On the value capture question, it's really not clear that the ability to write code at superhuman speed actually translates to an increase in the speed with which we can create new, better technology products that customers will pay more money for, vs. just enabling layoffs.
You could say the same thing about compilers versus assemblers, high-level languages versus low-level ones, and services and libraries versus monolithic programs.
All other things being equal, increasing the speed of some part of the development process will increase the overall pace of development. However, By Amdahl's law that increase will be sublinear, and that is why we should take "pull requests" as an imperfect metric.
We also don't get to pick the form that 'better technology products' take. While we'd probably like to keep cost(/effort) and complexity constant and increase robustness and performance, the market equilibrium might be 'worse is better' and reward whiz-bang features and lower effort.
Also, large companies are still experimenting on how to integrate LLMs into their workflows. Due to the fast cadence of releases, people forget that LLMs became robust (regardless of the capability level/parameter count/data size) enough to use semi-reliably in company-specific ways only 1 year ago. And bigger the org, the slower the process. I don't expect it to settle and get productive used across the majority of very large companies for another year atleast.
High prices are mostly a result of DC capacity. As more and more DCs get built out, prices will drop. At a unit level the inference business is extremely sound regardless.
> You could say the same thing about compilers versus assemblers, high-level languages versus low-level ones, and services and libraries versus monolithic programs.
Right, but we didn't invest a trillion dollars of capital into any of those things in the span of a couple years, thus forcing them to capture value and show a return on such a massive investment.
Reads like fearmongering about oppressive and violent applications of AI that the Chinese government hypothetically could deploy, which the US government is already actively working on in the meantime.
Only perfect multicollinearity (correlation of 1.0 or -1.0) is a problem at the linear algebra level when fitting a statistical model.
But theoretically speaking, in a scientific context, why would you want to fit an explanatory model that includes multiple highly (but not perfectly) correlated independent variables?
It shouldn't be an accident. Usually it's because you've intentionally taken multiple proxy measurements of the same theoretical latent variable and you want to reduce measurement error. So that becomes a part of your measurement and modeling strategy.
You make a good point, though the difference between ML and statistics isn't just about interpreting and validating the model. It's about the "novel discoveries" part aka Doing Science.
Statistical modeling is done primarily in service of scientific discovery--for the purpose of making an inference (population estimate from a sample) or a comparison to test a hypothesis derived from a theoretical causal model of a real-world process before viewing data. The parameters of a model are interpreted because they represent an estimate of a treatment effect of some intervention.
Methods like PCA can be part of that modeling process either way, but analyzing and fitting models to data to mine it for patterns without an a priori hypothesis is not science.
> A sailor is sailing her boat across the lake on a windy day. As the wind blows, she counters by turning the rudder in such a way so as to exactly offset the force of the wind. Back and forth she moves the rudder, yet the boat follows a straight line across the lake. A kindhearted yet naive person with no knowledge of wind or boats might look at this woman and say, “Someone get this sailor a new rudder! Hers is broken!” He thinks this because he cannot see any relationship between the movement of the rudder and the direction of the boat.
Indeed, causally linked variables need not be correlated in observed data; bias in the opposite direction of the causal effect may approximately equal or exceed it in magnitude and "mask" the correlation. Chapter 1 of this popular causal inference book demonstrates this with a few examples: https://mixtape.scunning.com/01-introduction#do-not-confuse-...
