I gave it a shitty harness and it almost 1 shotted laying out outlets in a room based on a shitty pdf. I think if I gave it better control it could do a huge portion of my coworkers jobs very soon
What I notice that I don't see talked about much is how "steerable" the output is.
I think this is a big reason 1 shots are used as examples.
Once you get past 1 shots, so much of the output is dependent on the context the previous prompts have created.
Instead of 1 shots , try something that requires 3 different prompts on a subject with uncertainty involved. Do 4 or 5 iterations and often you will get wildly different results.
It doesn't seem like we have a word for this. A "hallucination" is when we know what the output should be and it is just wrong. This is like the user steers the model towards an answer but there is a lot of uncertainty in what the right answer even would be.
To me this always comes back to the problem that the models are not grounded in reality.
Letting LLMs do electric work without grounding in reality would be insane. No pun intended.
I think they'll never be great at switchgear rooms but apartment outlet circuitry? Why not?
I have a very rigid workflow with what I want as outputs, so if I shape the inputs using an LLM it's promising. You don't need to automate everything; high level choices should be done by a human.
The main task of existing tools is rule-based checks and flagging errors for attention (like a compiler), because there is simply too much for a human to think about. The rules are based on physics and manufacturing constraints--precise known quantities--leading to output accuracy which can be verified up to 100%. The output is a known-functioning solution and/or simulation (unless the tool is flawed).
Most of these design tools include auto-design (chips)/auto-routing (PCBs) features, but they are notoriously poor due to being too heavily rule-based. Similar to the Photoshop "Content Aware Fill" feature (released 15 years ago!), where the algorithm tries to fill in a selection by guessing values based on the pixels surrounding it. It can work exceptionally well, until it doesn't, due to lacking correct context, at which point the work needs to be done manually (by someone knowledgeable).
"Hallucinogenic" or diffusion-based AI (LLM) algorithms do not readily learn or repeat procedures with high accuracy, but instead look at the problem holistically, much like a human; weights of neural nets almost light up with possible solutions. Any rules are loose, context-based, interconnected, often invisible, and all based on experience.
LLM tools as features on the design-side could be very promising, as existing rule-based algorithms could be integrated in the design-loop feedback to ground them in reality and reiterate the context. Combined with the precise rule-based checking and excellent quality training data, it provides a very promising path, and more so than tasks in most fields as the final output can still be rule-checked with existing algorithms.
In the near-future I expect basic designs can be created with minimal knowledge. EEs and electrical designer "experts" will only be needed to design and manufacture the tools, to verify designs, and to implement complex/critical projects.
In a sane world, this knowledge-barrier drop should encourage and grow the entire field, as worldwide costs for new systems and upgrades decreases. It has the potential to boost global standards of living. We shouldn't have to be worrying about losing jobs, nor weighing up extortionately priced tools vs. selling our data.
I gave it some custom methods it could call, including "get_available_families", "place family instance", "scan_geometry" (reads model walls into LLM by wall endpoint), and "get_view_scale".
The task is basically copy the building engineer's layout onto the architect model by placing my families. It requires reading the symbol list, and you give it a pdf that contains the room.
Notably, it even used a GFCI family when it noticed it was a bathroom (I had told it to check NEC code, implying outlet spacing).
for clarity now that I'm rereading: it understands vectors a lot better than areas. Encoding it like that seems to work better for me.
A good start would be getting image generators to understand instructions like “move the table three feet to the left.”
"Ok, I guess it could wipe out the economic demand for digital art, but it could never do all the autonomous tasks of a project manager"
"Ok, I guess it could automate most of that away but there will always be a need for a human engineer to steer it and deal with the nuances of code"
"Ok, well it could never automate blue collar work, how is it gonna wrench a pipe it doesn't have hands"
The goalposts will continue to move until we have no idea if the comments are real anymore.
Remember when the Turing test was a thing? No one seems to remember it was considered serious in 2020
> "the economic demand for digital art"
You twisted one "goalpost" into a tangential thing in your first "example", and it still wasn't true, so idk what you're going for. "Using a wrench vs preliminary layout draft" is even worse.
If one attempted to make a productive observation of the past few years of AI Discourse, it might be that "AI" capabilities are shaped in a very odd way that does not cleanly overlap/occupy the conceptual spaces we normally think of as demonstrations of "human intelligence". Like taking a 2-dimensional cross-section of the overlap of two twisty pool tubes and trying to prove a Point with it. Yet people continue to do so, because such myopic snapshots are a goldmine of contradictory venn diagrams, and if Discourse in general for the past decade has proven anything, it's that nuance is for losers.
To be clear, it's only ever been a pop science belief that the Turing test was proposed as a literal benchmark. E.g. Chomsky in 1995 wrote:
The question “Can machines think?” is not a question of fact but one of language, and Turing himself observed that the question is 'too meaningless to deserve discussion'."I believe that in about fifty years' time it will be possible, to programme computers, with a storage capacity of about 10^9, to make them play the imitation game so well that an average interrogator will not have more than 70 per cent chance of making the right identification after five minutes of questioning. The original question, "Can machines think?" I believe to be too meaningless to deserve discussion. Nevertheless I believe that at the end of the century the use of words and general educated opinion will have altered so much that one will be able to speak of machines thinking without expecting to be contradicted."
>If the meaning of the words "machine" and "think" are to be found by examining how they are commonly used it is difficult to escape the conclusion that the meaning and the answer to the question, "Can machines think?" is to be sought in a statistical survey such as a Gallup poll. But this is absurd.
This anticipates the very modern social media discussion where someone has nothing substantive to say on the topic but delights in showing off their preferred definition of a word.
For example someone shows up in a discussion of LLMs to say:
"Humans and machines both use tokens".
This would be true as long as you choose a sufficiently broad definition of "token" but tells us nothing substantive about either Humans or LLMs.
Also, none of the other things you mentioned have actually happened. Don’t really know why I bother responding to this stuff
i.e. the tell that it's not human is that it is too perfectly human.
However if we could transport people from 2012 to today to run the test on them, none would guess the LLM output was from a computer.
Also, the skill of the human opponents matters. There’s a difference between testing a chess bot against randomly selected college undergrads versus chess grandmasters.
Just like jailbreaks are not hard to find, figuring out exploits to get LLM’s to reveal themselves probably wouldn’t be that hard? But to even play the game at all, someone would need to train LLM’s that don’t immediately admit that they’re bots.
I strongly doubt this. If you gave it an appropriate system prompt with instructions and examples on how to speak in a certain way (something different from typical slop, like the way a teenager chats on discord or something), I'm quite sure it could fool the majority of people
Like if you put someone in an online chat and ask them to identify if the person they're talking to is a bot or not, you're telling me your average joe honestly can't tell?
A blog post or a random HN comment, sure, it can be hard to tell, but if you allow some back and forth.. i think we can still sniff out the AIs.
IOW, LLMs pass the Turing test.
I don't think it's fair to qualify this as blue collar work
Anything like this willl have trouble getting adopted since you'd need these to work with imperfect humans, which becomes way harder. You could bankroll a whole team of subcontractors (e.g. all trades) using that, but you would have one big liability.
The upper end of the complexity is similar to EDA in difficulty, imo. Complete with "use other layers for routing" problems.
I feel safer here than in programming. The senior guys won't be automated out any time soon, but I worry for Indian drafting firms without trade knowledge; the handholding I give them might go to an LLM soon.
For example, artists can create incredible art, and so can AI artists. But me, I just can't do it. Whatever art I have generated will never have the creative spark. It will always be slop.
The goalposts haven't moved at all. However, the narrative would rather not deal with that.