’re working on something like this. thismachine.ai. It’s still early, but interested to get feedback. The slack/chat part is still behind a feature flag. Let me know if you want to use it

The solution is parents! Stop making your bad parenting my problem!

I have been having this conversation more and more with friends. As a research topic, modern AI is a miracle, and I absolutely love learning about it. As an economic endeavor, it just feels insane. How many hospitals, roads, houses, machine shops, biomanufacturing facilities, parks, forests, laboratories, etc. could we build with the money we’re spending on pretraining models that we throw away next quarter?