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.

If they're providing a proxy service to bypass NAT then it makes sense they charge for it. Providing this proxying has a direct ongoing cost for them, so providing it free or for a one off fee is is totally unsustainable.

No they have a proper IPv6 implementation, but legacy IP is tunnelled over the IPv6 core and has a reduced MTU.
There are a _LOT_ of ISPs out there which are IPv6-first and have some second class setup for backwards compatibility with legacy sites.

GPUs are designed for graphics, for anything else they're basically a general purpose CPU that just by chance happens to be better than other general purpose processors for this specific task. Once the market becomes big enough, someone will expend the resources to develop ASICs which will outperform the general purpose GPUs.
Exactly the same thing happened with bitcoin.

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.

How do you know your mouse isn't faulty?

That's the inherent problem with classes, you have to teach 30+ students the same but they're not all capable of learning at the same pace or in the same way.

Kids who can't keep up fall behind, while those that are faster get bored and start to misbehave so they get labelled as troublemakers.
You also have the peer pressure from other kids, who will mock or even bully the top and bottom percentages of the class respectively, discouraging them from participating.

Catering to each child and teaching them at their own pace is obviously going to work best, but it doesn't scale to a school system.
If one or both parents is free to teach the kids that's great, but there are many cases where they aren't - some parents don't give a shit and are happy to send their kids off to school, many parents have to work and simply don't have time to teach the kids even if they would be willing/able to do so, and some simply don't have the ability to teach.

It can in some cases, depending on various factors like the codebase it's working with, the nature of the feature and how well you describe it.

You will often need to refine the prompts, or prompt it further to address bugs or things it decided to implement in a strange way. It also tends to work better with code bases that are smaller or more modular, and with code that was developed using an ai assistant rather than existing code bases.

You're right about it being like junior developers, it's good for getting mundane things done but does often need a lot of guidance.

A current generation LLM is not perfect and cannot replace a skilled employee, at best it can assist a skilled employee to do their work more efficiently.
If you understand this and have appropriate use cases, then it can absolutely be useful.

If you're trying to use it for something it's not suited for then it's going to be useless or even detrimental.

You can replace most of your developers once you have a mature product, just shift it into maintenance mode.

Because you had a specific goal in mind, knew what you were doing, knew about the different heatmap implementations available and gave precise instructions. You could probably have written this by hand yourself and it just would have taken a bit longer to do.

Problems come up when you have people who don't know what they're doing giving vague instructions to the LLM, and then blindly trusting the output. For instance if you said "draw a heatmap of $DATA" who knows what it would have come back with? it may well have tried to use the deprecated google api because there are likely a lot of examples online and in the LLM's training data.

LLMs are great when they're used to augment people who are already skilled in the art, and can generally help them save time doing a lot of the repetitive stuff. They're not some magic wand allowing someone with zero experience to achieve great results.