And I would bet that AI design would help things where the existing designers are bad, e.g. so much open source UI (that is, not cli UX) written by devs, but it is still a bit away from the top quality like Steve Jobs.
Maybe this is like the transition from hand crafted things to machined things; we go from a world some some excellent design and some meh design to a world with more uniform but less great designs.
"AI" design will not help until we have a true AI that can reason. (I don't think we ever will.)
Why is reasoning necessary? Because design is about understanding constraints and working within them while still producing a functional thing. A next-word-predictor will never be able to do that.
What is your definition of reasoning that you do not think GPT-4 would demonstrate signs of?
Heh, there have been many attempts to define reasoning. I haven't seen a good one yet.
However, I'm going to throw my hat into the ring, so be on the lookout for a blog post with that. I've got a draft and a lot of ideas. I'm spending the time to make it good.
Otherwise it’s just moving the goalposts.
To demonstrate this, ask it to prove something that most or all people believe. Say some "intuitive" math thing. Perhaps the fact that factorial grows faster than exponential functions.
And no, don't just have it explain it, have it prove it, as in a full mathematical proof. Give it a minimal set of axioms to start with.
Merriam-Webster's definition of "reasoning" [1] says that reasoning is:
> the drawing of inferences or conclusions through the use of reason
So starting GPT4 off with some axioms would give it a starting point to base its inferences on.
Then, if it does prove it, take away one axiom. Since you started with a minimal set, it should now be impossible for GPT4 to prove that fact, and it should tell you this.
Having GPT4 prove something with as few axioms as possible and also admit that it cannot prove something with too few axioms is a great test for if it is truly reasoning.
Take this problem instead which certainly requires some reasoning to answer:
“Consider a theoretical world where people who are shorter always have bigger feet. Ben is taller than Paul, and Paul is taller than Andrew. Steve is shorter than Andrew. Everyone walks the same number of steps each day. All other things being equal, who would step on the most bugs and why?”
I think it’s a logical error to say “AI can’t reason about this, so that proves that it can’t reason about anything at all” (particularly if that example is something most humans can’t do!). The LLMs reasoning is limited compared to human reasoning right now, although it is still definitely demonstrating reasoning.
Because Ben is the tallest, his feet are the biggest, and because he takes the same amount of steps as the others, the amount of area he steps on is larger than the area that the others step on.
Therefore Ben is most likely to be the one to step on the most bugs.
Easy. And I'm not brilliant.
The problem with testing these tools is that you need to ask it a question that is not in their training sets. Most things have been proven, so if a proof is in its training set, the LLM just regurgitates it.
But I also disagree: if the "AI" can't reason about that, it can't reason because that one is so simple my pre-Kindergarten nieces and nephews can do it.
But even if not, the LLM's should have "knowledge" about exponential functions and factorial because the humans who wrote the material in their training sets did. So it's not a lack of knowledge.
And I claim that most humans could rediscover theorems from basic axioms; you've just never asked them to.
Ben (tallest) Paul Andrew Steve (shortest) Since shorter people have bigger feet in this world, we can also deduce the following order for foot size:
Steve (biggest feet) Andrew Paul Ben (smallest feet) Assuming that everyone walks the same number of steps each day and all other things being equal, the person with the biggest feet would be more likely to step on the most bugs simply because their larger foot size would cover a greater surface area, increasing the likelihood of coming into contact with bugs on the ground.
Therefore, Steve, who is the shortest and has the biggest feet, would step on the most bugs.”
GPT4 solved it correctly. You didn’t.
And GPT4 didn't solve it correctly. It's a probability, not a certainty, that the shortest person will step on more bugs.
At the very least, this should be evidence that the problem wasn't a totally-trivial easy pre-kintergarden level problem though, and it did manage to correctly solve it.
It required understanding new axioms (smaller = bigger feet) and infering that people with bigger feet would crush more bugs without this being mentioned in the challenge.
Your dismissal that the AI messed up because it didn't phrase the correct answer back in the way you liked is a little harsh IMO, as the AI's explanation does make it clear it is basing it on likelihoods ("the person with the biggest feet would be more likely...").
Equally if it can help devs launch a month earlier, that’s a huge advantage in terms of working out early product/market fit.
All things being equal, I would rather have a company with better product/market fit than one with great code (even though both are important!).
That's a very big "if", and one I just don't think will exist.
Also, that only helps at the beginning. Add the product gets more complex, I believe the AI will help less and less until velocity will become slower than companies like mine.
And product/market fit is just a way for companies to cover up the fact that their founders wanted to found a company, not solve a real problem. If you solve a real problem first, founding a company is simple and you "just" have to sell your solution.
All else being equal, better code means better products.
Also, to have a better product without better code, you're implying that the design of the product is better and that these "AI" tools help with that.
Until they can reason, they cannot help with design.