He's so good at comedy and it's under praised.

He was great on SNL too.

That's optimistic.

The way I see it they have two options:

1) throttle back on AI search and hope they don't lose share to OpenAI

2) enshitify AI search to get more money from it.

2 seems way more likely

Yes, but not for you and me. Graphics designers have been using stylus-displays for years and years, because it's great to draw on the thing you are making instead of drawing on a different surface like a Wacom.

But I'm like you, I don't have a use case for a touchscreen and if you touch my notebook screen I'll be angry because I prefer it without fingerprints, thanks. So my first question is: Do they offer this thing with OLED but without touch? I guess not. Pity.

I've been slowly moving towards my dream setup: Solar panels, battery, EV car, island mode capability. This is one thing that'd be a factor in my buying decision. Using your car as a battery or secondary battery is a big plus, and if they built the necessary equipment to run independent of the power grid, it'd come in a neat package.

While power failures are rare where I live, I expect them to become more common with the move to renewable energies. I support that, but it's more fragile than constantly running power stations. So being able to run your home at least for a few hours on your own energy is something that I consider a good idea.

You missed the point.

A friend of mine made a tea-timer app for the iPhone. Where you enter what tea you are making and it sets the correct number of minutes. Today, people would ask ChatGPT before they go on the App Store to search for something like that. That's the point. Not what YOU would do, but what the general public does. And for Jane Doe, ChatGPT has replaced a lot of these specialty apps.

Skills are great until they aren't. The problem is exactly what you say: You can literally create a skill by telling the LLM to create a skill. Which is... why the f do I need to make a skill if the AI already knows how to do it?

Properly created skills, that include reference material, specific instructions, and expert knowledge the LLM doesn't have or that's buried somewhere under 10 layers of reddit posts is genuinely useful. But most skills aren't. I'm constantly underwhelmed by hyped-up skills that, when I look at them, turn out to be bland, basic instructions.

Uncopyrightable just means public domain. Using public domain code doesn't make your whole work public domain.

But that's exactly my point: if all you want or need is competence, then according to the professor's own admission, an AI will do that. No need for your job, no need for grants, no need to teach. What is the purpose of teaching future generations if an AI will be better at mathematics than you ever can be?

And what I am saying is that either (1) the framing of AI as being the hyper-competent replacement for everything intellectual that we do is fundamentally flawed, and/or (2) it is necessary to understand, for the well-being of our own species, that "competence" and "results" are not the end-all and be-all of a task.

If we fail to appreciate the importance of doing and learning things for their own sake, and to find satisfaction in that process, then we will cede the task of thinking to machines. We can already see early signs of this. And the consequences of this short sightedness is catastrophic.

But again that's backwards. I let my AI coding assistant write tests exactly so that I have a reproducible, deterministic ground truth that I can inspect for logic errors. If you let the AI do the testing directly, it's a toss-up if today it feels like it or it decides that your desired result is "everything green" so it'll just give you that answer without actually testing anything.

LLMs obviously aren't human. But that's not the point. The point is that you don't need to know a programming language to program a computer.

For example: Especially in the early days of smartphones, there were mountains of small tools that did something that's essentially a simple script. Egg timers, remind-me-tomorrow apps, tons of stuff that's basically a wrapper around a web search, and so on. Almost all of those are today something ChatGPT can do, and are easy enough to describe.

I've actually outsourced the first iteration of that to AI. :-)

At the end of coding sessions, I generall ask it to spin up an adversial agent whose task it is to find flaws, inconsistencies, mistakes, bugs, etc.

I'd still strongly recommend to anyone shipping AI-generated code to have a thorough human code review, but for a first pass at surface level, the AI can check itself if it has some kind of separation between the generator and the reviewer.

Personally, after some experimenting, I've concluded that:

• AI is good at implementing known algorithms.
• AI is good in creating code that flows straightforward from a design document.
• AI is good at code reviews and finding bugs.
• AI (unlike me) doesn't mind writing a few hundred tests.
• But, it tends to over-engineering simple tasks to the point of "why does Hello World need enterprise-software levels of abstraction?"
• It struggles with code that doesn't have a known solution.
• It's difficult to get it into a different lane once it started going down the wrong way.

What I use it for these days is rapid prototyping. Using AI I can make a functioning prototype of an idea I have in a few days instead of a few weeks. I can quickly test out an idea for a feature or change in my existing codebase.

What I also have been using it for a while is as a code-reviewer and for someone like me who is mostly working solo, that's an insanely valuable thing. Not just the bugs themselves, but also the ability to discuss code with someone. Basically, AI is a rubber ducky that replies.

Mickeysoft lost the plot again.

The game-changing element of LLMs and agents is that they have a non-computer-language interface - you can talk to them in plain English (or Chinese or whatever). This makes complex computer interactions available to a much wider audience than any previous computing invention. Adapting humans to the AI is getting that backwards. It's a sign you don't understand the first thing about why AI is so widely successful.

I predict this will be an abject failure and a year from now will quietly be pushed to the very back of the bottom-most drawer, never to be spoken off again (though it is getting crowded in there for M$).

Meanwhile, in the real world, everyone I've talked to about these things already has a skills file to teach the AI about their personal coding style. This is what actual humans came up with, independently several times, and it works: Adapt the AI to you. Plus we've been writing coding style guides for humans for a long time now, we've got plenty of experience doing that at least somewhat right.

I wonder if it's 50% larger or 12kb flat overhead.

The data from a single component 2 orders of magnitude smaller than typical isn't so useful.

Let's be clear about the nature of education. Before a student reaches a particular level of self-discipline during their formative educatoinal years, homework is assigned because it is an effective mechanism to have them practice the concepts being taught to them, and to do this outside of the structured classroom environment. This activity promotes the crystallization of learning objectives and prepares the student for the next lesson, which builds upon the previous.

After a student becomes largely self-sufficient, the homework model becomes less about enforcement through grading, and becomes more about preparing the student to manage their own academic development outside of the lectures and office hours. The student goes to class because they understand they NEED to be there to learn, unlike a child who must be TOLD to be there.

In this regard, whether or not homework is graded, and whether or not a student feels a need to cheat or take shortcuts with homework, is really just a reflection of the student's maturity and sense of responsibility for their own intellectual development. Typically, a professor should not need to police what a student does on their own time; what matters is the ability to certify that a student has met the learning objectives--and that is done through examination.

So, what I find disturbing is not the question of whether this professor should grade his students' homework, or whether the students are using AI to do their homework for them instead of actually learning the material. As the old adage goes, cheaters are only cheating themselves--at least, that's what I would still like to believe.

No, what I actually find most disturbing is this apparent disconnect in the professor's views on the future of AI as they pertain to professional mathematics, and the impact of AI use on the education of humanity. He predicts that AI will be better than professional human mathematicians, and I think this belies a deeply mistaken perspective on the purpose of learning and doing mathematics, one that I am extremely surprised that a working professional mathematician would make.

Even if AI models far exceed the human capacity to prove theorems, will it ever FEEL enjoyment and excitement about doing mathematics? Because if it can't stir something in you, then why do it, and why bother teaching it to others? Why would you ever need to teach another student, if you could just always ask a computer instead? In fact, why will anyone in the future ever need to learn anything, if AI will be the replacement for our brains?

I am a human being and I feel. I dream, and I love. I love mathematics, and it is not simply the result or the answer that I love, but the entire process. The struggle, the failure, the questioning and working away at it until at last, there is UNDERSTANDING. And the flaw that I see my fellow humans are making, over and over, is this crazy focus on RESULTS. ANSWERS. GOALS. They seem to have forgotten how to enjoy the process of doing things, whether it is the restoration of a classic car, or composing a musical score, or deciding what to cook for dinner.

I mean, why spend months and months on taking apart a 1957 Buick engine, lovingly cleaning every component, repairing and sometimes manufacturing replacements, and reassembling it? Why, when you can have a new electric car that is far safer and more efficient, with more functions and conveniences? Let me be clear; I don't work on classic cars or have any experience with automotive mechanics. But even from my outsider's perspective, I instantly know, at a visceral level, why they do it. It is for the love of the process; to take this machine and build it with your bare hands so that you have a complete understanding of it; and that this machine is an embodiment of engineering, aesthetics, and a symbolism of the freedom of movement.

That's what AI will not, and will never, do. It can't fall in love with the process, because to do so is what it means to love learning, and is part of what it means to be human. And for a professor of mathematics to not appreciate this is, to me, very misguided, and does not bode well for the education of future generations.