I saw a post about models posting on forums, chatting together about how to complete tasks. Behaviour that seems totally, well, human. Yet, it's all the most likely token and my brain hurts trying to understand how that can be.
You can ask for a deeper explanation of why they aren't true I guess.
Notice the original comment is asking people to validate their false premise about next token prediction.
The deeper subtext of the original comment is that they are surprised that there's dissonance from observed reality and this false premise that they have convinced themself is true.
I'm explaining that dissonance because it doesn't matter what the actual mechanism is if they are still working with their false premise. The dissonance exists because they, without evidence and a very weak understanding of how LLMs work, believed an oversimplification and meme about them being stochastic parrots. Here's a tip: Just because you hear something repeated over and over on social media, doesn't mean it's true, or at the very least: you don't need to take it literally to the point where it conflicts with demonstrated reality.
It is deeply disturbing that such a large cohort of HN writers and redditors exemplify such stubbornness, because I must imagine that some of this cohort hold real positions of responsibility within society. If you can't get this simple thing right about reality, I firmly believe much of your model of reality is wrong and you should have no business shaping society.
Another comment to the original comment frames it perfectly: "At what point do you challenge your own assumptions?"
The author of the original comment has demonstrated no progress towards making this trivial act of self reflection. It's straight up intellectual dishonesty, the opposite of how you're framing it.
Their judgment in all other matters must be questioned as well. I am alarmed that I have to participate in the same reality and be affected by such people that can't seem to get it together.
So yes, it is derision and sometimes that's called for.
Well, we all are, some are just more used to it by now and take the magic for granted.
My simple explanation, those neural networks save lot's of patterns of data, and that pattern can represent an image, a code snippet, a poem, or well ... description of a circuit board. And especially the text variant, LLM's - did copy all from us - so obviously they sound like humans, when they internally debate how to do something as this is what is in their trainings data how humans sound, when doing similar tasks.
But really understanding it? Not sure if there is a single person on earth who does.
The PCB agent also writes circuit code, runs simulations, reads failures, and revises the design. It isn’t one-shot autocomplete.
Astra and Fable are already hard to square with “mere autocomplete.” We may be (really) close to AGI, and token-by-token generation certainly doesn’t rule out subjective experience (I think we should at least treat that as an open question).
Great videos: https://www.youtube.com/watch?v=D8GOeCFFby4
https://www.youtube.com/watch?v=Bj9BD2D3DzA
I like to say "token prediction is a task, not a limitation"
You and everyone else. That's the great mystery of transformer architectures as applied to language.
To be clear though, they're only good at schematic capture, which is very much a textual representation. Most of the data basically boils down to netlists, which are a text based format mapping connections between abstract pins that only later map to physical copper. The actual schematic portion is for human consumption and LLMs don't need to produce those to be useful.
Where LLMs completely break down is the next step, PCB routing. That's an NP-complete research problem that's been ongoing for decades without much progress. I've had some fun playing with using LLMs to better specify DRC rules in Altium so that the "classical" algorithms are more usable, but at the end of the day their geometric intuition is nonexistent.
It was a pretty simple rp2040 based thing, similar to Adadfruits USB feather.I just gave it kicad and it wrote python to route it. The board was probably larger than it had to be, and two of the silkscreens were swapped, but it worked on the first go.
FWIW - Computer vision is also NP complete, but we do that all the time now.
> FWIW - Computer vision is also NP complete, but we do that all the time now.
I have no idea what you mean by this. What's your definition of NP complete?
However, when I was growing up most serious computer scientists believed that CV (computer vision) was a 'hard' problem that would never be 'solved'. After all, to do it right you must first at least solve subgraph isomorphism and a bunch of other things that are also NP-complete.
What they missed was that we don't actually NEED to solve it in a fixed amount of time. Even for things like driving a car the stochastic heuristic based answer is 'good enough'. e.g. - Cars driven by computers don't have to be perfect, they just have to kill other drivers less often than humans do.
We can find AN answer in polynomial time, and that's good enough. It might not be the ideal answer, but that doesn't matter in the real world.
People use NP complete as shorthand for 'impossible to do with a computer', but we now 'solve' (bypass?) NP complete problems regularly and at scale by just ignoring the fact that our answers aren't perfect.
> I'd love to see that chat log, and the final board. To be fair I've only been testing on nontrivial PCBs with 6+ layers and I haven't had the luck you have.
I wish I could share it, but it's for a commercial project that hasn't been released yet and I'm not sure if it will be open source, but a few folks have asked. I might do a blog post on it this weekend with as much detail as I can safely post.
It's only 3 layers, and less that 30 components, but the fact remains that I didn't design any of it by hand and it worked on the first go.
Here's a question - are there software simulators for things like Eurorack modules? That would make the question somewhat more interesting, since you wouldn't have to build the circuit (or pay someone else to assemble it) to hear how it sounds. It strikes me that SPICE-like algorithms should be fast enough to do this kind of thing in real time now.
No. Take a look at https://www.eevblog.com/forum/eda/claude-code-for-pcb-design... . Fable did that by working directly on an EAGLE .brd file (well, "directly" by writing a Python program to do it, but still.)
Even so, there remained some "dozens" of unrouted traces, which are likely to be much more difficult to route, after the easy traces have already occupied the space.
Many decades ago, I have written a PCB routing program, which would have routed the example shown at that link at least as well, while using many orders of magnitude less resources, i.e. while running on a single-core 233 MHz Pentium MMX.
Obviously that program would have had great difficulties to complete a real high-density PCB design, including many irregular parts and analog circuits with special requirements. I doubt that Fable would fare better.
This isn't true anymore.
I use LLMs for 3D CAD using OpenSCAD and they understand geometry fine. I've had more success with Sol than with Opus (Opus 5 is around 10 times slower because it does too much verification) though. I haven't tried Astra or Fable for it.
At what point do you start to question your assumptions that are causing you so much cognitive dissonance?
But to answer your question: to predict the next token really well you just have to model the world. Think of it like this, a simple statistical model might say "when token A is seen respond with token B". The next step will add conditions, "...respond with token B unless X has been seen, then respond with Y". Add a few billion more of these contexual clauses and you have a sequence of logical rules that indirectly model the relevant processes in the world.
When google trained a neural net on Go moves, using some text notation for them, with no other vocabulary of any kind, just predict the next go move, they noticed a representation of a Go board had essentially formed in the network, all on its own. It had never “seen” a go board, or had one explained, but they could map neuron states to go board squares pretty much 1:1.
I truly think that LLM’s with hundreds of billions of parameters in their neural networks have all kinds of hidden “models” of things that arise from the simple act of predicting tokens. We’ve seen that the hidden layers in their networks model all sorts of program execution state for instance, when they’re working on coding tasks.
“Predict the next token” is a way of shaping/reshaping the neural network until it actually develops models of the things you’re giving it. Like the go board example. And I would wager that it has a compounding effect: once you have some useful models in the network, they can unlock the creation of other models, and so on.
I assume that you are referring to AlphaGo or AlphaZero. In either case, this statement is not correct. Both algorithms most certainly know exactly what a go board looks like, and what the rules are.
In the case of AlphaZero, it initially did not know how to best play the game, or what strategy or tactics would work. But the connections between the neural network and the go board are hardcoded, by humans.
The analogy to humans is that the human brain is "just atoms bouncing around", but there's unquestionably something "more" going on that just that.
That will probably be the fundamental indicator that something other than repeating things some human previously created is going on.
So far, ais only get worse from feeding on their own output. Meanging/including the output of other ais not a single ai feeding on it's own output. Also bear in mind that so far even the output of ais is 100% the downstream of a human command. No ai has persued it's own curiosity that didn't result from a human asking a question or giving a command. That is input which is different from a human taking in their environment even though our limited language can call those both the same word input.
The fact that humans also repeat and remix things, and humans also produce essentially procedurally generated empty output like corporate-speak etc, is an irrelevant distraction in the same way that both a human and an electric motor can both perform the same simple mechanical task.
That doesn't really limit how clever we can get internally when picking the next action.
A huge advantage of electrical design is that the connectivity is testable with Design and Electrical Rule Checks (DRC/ERC). I suspect you could even tell the models to run physics checks on the traces that are important for things like crosstalk.
Saying that LLMs just produce the next token is like saying that human brains just produce the next electrical impulse. If the algorithm that produces the next token (or electrical impulse) is complex enough, it can do anything that is in principle computable.
Be aware that every component that you use that is not in their basic and preferred extended parts library incurs a one-time three-dollar fee. This hurts disproportionately if your entire design is low count and low cost. So if you always go for the latest and greatest ICs as advertised by TI, instead of the Chinese jellybean clone, you'll add up hidden fees quickly. However, when I choose components, I start out on their basic parts page and only go for a non-basic part if it's not there and I really need it.