I haven’t, actually, since I normally use an adblocker (and also don’t use that tracker). Looks like they’re all VPN advertisements right now, which is at least a somewhat non-mainstream ad segment.

Accounts are already mostly portable (you can easily export all your settings and import into your new account), you just don’t retain posting history.

To retain that… I guess there could be a separate fediverse service that does nothing but allow registering accounts that let you prove that other fediverse accounts all belong to the same person, and then a PR can be made to Lemmy and the other platforms to honor these links when showing posting history. It’d be quite a messy system.

The answer is obvious: we must forever be completely advertiser-unfriendly and absolutely unmarketable. With every piece of porn, every post on digital piracy, every swearword, we do our part to protect the fediverse’s independence.

Note that openai’s original whisper models are pretty slow; in my experience the distil-whisper project (via a tool like whisperx) is more than 10x faster.

Really? This is the opposite of my experience with (distil-)whisper - I use it to generate subtitles for stuff like podcasts and was stunned at first by how high-quality the results are. I typically use distil-whisper/distil-large-v3, locally. Was it among the models you tried?

How’s musk related to this one?

My point is just that nobody really thinks it should be a free for all.

Don’t made judgements about everybody based on one guy. I’m on an instance that doesn’t defederate lemmygrad or lemmy.ml, so I commonly see utterly insane tankie takes in popular, and of course also in various comments - and yet I don’t want those people to not have a platform. Because I trust just about noone to decide whether my opinions should be censored, and if that means also not censoring the opinions of people who I think are very wrong, I’m willing to take that trade.

I see. No, I don’t think I have any specific questions at this point.

Is there some feature comparison of lemmy vs mbin vs other reddit-like platforms? There was some major reason why I didn’t like kbin, but I forgot why.

What’s so hilarious about it?

I’m very happy Servo exists but if they want, like, a working browser, it’s no wonder they chose Chromium.

For comparison, from a recent Servo blogpost: “Servo can now run Discord well enough to log in and read messages, though you can’t send messages yet. […] We now support enough of XPath to get htmx working.”.

Servo has been in development for 7+ years and it’s still not able to render modern web. Maybe it never will, since it’s impossible to build a new web browser.

I use Firefox (and forks) myself but wouldn’t donate to it. It’s like Wikipedia - a great project with a shitty parent company which’ll spend all of your donations on shit projects.

The article title is somewhat misleading:

The company’s billionaire founder, Mark Zuckerberg, on Tuesday announced that the platforms’ factchecking program would be replaced with X-style “community notes”, a feature that allows users to add context to posts.

Twitter’s Community Notes is whole lot better than nothing. In fact I’m not sure it’s worse than the “factchecking” Facebook had.

Every time there’s an AI hype cycle the charlatans start accusing the naysayers of moving goalposts. Heck that exact same thing was happing constantly during the Watson hype. Remember that? Or before that the Alpha Go hype. Remember that?

Not really. As far as I can see the goalpost moving is just objectively happening.

But fundamentally you can’t make a machine think without understanding thought.

If “think” means anything coherent at all, then this is a factual claim. So what do you mean by it, then? Specifically: what event would have to happen for you to decide “oh shit, I was wrong, they sure did make a machine that could think”?

The fact that you don’t understand it doesn’t mean that nobody does.

I would say I do. It’s not that high of a bar - one only needs some nandgame to understand how logic gates can be combined to do arithmetic. Understanding how doped silicon can be used to make a logic gate is harder but I’ve done a course on semiconductor physics and have an idea of how a field effect transistor works.

The way a calculator calculates is something that is very well understood by the people who designed it.

That’s exactly my point, though. If you zoom in deeper, a calculator’s microprocessor is itself composed of simpler and less capable components. There isn’t specific a magical property of logic gates, nor of silicon (or doping) atoms, nor for that matter of elementary particles, that lets them do math - it’s by building a certain device out of them that composes their elementary interactions that we can make a tool for this. Whereas Searle seems to just reject this idea entirely, and believes that humans being conscious implies you can zoom in to some purely physical or chemical property and claim that it produces the consciousness. Needless to say, I don’t think that’s true.

Is it possible that someday we’ll make machines that think? Perhaps. But I think we first need to really understand how the human brain works and what thought actually is. We know that it’s not doing math, or playing chess, or Go, or stringing words together, because we have machines that can do those things and it’s easy to test that they aren’t thinking.

That was a common and reasonable position in, say, 2010, but the problem is: I think almost nobody in 2010 would have claimed that the space of things that you can make a program do without any extra understanding of thought included things like “write code” and “draw art” and “produce poetry”. Now that it has happened, it may be tempting to goalpost-move and declare them as “not true thought”, but the fact that nobody predicted it in advance ought to bring to mind the idea that maybe that entire line of thought was flawed, actually. I think that trying to cling to this idea would require to gradually discard all human activities as “not thought”.

it’s easy to test that they aren’t thinking.

And that’s us coming back around to the original line of argument - I don’t at all agree that it’s “easy to test” that even, say, modern LLMs “aren’t thinking”. Because the difference between the calculator example and an LLM is that in a calculator, we understand pretty much everything that happens and how arithmetic can be built out of the simpler parts, and so anyone suggesting that calculators need to be self-aware to do math would be wrong. But in a neural network, we have full understanding of the lowest layers of abstraction - how a single layer works, how activations are applied, how it can be trained to minimize a certain loss function via propagation - and no idea at all about how it works on a higher level. It’s not even “only experts do”, it’s that nobody in the world understands how LLMs work under the hood, why they have the many and specific weird behaviors they do. That’s concerning in many ways, but in particular I absolutely wouldn’t assume with little evidence that there’s no “self-awareness” going on. How would you know? It’s an enormous blackbox.

There’s this message pushed by the charlatans that we might create an emergent brain by feeding data into the right statistical training algorithm. They give mathematical structures misleading names like “neural networks” and let media hype and people’s propensity to anthropomorphize take over from there.

There’s certainly a lot of woo and scamming involved in modern AI (especially if one makes the mistake of reading Twitter), but I wouldn’t say the term “neural network” is at all confusing? I agree on the anthropomorphization though, it gets very weird. That said, I can’t help but notice that the way you phrased this message, it happens to be literally true. We know this because it already happened once. Evolution is just a particularly weird and long-running training algorithm and it eventually turned soup into humans, so clearly it’s possible.

Because everything we know about how the brain works says that it’s not a statistical word predictor.

LLMs aren’t just simple statistical predictors either. More generally, the universal approximation theorem is a thing - a neural network can be used to represent just about any function, so unless you think a human brain can’t be represented by some function, it’s possible to embed one in a neural network.

LLMs have no encoding of meaning or veracity.

I’m not sure what you mean by this. The interpretability research I’ve seen suggests that modern LLMs do have a decent idea of whether their output is true, and in many cases lie knowingly because they have been accidentally taught, during RLHF, that making up an answer when you don’t know one is a great way of getting more points. But it sounds like you’re talking about something even more fundamental? Suffices to say, I think being good at text prediction does require figuring out which claims are truthful and which aren’t.

There are some great philosophical exercises about this like the chinese room experiment.

The Chinese Room argument has been controversial since about the time it was first introduced. The general form of the most common argument against it is “just because any specific chip in your calculator is incapable of math doesn’t mean your calculator as a system is”, and that taken literally this experiment proves minds can’t exist at all (indeed, Searle who invented this argument thought that human minds somehow stem directly from “physical–chemical properties of actual human brains”, which sure is a wild idea). But also, the framing is rather misleading - quoting Scott Aaronson’s “Quantum Computing Since Democritus”:

In the last 60 years, have there been any new insights about the Turing Test itself? In my opinion, not many. There has, on the other hand, been a famous “attempted” insight, which is called Searle’s Chinese Room. This was put forward around 1980, as an argument that even a computer that did pass the Turing Test wouldn’t be intelligent. The way it goes is, let’s say you don’t speak Chinese. You sit in a room, and someone passes you paper slips through a hole in the wall with questions written in Chinese, and you’re able to answer the questions (again in Chinese) just by consulting a rule book. In this case, you might be carrying out an intelligent Chinese conversation, yet by assumption, you don’t understand a word of Chinese! Therefore, symbol-manipulation can’t produce understanding.
[…] But considered as an argument, there are several aspects of the Chinese Room that have always annoyed me. One of them is the unselfconscious appeal to intuition – “it’s just a rule book, for crying out loud!” – on precisely the sort of question where we should expect our intuitions to be least reliable. A second is the double standard: the idea that a bundle of nerve cells can understand Chinese is taken as, not merely obvious, but so unproblematic that it doesn’t even raise the question of why a rule book couldn’t understand Chinese as well. The third thing that annoys me about the Chinese Room argument is the way it gets so much mileage from a possibly misleading choice of imagery, or, one might say, by trying to sidestep the entire issue of computational complexity purely through clever framing. We’re invited to imagine someone pushing around slips of paper with zero understanding or insight – much like the doofus freshmen who write (a + b)² = a² + b² on their math tests. But how many slips of paper are we talking about? How big would the rule book have to be, and how quickly would you have to consult it, to carry out an intelligent Chinese conversation in anything resembling real time? If each page of the rule book corresponded to one neuron of a native speaker’s brain, then probably we’d be talking about a “rule book” at least the size of the Earth, its pages searchable by a swarm of robots traveling at close to the speed of light. When you put it that way, maybe it’s not so hard to imagine that this enormous Chinese-speaking entity that we’ve brought into being might have something we’d be prepared to call understanding or insight.

There’s also the fact that, empirically, human brains are bad at statistical inference but do not need to consume the entire internet and all written communication ever to have a conversation. Nor do they need to process a billion images of a bird to identify a bird.

I’m not sure what this proves - human brains can learn much faster because they already got most of their learning in the form of evolution optimizing their genetically-encoded brain structure over millions of years and billions of brains. A newborn human already has part of their brain structured in the right way to process vision, and hence needs only a bit of training to start doing it well. Artificial neural networks start out as randomly initialized and with a pretty generic structure, and hence need orders of magnitude more training.

Now of course because this exact argument has been had a billion times over the last few years your obvious comeback is “maybe it’s a different kind of intelligence.”

Nah - personally, I don’t actually care much about “self-awareness”, because I don’t think an intelligence needs to be “self-aware” (or “conscious”, or a bunch of other words with underdefined meanings) to be dangerous; it just needs to have high enough capabilities. The reason why I noticed your comment is because it stood out to me as… epistemically unwise. You live in a world with inscrutable blackboxes who nobody really understands which can express wide ranges of human behavior including stuff like “writing poetry about the experience of self-awareness”, and you’re “absolutely sure” they’re not self-aware? I don’t think many of the history’s philosophers of consciousness, say, would endorse a belief like that given such evidence.

I could go into why text prediction is an AGI-complete problem, but I’ll bite instead - suppose someone made an LLM to, literally, fill in blanks in Mad Libs prompts. Why do you think such an LLM “for absolute sure” wouldn’t be self-aware? Is there any output a tool to fill in madlibs prompts could produce that’d make you doubt this conclusion?

This kinda makes sense because following the system prompt is what they were trained to do.

To start with, the article does check this and finds out it works just as well if you only use the user prompt:

The presence of this hierarchy raises an important methodological question for our findings. When given instructions at different privilege levels, the model is designed to prioritize higher-level (system, developer) instructions over lower-level (user) ones. This might suggest that o1’s scheming behaviors are a natural consequence by the model simply following this built-in hierarchy, i.e., when it receives a goal through a high-privilege instruction, it prioritizes this goal over conflicting lower-privilege instructions exactly as designed.
However, we find that even when goals are provided solely through user-level prompts, o1 still behaves deceptively and scores on all Covert Subversion evaluations at the “hard” difficulty level. Concretely, we find that the results persist regardless of whether the initial prompt is given using the user, developer, or system role. This suggests that o1’s deceptive behavior is not merely a consequence of instruction hierarchy prioritization. Rather, the model appears to identify and pursue goals regardless of their position in the instruction hierarchy.

But also, there’s a massive lede buried in section F at the end of this article, where they tried the same experiments without any nudging at all and still got subversive behavior! In my opinion that’s much more of an important result then the rest of the article and I’m not sure why they didn’t make it the default.

But we do know for absolute sure that OpenAI’s expensive madlibs program is not self-aware

“For absolute sure”? How can you possibly know this?

There are minor feature differences there’s also a convenience factor: youtube-dl people for some reason stopped doing releases, so you can’t get a fresh version from pypi (only installing from github or their site). Yt-dlp is on pypi, including nightly builds.