Employers Say Students Need AI Skills. What If Students Don't Want Them?

Ok. So why generate it at all? Just have the user generate it when needed or automate as part of the build. Seems like option 3, the lazy option, might be the right one.

Now, they are in the customer facing documentation part of the business. Think manuals for all the myriad products we make, in all the many languages, and all the many regulatory systems. The job is exacting, precise, and must make sense to readers at the end of the day. Many manuals go through countless revisions and take months to release under all the requirements. Large parts of the job are pretty boring and automatable though.

So, they've been aggressively perusing AI tools for large parts of the boilerplate work and the formatting work. It turns out, that most of the hard parts of the job, the xml formatting and translation, are very easy to automate with AI. As in, what usually took them months of manual editing to preform now takes them seconds, with a day or two of spot checking the language. Regulatory requirements review still take just as long.

Here is where all the nerds here on HN can't believe that you're not using LaTeX or something.'How could it have taken them months?!'

They're all English majors above 50 years old. They have never coded anything in their lives. All this work was previously being done in MS Word. They at least had some sort of central file repo beforehand, but now they all use git and managed to stand up their own servers. I'm sure all of this is being done poorly, but it's being done in the first place. The alternative was much worse.

It's not just that the AI is managing to help them make xml skeletons in the blink of an eye, it's that AI told them and taught them about xml at all. It taught them about git too, and I'm sure a lot of other things they never knew about. AI is a really patient teacher.

So, the thing is that there is a lot of internal pushback against AI. From what I was told, there is about half the team that refuses to touch AI. Thinks it's the devil, that they can always do it better, do it faster, etc.

So far, the company line is that they are right and that you can elect to use AI or not. But that boss I was talking to said that the internal vibe of the higherups is that in about 2 years, you're going to be forced to use it or let go. Their internal numbers (no idea what those are, this is all third-hand info now, believe me if you want) are pointing out that using AI has just so much better results that it's going to be a job requirement going forward.

So, as one piece of anecdata, doing at least a few jobs is going to be, very soon, chatting with a bot too.

(I'm not entirely serious, but also not entirely joking. Looking for these kinds of red flags is probably more within the ability of contemporary systems than reliably writing prose.)

The point is to build intuition, those take time and unless the student is particularly mathematically inclined, you can't rush it. That's why you have to start in high school. Right now, the focus of pre calc is to induce them into calculus through slopes, limits, and derivatives. But a good chunk of high level statistics don't need that to build intuition. For example, we generally teach students y=mx+b in middle school and high school. This is considered a perfectly normal part of math education. It's accepted that the students may not understand the full meaning of linearity and regression at first, but at higher levels the formula would be second nature to them. This is the point I am trying to make about statistical learning (not specifically Bayesian theory in particular, but the key concepts around it). Generative ML and stuff like LLMs require a specific frame of mine and view of the world, and it's best to develop it early. The way we are teaching statistical learning theory, at the lower levels, is essentially dumping a massive bag of tricks on them and hoping for the best. We need to change that approach and start treating it like pre-calc and algebra, something to be taught level by level, with building intuition as a core focus. It's not easy to communicate the idea that "the next word prediction of the LLM (depending on the model) is generally the joint probability of all other words that come before it by sampling from a multidimensional probability distribution" to most 14-16 year olds.