Fwiw the teaching of the church is that (today's) AI isn't at a human level because human intelligence is something that we experience whereas artificial intelligence seems to be merely a sophistication in performing tasks.
I think "stopping" is great for software that people want to be stable (like `ls`) but lots of software (web frameworks, SaaS) people start using specifically because they want a stream of updates and they want their software to get better over time.
The bar for adding new options, especially short options, is quite high for coreutils. We have a (likely outdated) page of rejected requests [1]. Some of the changes people have strong feelings about...
Not sure DORA is that much of an indictment. For "Change Failure Rate" for instance these are subject to tradeoffs. Organizations likely have a tolerance level for Change Failure Rate. If changes are failing too often they slow down and invest. If changes aren't failing that much they speed up -- and so saying "change failure rate hasn't decreased, obviously AI must not be working" is a little silly.
"Change Lead Time" I would expect to have sped up although I can tell stories for why AI-assisted coding would have an indeterminate effect here too. Right now at a lot of orgs, the bottle neck is the review process because AI is so good at producing complete draft PRs quickly. Because reviews are scarce (not just reviews but also manual testing passes are scarce) this creates an incentive ironically to group changes into larger batches. So the definition of what a "change" is has grown too.
Re: model Supremacy -- I think there's so much cross-pollination between the AI labs that this is unlikely. Everybody at these labs basically know what everybody at the other labs are doing. *data* can be hard to get but I don't think acquiring good data is positive feedback loop-y where whoever wins just skyrockets away from everybody else.
Model stagnation, I thought stagnation was coming last year, then Opus 4.5 came out. I think maybe the models are slowing at getting *smarter* per se, but they are still getting better at coding. And even if they stopped getting better at coding, if they got as good as they have gotten at coding on other fields of inquiry (like, say, writing) so I think we've got a ways to go yet before the progress in terms of economic usefulness slows down.
I don't think 10 minutes of AI experience is enough to be a master? AI-assisted software engineering is a skill that I want to get better at, just like traditional software engineering is.
Why do you think that the human mind can contain semantics but a machine cannot? This argument needs some sort of dualism, or what Turing called "the objection from continuity" to account for this.
FWIW I don't think that the "triangularity" in my head is the true mathematical concept of "triangularity". When my son e.g. learned about triangles, at first the concept was just a particular triangle in his set of toy shapes. Then eventually I pointed at more things and said "triangle" and now his concept of triangle is larger and includes multiple things he has seen and sentences that people have said about triangles. I don't see any difficulty with semantics being "a matter of image", really.
Why do we believe that semantics can exist in the human mind but cannot exist in the internals of a machine?
Really "semantics"
I had come across this Catholic philosopher: https://edwardfeser.blogspot.com/2019/03/artificial-intellig... who seems to make a similar argument to this; i.e. that it's the humans who give meaning to things, "logical symbols on a piece of paper are just a bunch of meaningless ink marks"
> Why do you think that the human mind can contain semantics but a machine cannot? [...] Why do we believe that semantics can exist in the human mind but cannot exist in the internals of a machine?
Because I know human minds have semantic content (it would be incoherent to deny it, as the denial itself involves concepts), and because I know the definition of what a computer is, which is that it is a purely syntactic formalism. Anyone who knows the history of computer science will know that computation was intentionally defined as a purely syntactic process. And because it is syntactic, we can mechanize it using physical processes. And no amount of syntax ever amounts to semantics, just as so matter how many natural numbers you add, you'll never get a pineapple or even the number pi. How could it?
Whether this entails dualism or not depends on what you mean by "dualism". It does not entail Cartesian dualism, though a Cartesian dualist can accept this view as presented.
> seems to make a similar argument to this; i.e. that it's the humans who give meaning to things, "logical symbols on a piece of paper are just a bunch of meaningless ink marks"
We don't give meanings to things per se. The meaningless ink marks on a piece of paper mean just that: ink marks on a piece of paper. Those are still meanings. However, writing involves the instrumentalization of physical things to make conventional signs, and signs are things that stand in for something else. So, yes, we can make ink marks with which we associate certain meanings and agree to a convention so that we can communicate.
> FWIW I don't think that the "triangularity" in my head is the true mathematical concept of "triangularity".
What is the "true mathematical concept"?
Concepts can be vague (though triangularity per se is so crisp and simple that I reject the idea that you don't have a clear idea of "triangularity" as such), and we usually do not explicitly grasp all that's entailed by them. For example, people knew what triangles were before they learned that the sum of their angles is always 180 degrees. The latter falls out of an analysis of the concept. And this law applies to all triangles because it necessarily falls out of the concept of triangularity, not because we've empirically shown that all triangles seem to have this property, approximately.
> I don't see any difficulty with semantics being "a matter of image", really.
Your son, as he was learning, was abstracting from these individual examples. He realized that you don't mean this triangle, or that triangle, but something both have in common, and ultimately, that is triangularity, which is not just a property or feature of a given triangle, like "green" as in "green triangle", but the what of a triangle. But if you reduce concepts to images, you end up with problems and paradoxes. For example, why should a collection of these things, to the exclusion of those things, be triangles? Or the number three: you have never encountered the number three. Or the notion of similarity between images. There are well known issues with an imagist notion of the mind.
> What Turing was trying to do, is to isolate this "hard problem of consciousness" and separate it from easier problems we can actually answer.
Yes exactly. As a computer scientist this is a great thing to do, science is all about taking mushy concepts like "intelligence" and extracting simplified versions of them that are more tractable in technical settings. The trouble is, Turing doesn't seem to want to stop at merely arguing that forgetting about interior consciousness is useful for technical discussions -- he seems to think that interior consciousness shouldn't be important for philosophical or popular notions of thinking and intelligence, either, and that they should update to use something like his test.
So even if you updated the Turing Test for 2025 the church would probably still be writing "Antiqua et Nova" to remind people that -- yes, interior consciousness exists and is important and robot intelligence really isn't the same as human intelligence without it.
