Fair. An interesting question: how quickly can we detect something without being thwarted by anisotropy / the multiplicity of backward paths? ie- retrodiction
Let’s organize the temporal order a bit. This is what some research turned up.
“Groups of senior employees, concerned with Altman’s leadership and lack of transparency, asked Loopt’s board on two occasions to fire him as C.E.O., according to Hagey.”
“As Mark Jacobstein, an older Loopt employee who was asked by investors to act as Altman’s “babysitter,” later told Keach Hagey, for “The Optimist,” a biography of Altman, “There’s a blurring between ‘I think I can maybe accomplish this thing’ and ‘I have already accomplished this thing’ that in its most toxic form leads to Theranos,” Elizabeth Holmes’s fraudulent startup.”
This is super, but students will have access to AI during the test in real life, so it's ironically less realistic to remove it (thinking of the "... GPT-4 actually harmed subsequent performance by 17% when the tool was removed ..." part).
I'm more curious how students perform on the test with vs. without AI.
True, during Covid when blue collar in-person maga unemployment skyrocketed, the Fed could have avoided increasing the money supply. I wish they had, bc we all had to pay the resulting inflation cost. How were thanked for it? Tons of Constitutional law-breaking.
There’ll always be boundary tending, true. Only a portion of CS deals with stochastic functions though, whereas all of statistics is stochastic. That makes a big difference, bc the world is complex.
Information theory doesn’t even incorporate utility.
Programming is a lot easier than statistics bc it’s deterministic, whereas statistics is stochastic (that extends and encompasses deterministic functions).
AI speeds up learning, so I bet that’s what you’re noticing with R.
As an aside, the best programmers these days are probabilistic programmers (who write stochastic functions). Our languages are Stan and PyMC. Both can be called by Python or R, and AI writes all of them extremely well. So it seems to me that the underlying language matters less than ever.
The gold standard and metalism generally, leads to all kinds of unproductive panics bc the quantity of money can’t wisely be adjusted to the situation. It’s a bad trade off, bc it’s well-known in the literature that inflation-targeting works (and that’s the current world-wide central bank policy since 1991).
I'd generally point to econometrics and statistics applied to business. The key activity is causal inference and then the context determines the mix of econo vs. stats required to help the org make high-quality decisions to increase output or make it more lucrative or higher-quality.
Econometricians can solve it, bc we can create rigorous models that map causal inputs to output.
It’s extremely advanced technology, though, and most CEOs would rather rent seek / camp than give up some decision-making power (and very few are even aware it’s possible).
I’d certainly not say it’s everything, look at all the highly-paid mediocre CEOs. Education has rigorously been shown to lead to higher incomes and wealth on average.
Yup! I was a part of the learn to code industry. I am proud of that, bc I know my worker helped a lot of marginalized people gain wealth and power (woo!). My own occupation, stats and econometrics, requires years of higher education to even begin (and decades to master), and yet ~ half of SWE were looking down on me, disrespecting me. To be clear, there were many who were not, but usually they were from some marginalized group: women, autistic, person of color, gay, person from a poor country, etc. I thought, why is my towering knowledge not being respected? Ah, the patriarchy combined with SWE. And then as time went on I just started using my knowledge for myself / those that know and that’s worked out well (bc it’s based on actually knowing math as opposed to relying on the patriarchy).
I think it’s possible the industry eventually figures out that statisticians and econometricians know far more than CS / SWEs (bc AI will tell people), but it could be a decade from now.
Agree, AIs are better decision-makers on average than people (just look at the grifters we've given power to). These are machines that can perform more advanced mathematics than even the most advanced mathematicians.
Sadly I think this post will mislead people, bc the difficult truth (for many) is that software engineering isn't that hard and that's why AI can easily substitute that layer (lower barriers to entry than widely believed).