I feel like I am the only one thinking AI is actually much better than me in the things I'm supposed to do well. I feel like that for years now, so it's not about the latest generation of models. I can't imagine a single thing I can really compete with an AI at this stage. I am not sure if I am under-skilled or others are overconfident. Maybe people who feel like me don't say this out laud.
agree. it's strange reading the loud voices that are counter to my lived experience. llms just have seemingly infinite depth - or can at least debug and execute without fatigue.
After watching a lecture of Dreyfus on Heidegger and skimming his books I think I begin to see what's going on. Hubert Dreyfus teaches a specific set of philosophers beginning with Heidegger. His brother Stuart is an industrial engineer working on programming very early computers (50s) for Operations Research. They both worked at RAND corporation and involved in early "AI" research projects for military. Those AI projects of course were logic based problem solvers of 50s and 60s. But Hubert sees a problem with this work. What they do is incompatible with the philosophical tradition he belongs:
"I began reading NSS's landmark papers with a mixture of excitement and fear. Perhaps Hobbes, Kant, and Husserl were right after all, and the human mind was an analytic engine. But then what about the seemingly plausible arguments of Merleau-Ponty, Heidegger, and Wittgenstein, which I had come to accept? As I read the RAND papers my excitement and fear turned to disappointment and relief."
I think this was more like a cognitive dissonance than an actual contradiction. It was about choosing sides between Heidegger and maybe Descartes for him. That's why his objection sounds personal and dogmatic.
So what was the big idea of Heidegger? Elephant in the room is the concept of "Dasein" (being-there), which Dreyfus think Heidegger is a genius for being the first philosopher noticing that. Dasein is an entity, with special mode of being. Only *human-beings* can be Dasein. So it's not an "object" like a table, that has properties (like in object oriented programming). It's not an equipment like a hammer (objects having methods). Human-beings, and only human-beings (definitely not LLMs, not dogs) can have this special way of being, or Existence. This idea of course has some roots in Christian Theology, as Heidegger himself.
I think this is the reason of the strong opinions. A bit dogmatic. A belief that humans are fundamentally different beings. So an Equipment trying to mimic "Dasein" is categorically wrong (even impossible) in this belief system. The problem doesn't have to be Dasein itself, but you get the idea. It's either-or. If modern AI research is not flawed, their philosophy must be flawed. Since it can't be flawed, AI research must be flawed. Since research is trial and error, failures are the part of the process. But for them, each failure is an "increasing evidence".
For me it's not about the capabilities but what they can be used for. Think of all the recent drama between Anthropic and the Department of War. A real wake up call (especially if you are not a US citizen). Proves that AI is essentially a Surveillance and Warfare technology (which justifies the big valuations).
AI automatically analyzes all your social media posts in your life and can generate a pretty accurate profile about you in a second. We have no privacy anymore. Social media sites like Reddit already do that for moderation. Others do for more sinister reasons.
Note that Profiling is illegal in many countries. But laws can't protect us anymore.
Yes, it was always possible to that manually. But with AI it's so easy, fast and accurate to do in large scales. A hacker having access to your computer, reading your mails and messages is one thing. An AI reading and analyzing all your mails, messages and data is something different. Doing this for whole demographics (Cambridge Analytica style) is at another level.
> I highly recommend people in the AI research space should read philosophy and modern linguistics.
On the contrary, I highly recommend people in Philosophy of mind and linguistics should start reading AI research papers because their theories and ideas are highly outdated, even ancient. Your books are from 1927 and 1972 respectively and Turing's article is from 1950s. And they are relatively new with respect to other works in Philosophy.
If one doesn't adequately understand what we have in 2026, how can they theorize about it? As others they don't understand how the mind/brain work, BUT ALSO they don't understand how the AI works.
Also with this mindset that we can't understand seemingly complicated things, there would be no advancement in science and technology.
I think philosophy people and Linguist will catch up in a century, like they did with Turing. Philosophers of this century are not in humanities or literature. They are in science and engineering.
Heidegger was trained on priesthood and Theology. You should read greater minds like Hinton, LeCun etc. if you want to think on these things. They are the real Philosophers.
That's my point. Everything from 1927 is already in plain sight and a part of the current public knowledge. Horizon can only be expanded at the cutting edges.
> On the contrary, I highly recommend people in Philosophy of mind and linguistics should start reading AI research papers because their theories and ideas are highly outdated, even ancient. Your books are from 1927 and 1972 respectively and Turing's article is from 1950s. And they are relatively new with respect to other works in Philosophy.
People in philosophy and cognitive linguistics do read AI research. Don't get fooled by the publishing dates: although Heidegger's work dates from 1927, the work is contemporary. The same happens with Dreyfus' work. Again, publishing dates don't mean anything here.
Maybe you can clarify why they are outdated.
> If one doesn't adequately understand what we have in 2026, how can they theorize about it? As others they don't understand how the mind/brain work, BUT ALSO they don't understand how the AI works.
I would say that people involved in the critique of AI do know how it works. But I've found that is normally the case that people in AI research does not have the framing provided by works in philosophy or cognitive linguistics.
> I think philosophy people and Linguist will catch up in a century, like they did with Turing. Philosophers of this century are not in humanities or literature. They are in science and engineering.
What do you base your claims on? Plenty of philosophers work in humanities, literature, sociology as well as science and engineering. Philosophers not catching up? The critique on automation and AI already dates from the early 20s if not before.
> Heidegger was trained on priesthood and Theology. You should read greater minds like Hinton, LeCun etc. if you want to think on these things. They are the real Philosophers.
Sorry, but this does not make too much sense. Hinton and LeCun are great in their own fields. But seriously, they are not philosophers, they are inventors.
The First Edition (1995) of the classic textbook Artificial Intelligence: A Modern Approach by Russell and Norvig talks about the criticisms of Dreyfus quite extensively.
In the second edition (2003) they conclude:
"In sum, many of the issues Dreyfus has focused on-background commonsense knowledge, the qualification problem, uncertainty, learning, compiled forms of decision making, the importance of considering situated agents rather than disembodied inference engines-have by now been incorporated into standard intelligent agent design. In our view, this is evidence of AI's progress, not of its impossibility."
In the 4th edition (2020) Dreyfus reduces to a paragraph and Heidegger is just a reference in a footnote.
I have that book at home and I'll check as soon as I get back from a trip.
When Dreyfus' book appeared in 1972, it received really harsh criticism from the then AI community. Dreyfus actually comments on that criticism in the revised 1992 edition.
I just don't see how Dreyfus critique of AI has been dealt with in modern AI: the critique is aimed at fundamental issues, not at the technical issues.
It is true that the critique written by Dreyfus is based on GOFAI algorithms from the 60s, but it is also true that if you read the book today, you'll find lots of similar situations and a similar way of thinking about the possibilities of AI, as well as the same underlying assumptions.
And as a side note, outdated means that it does not apply anymore, or that is not relevant anymore. Which is different from 'establishing a dialogue' with the text/author, in a way that 'seems' not to be relevant anymore. If you say that Dreyfus' book is outdated just because the 4th edition of Norvig's text only mentions it in a footnote, you are assuming that Norvig and Russell's opinion are definitive. They might be not.
I have authors like Norvig, Russel, LeCun, Minsky and other in the field in high regard. But they are normally not trained in either modern linguistics nor philosophy. Let alone the rest, large amount of researchers in the field. AI research is a complex field, and maybe (in this we could follow Foucault) not even a science. Doing research in an area of study does not turn it into science.
It is precisely philosophy, and even more contemporary philosophy, the discipline that focuses on how we build knowledge, and how we experience the world. Two really important, almost fundamental, topics that directly contribute to how AI is developed as a field of knowledge.
I have the third edition, so I can only speak for it. Being the 3rd edition of the book, I assume that it is the 3rd time the text is revised, so I expect the other two editions (1st and 2nd) to adolesce from the same problem, which I state in the following paragraphs.
The mention to Dreyfus in the 3rd edition of Artificial Intelligence, a Modern Approach, by Stuart Russell and Peter Norvig, is made in 4 different places of the book, referencing four different problems.
The first mention is in page 279, effectively in the bibliographical notes, and it is about something called the 'frame problem'. Dreyfus presents this problem in the 1972 edition of the book, as a problem pertaining 'how to differentiate figure from ground', or 'how to account for what is important and what is not in a specific scenario'. But the solution to the problem that Norvig and Russel cite (Ray Reiter, 1991) is from a paper that _changes the conditions of the problem_, even _change the problem completely_ (by reductionism) to 'how to detect objects that do not change after an action'. They claim the problem solved, but they are actually not addressing Dreyfus criticisms, and misleading the reader to think that the problem is actually solved. The frame problem, by now, is still unsolved (and is one of the most difficult problems to solve).
The second mention is in page 1024, under a section called 'Weak AI: Can machines act intelligently?', and subsection 'The argument from informality'. The section mentions the books What Computers can't do (1972) and What Computers still can't do (1992), as well as Mind over Machine (1986). Unfortunately, this section completely misunderstands the critique of AI that Dreyfus exposes in those books. The whole section is misleading, obfuscating or tergiversing the critique from Dreyfus to fit the purpose of Norvig and Russell (mainly, to show that advances in machine learning and AI can make a solid base for machines that 'act intelligently').
The third mention is in page 1049, and it tries to undermine the first-step fallacy (which is similar to the fallacy of composition). Again, they do it by completely dismissing Dreyfus' critique, not addressing the issue. Then they go on talking about 'rationality' (as explained in chapter 1), but with a trick: only in terms of machines, goal-oriented expectations, computing resources. Dreyfus' critique is about the overall AI enterprise and the search for 'artificial' intelligence, Russell and Norvig discourse in this section first reduce Dreyfus' critique to what they can handle, to their own terms. That is, they evade the issue.
The fourth and final mention, in page 1072, is the bibliographical citation.
Re-reading the non-technical, but more theoretical parts of the book just made me realize how poorly constructed the book is. For example, the definitions given about AI in page 2 are just laughable. Compare with an introductory text on Psychology [0].
I see people give too much importance to specific engineering design choices of the current generation of LLMs. Tokenizer is not an absolutely essential part of the system. It’s just and adapter for text input/output. It can be eliminated completely and model can use bytes directly.
I think the short story captures this well. Weights (connections) are the essential and philosophically important part. They do the thinking, memory, singing etc.
A tokenizer is roughly and approximately Huffman-coding sequences of input (bytes of English etc) into shorter sequences (list of tokens), as a performance optimization.
As you said, it's not in any way intrinsic to the LLM, though it may be a very necessary optimization on today's hardware.
IMO, we are probably talking about a 6x slow down (for typical english). You would need to be absolutely stupid not to implement some kind of optimisation along these lines.
Slower and maybe a little dumber; But it would work.
My thinking is that for most tasks, a byte-orientated LLM still needs something like the wide "single activation per word" formatting that the tokeniser mostly provides. And it will likely waste its first and last few layers implementing a replacement tokeniser (and would probably do a much better job at it). It would also need to decode and encode unicode at the same time.
My estimate is that it might lose about 10% of its weights to these new tasks. Your 80B parameter model becomes as smart as a 72B parameter model - Measurably dumber, but not drastically so.
I’ve worked in VR for a long time (including visionPro) and my eyesight definitely got worse. The most ironic thing to me is how iPhone has this screen distance warning telling you to move the screen further from your face while Vision Pro is literally an iPhone strapped to your face.
I was told the issue isn’t the physical distance of the screen to your eyes, but the distance of where your eyes are focusing? So in VR if you focus on an object a meter away it shouldn’t strain your eyes as much as a phone screen 10cm away? No idea if this is scientifically proven.
Your eyes are still looking at an object (roughly?) 10cm away from your face: the screens. Your eyes are not adjusting focus. Any focus (or blur) you see in VR is simulated depth.
So yes, the issue is indeed the distance where your eyes are focussing, caused by the fact that they're constantly focussing on something very close to your face.
My optician told me its like stretching your arm while holding something heavy. At first that's no problem. But eventually your muscles will start burning and you can't hold it and even when you relax your arm it still hurts if you held it for too long.
As far as I'm aware there are no VR headsets yet that adjust the live generated depth vision based on the diaphragm of your eyes. That would be wild.
> Your eyes are still looking at an object (roughly?) 10cm away from your face: the screens. Your eyes are not adjusting focus
Technically you can absolutely have something close to your face but focus your eyes far away. If you wear glasses you do that all the time. Just imagine that your glasses are like screens that reproject what's behind them.
You're not totally wrong because there are two components to focusing, one is rotating eyes according to how far is the object and another adjusting each eye's lens. AR/VR can cause them to mismatch https://en.wikipedia.org/wiki/Vergence%E2%80%93accommodation...
However the screen imitates focal plane a bit in the distance and THAT's where your eyes are focusing. There's still can be a mismatch because it's a fixed distance, but your eyes are NOT focusing like you strapped a phone to your head which is what you are implying.
(Actually I heard AVP dev guidelines recommend to avoid putting objects too far and too close to keep everything near focal plane probably to miminize the mismatch.)
I want to Ask HN relating to this: What can be the motivation behind this change? Is this the preferred way of using AI coding tools nowadays? I've been using Antigravity mainly because of its tab completions. So I can work in code like in a traditional way and AI assists me. But it was a broken experience and now they are moving away from IDE based tool. The alternative is you write the prompt and it does everything. Is this the standard SW development workflow in 2026?
This is how they want you to use AI-powered apps. The more ambiguity there is between you and the end result, the likelier you are to keep paying them to avoid friction.
The problem with AI products vs other rent-seeking is that AI is very expensive to build out and run… so they are desperate to push you into relying on it quickly.
Yes, this is the standard model for the big frontier models. You don't need Gemini or Claude to do tab completions. A modest size local model can do that just fine. If that is all you are using AI tools for you are wasting money subscribing to Google.
It is the new standard. It sounds awful until you try it, and then you can't go back. But you can still use an IDE as well to edit code by hand and review changes that agents have made.
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