Sure do :) I can provide more if you want, but start there, as it's a good read. Indeed, blind people are much better at understanding the consequences of colours than they are at knowing what colours things are..

The congenitally blind have never seen colours. Yet in practice, they're practically as efficient at answering questions about and reasoning about colours as the sighted.

One may raise questions about qualia, but the older I get, the weaker the qualia argument gets. I'd argue that I have qualia about abstracts, like "justice". I have a visceral feeling when I see justice and injustice, and experience it; it's highly associative for me. Have I ever touched, heard, smelled, seen, or tasted an object called "justice"? Of course not. But the concept of justice is so connected in my mind to other things that it's very "real", very tangible. If I think about "the colour red", is what I'm experiencing just a wave of associative connection to all the red things I've seen, some of which have strong emotional attachments to them?

What's the qualia of hearing a single guitar string? Could thinking about "a guitar string" shortly after my first experience with a guitar string, when I don't have a good associative memory of it, sounding count as qualia? What about when I've heard guitars play many times and now have a solid memory of guitar sounds, and I then think about the sound of a guitar string? What if it's not just a guitar string, but a riff, or a whole song? Do I have qualia associated with *the whole song*? The first time? Or once I know it by heart?

Qualia seems like a flexible thing to me, merely a connection to associative memory. And sorry, I seem to have gotten offtopic in writing this. But to loop back: you don't have to have experienced something to have strong associations with it. Blind people don't learn of colours through seeing them. While there certainly is much to life experiences that we don't write much about (if at all) online, and so one who learned purely from the internet might have a weaker understanding of those things, by and large, our life experiences and the thought traces behind them very much are online. From billions and billions of people, over decades.

Language does not exist in a vacuum. It is a result of the thought processes that create it. To create language, particularly about complex topics, you have to be able to recreate the logic, or at least *a* logic, that underlies those topics. You cannot build a LLM from a Markov model. If you could store one state transition probability per unit of Planck space, a different one at every unit of Planck time, across the entire universe, throughout the entire history of the universe, you could only represent the state transition probabilities for the first half of the first sentence of A Tale of Two Cities.

For LLMs to function, they have to "think", for some definition of thinking. You can debate over terminology, or how closely it matches our thinking, but what it's not doing is some sort of "the most recent states were X, so let's look up some statistical probability Y". Statistics doesn't even enter the system until the final softmax, and even then, only because you have to go from a high dimensional (latent) space down to a low-dimensional (linguistic) space, so you have to "round" your position to nearby tokens, and there's often many tokens nearby. It turns out that you get the best results if you add some noise into your roundings (indeed, biological neural networks are *extremely* noisy as well)

As for this article, it's just silly. It's a rant based on a single cherry picked contrarian paper from 2024, and he doesn't even represent it right. The paper's core premise is that intelligence is not lingistic - and we've known that for a long time. But LLMs don't operate on language. They operate on a latent space, and are entirely indifferent as to what modality feeds into and out from that latent space. The author takes the paper's further argument that LLMs do not operate in the same way as a human brain, and hallucinates that to "LLMs can't think". He goes from "not the same" to "literally nothing at all". Also, the end of the article isn't about science at all, it's an argument Riley makes from the work of two philosophers, and is a massive fallacy that not only misunderstands LLMs, but the brain as well (*you* are a next-everything prediction engine; to claim that being a predictive engine means you can't invent is to claim that humans cannot invent). And furthermore, that's Riley's own synthesis, not even a claim by his cited philosophers.

For anyone who cares about the (single, cherry-picked, old) Fedorenko paper, the argument is: language contains an "imprint" of reasoning, but not the full reasoning process, that it's a lower-dimensional space than the reasoning itself (nothing controversial there with regards to modern science). Fedorenko argues that this implies that the models don't build up a deeper structure of the underlying logic but only the surface logic, which is a far weaker argument. If the text leads "The odds of a national of Ghana conducting a terrorist attack in Ireland over the next 20 years are approximately...." and it is to continue with a percentage, that's not "surface logic" that the model needs to be able to perform well at the task. It's not just "what's the most likely word to come after 'approximately'". Fedorenko then extrapolates his reasoning to conclude that there will be a "cliff of novelty". But this isn't actually supported by the data; novelty metrics continue to rise, with no sign of his suppossed "cliff". Fedorenko argues notes that in many tasks, the surface logic between the model and a human will be identical and indistinguishable - but he expects that to generally fail with deeper tasks of greater complexity. He thinks that LLMs need to change architecture and combine "language models" with a "reasoning model" (ignoring that the language models *are* reasoning - heck, even under his own argument - and that LLMs have crushed the performance of formal symbolic reasoning engines, whose rigidity makes them too inflexible to deal with the real world)

But again, Riley doesn't just take Fedorenko at face value, but he runs even further with it. Fedorenko argues that you can actually get quite far just by modeling language. Riley by contrast argues - or should I say, next-word predicts with his human brain - that because LLMs are just predicting tokens, they are a "Large Language Mistake" and the bubble will burst. The latter does not follow from the former. Fedorenko's argument is actually that LLMs can substitute for humans in many things - just not everything.

Greater Fool scheme finds Greatest Possible Fools (goverments) to keep pumping money into their zero-sum game.

It's so frustrating.

My favorite is when laymen see the word "intelligence" and think that we're talking about cognition.
We're not, and rarely have been. Diatribes like this one use language so subjectively, that it's not really even clear what they mean by "thinking" in the first place, or whether machines can or can't do it. If by "thinking" they mean "reasoning" then they are wrong. Reasoning has a definition. The stochastic parrot crowd was proven wrong again by emergent structures, and the machine does do it, or at least... it can. It's complicated.

Feels like splitting hairs to me.
The kind of thing you only put together when you're feeling threatened by existential dread and sexy waifus.

I feel like we've all been there.

Why would he feel inadequate when, according to a trustworthy source, he's a better boxer than Mike Tyson, fitter than LeBron James, hotter than Tom Brady, one of the top minds in history with a near-Olympian physique, the world's best runway model, better at resurrection than Jesus, the world's best bottom (ahem) (cough) and the ultimate throat goat?

Sooner or later, we'll end up at the point where trying to maintain the ways of the past is a fruitless fight. Teachers' jobs are no longer going to be "to teach" - that that's inevitably getting taken over by AI (for economic reasons, but also because it's a one-on-one interaction with the student, with them having no fear of asking questions, and that at least at a pre-university level, it probably knows the material a lot better than the average teacher, who these days is often an ignorant gym coach or whatnot). Their jobs will be *to evaluate frequently* (how well does the student know things when they don't have access to AI tools?). The future of teachers - nostalgia aside - is as daily exam administrators, to make sure that students are actually doing their studies. Even if said exams were written by and will be graded by AI.

"It's my estimation that every man ever got a statue made of him was one kind of a son of a bitch or another." --Malcolm Reynolds

(Ironically applies well to Joss Whedon himself. Kind of wonder if one of the show writers was thinking about Joss when they wrote that...)

The only single-source point of failure is me.

I think I saw someone swimming in some sewage en route from scraping a bear carcass off the road, let me go check.

1. I got asked once if I played world of warcraft since they say a guy with the name "thegarbz" playing. I said no. By the way I know exactly who that person is because he impersonated me as a joke. I found that flattering and funny, but it has no impact on my life beyond that.

Reminds me of my first email account ;) One of my professors said we all had to register for an email account (this was in the mid-90s) so we could submit our homework to him, so I registered his name at hotmail.com to mess with him ;)

I don't trust single points of failure.

Yeah, this. If I have to sign up to some site that I don't care at all if it gets hacked, I use a throwaway password. Oh noez, someone might compromise my WidgetGenerator.foo.bar account and generate some widgets in my name, heavens to betsy!

I found GP2.5 to be great at academic-style research and writing; it was absolutely awful at writing code. So; I would tell it to plan some thing for me and write it in a way that could be used by another agent (Claude Code) to build the code to do the thing. In this way, it has been great! I haven't yet attempted it with 3.

That said, I found GP3.0's page to be hilarious:

It demonstrates PhD-level reasoning with top scores on Humanityâ(TM)s Last Exam (37.5% without the usage of any tools) and GPQA Diamond (91.9%). It also sets a new standard for frontier models in mathematics, achieving a new state-of-the-art of 23.4% on MathArena Apex.

It then proceeds to show, lower down on the page, an example of what it can do, by showing off 'Our Family Recipes". If there's anything that touts PhD-level reasoning and writing, it's a recipe book.