541–550 of 641 posts
So we're speed running the idea of AI Facebook friends and creating a new para(ai)social relationship
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At what point do we look at 'Industrial Society and its Future' and go from "yeah that'll never happen", "ok some parts of it are happening", to ...? I swear tech folks are the most obtuse people on the planet.
I think it's completely normal. Whenever automation comes knocking, people are inclined to think it's going to flatline conveniently before their job is at risk. LLMs can code now? Cool, they can't code well though can they? Oh they can code pretty well now? Cool, coding was never the hard part of SWE anyway, it's [thing we have no reason to think AI can't beat 99% of humans at at some point], etc I think SWE as a ma…
This makes software development AGI-complete. If you have an LLM that can write software for every domain, then for every task you assign it, it could build software that performs the assigned task and thereby solves every problem in existence.
What I'm trying to get at here is that an "SWE" is a biological machine building machine. If you have a digital machine that can build any machine, you haven't solved the first step, you've solved the final step in all of human history that ever needs to be done, whatever that means. Beyond that point, human work no longer exists, because the machines have taken over everything.
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> Prior to the industrial revolution, the natural world was nearly infinitely abundant. We simply weren't efficient enough to fully exploit it. This is just wildly incorrect. People started running out of trees during the early Iron Age. Woodlands have been a managed and often over exploited resource for a long time. Active agriculture vs passive woodlands vs animal grazing has been in constant tension for thousands…
The general point is accurate, don’t take it so literally. There were more than enough trees until we developed the technology to clear cut in expeditious manner. There were more than enough fish until we developed the technology to pull massive indiscriminate amounts out of the ocean (and/or started polluting our rivers with industry). There was more than enough topsoil until we developed mechanized plows and artifi…
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Agreed, totally! I still write and put stuff online. But it definitely feels different now. It used to feel like I was tending a public garden filled with other people who might enjoy it. It still kind of feels like that, but there are a handful of giant combine machines grinding their way around the garden harvesting stuff and making billionaires richer at the same time. It's not enough to dissuade me from contribut…
There were a couple of threads on HN this week. "Do you have any unusual hobbies" and "How do you relax". I enjoyed them and was thinking of contributing. Then it occurred to me that my comments would be gold for targeting advertising at me. That is the distrust that has been bred by the data harvesters.
I think it's too early to declare the Turing test passed. You just need to have a conversation long enough to exhaust the context window. Less than that, since response quality degrades long before you hit hard window limits. Even with compaction. Neuroplasticity is hard to simulate in a few hundred thousand tokens.
For as rigorous of a Turing test as you present, I believe many (or even most) humans would also fail it. How many humans seriously have the attention span to have a million "token" conversation with someone else and get every detail perfect without misremembering a single thing?
Like failing to pick out all the motorcycles in a captcha, or a turing test where you have a guy chat with two people without knowing that one of them could be a computer, and the interrogator, unprompted, suggesting one of them might be a computer?
> 2017’s Attention is All You Need was groundbreaking and paved the way for ChatGPT et al. Since then ML researchers have been trying to come up with new architectures, and companies have thrown gazillions of dollars at smart people to play around and see if they can make a better kind of model. However, these more sophisticated architectures don’t seem to perform as well as Throwing More Parameters At The Problem. P…
Literally the paragraph right before the one you quote is this: > I am generally outside the ML field, but I do talk with people in the field. One of the things they tell me is that we don’t really know why transformer models have been so successful, or how to make them better. This is my summary of discussions-over-drinks; take it with many grains of salt. I am certain that People in The Comments will drop a gazilli…
The most obvious reason is that transformers accept a sequence as an input and produce a sequence as an output. The vast majority of pre-transformer architectures only accepted a fixed input and output size. Before 2016 I was somewhat interested in ML, but my curiosity vanished because of the fixed input and output size limitations.
RNNs including LSTMs at the time were pretty bad and difficult to train due to vanishing and exploding gradients at long sequence lengths and sequential training along the sequence length. Meanwhile transformers can be parallelized along the sequence length.
Then there are theoretical limitations. Transformers re-read the entire sequence for every output. This leads to quadratic attention. There are plenty of papers that tell you why it is impossible to replicate the properties of quadratic attention with linear attention.
The reason is blatantly obvious. If you want linear attention to have the same capability, you need to re-read the entire input sequence after every output. If you do this at the token level, then you have basically implemented quadratic attention.
Transformers aren't a mystery success, they are using computational brute force, which is hard to beat with other architectures. If you go with a more efficient architecture, you are by definition giving up some non-zero capabilities. Nobody really cares about getting slightly worse results from a much more efficient architecture. In the current ML space, it's SOTA (state of the art) or go home.
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It's sort of the exception that proves the rule. This is where STEM people are weak- a lack of knowledge on history. In another forum, someone would have chipped in that England's virgin forests were fully deforested by 1150. And someone else would have pointed out that this deforestation produced the economic demand for coal that drove the Industrial Revolution in the first place. Still, that kind of underscores OP'…
Yeah - really struggling to understand why people are not grasping this point. Yes, Easter Island was deforested far earlier - but you wouldn't compare the steam engine's capability in resource extraction compared to what people on Easter Island were doing. It feels like people are almost straining to not understand the point - I think it's quite clear how ML + AI serve to extract resources of data at a unheard of sc…
I intentionally pointed out the STEM-esque responses of pedantic correction as a symptom of a disciplinary blind spot: technically correct nitpicking that misses the forest for the trees, a tendency to atomize arguments and lose the structural point, and that tendency is a weakness, not a strength.
There's also a lack of historical training to contextualize their own objection. That's also why I brought up Devereaux as an authority hammer: the actual domain experts consider those objections and dismiss it.
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> LLMs are clearly unable to propose new, creative solutions for problems it has never seen before. LLMs are incredibly useful but I'm not sure about this statement. It is proposing stuff that I haven't seen before, but I don't know about it is new or creative from the entirety of collective human knowledge.
I'm not sure if you misread the statement you quoted or I'm misreading yours, but it doesn't sound like you're really disagreeing with their point. Did you miss the "un" in "unable", or am I misunderstanding you as also saying that you don't consider them to be creative?
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In many examples, LLMs betray the fact that they are not reasoning, because when provided with problems that can be solved with the ability to reason, they fail. Even in this discussion someone provided an example of coming up with board game rules. LLMs found all board game rules valid, because they looked and sounded like board game rules. Even when they were not. In short, You can learn a subject, you can make a m…
I don’t believe the board game rules example. I think this would be a piece of cake for an llm. I’m happy to be proven wrong here if you share an example.
Thank you for putting it so succinctly. I keep explaining to my peers, friends and family that what actually is happening inside an LLM has nothing to do with conscience or agency and that the term AI is just completely overloaded right now.
> what actually is happening inside an LLM has nothing to do with conscience or agency What makes you think natural brains are doing something so different from LLMs?
Transformers have a thing called a context window which doesn't really have a biological equivalent, since the brain has a fixed size and doesn't grow or shrink in response to the amount of information being processed.
LLMs consist of several layers that communicate at fixed points between the layers, whereas neurons can form feedback loops and communicate with any neighbour in any direction.
Humans do not consume or produce tokenized information. The brain controls the human body which is a biomechanical system. Spoken or written language is the result of controlling muscles via an internal model of the biomechanical system, not something that was designed via a software tokenizer that compresses character sequences.
The equivocation just doesn't seem appropriate. Try again in 2050.