Sufficient if all else were equal. But the human brain and artificial neural networks are clearly not equal. This is setting aside the whole question of whether we hope to equal human performance or exceed it.
Isn't this a little bit of a category error? LLMs are not a language. But prompts to LLMs are written in a language, more or less a natural language such as English. Unfortunately, natural languages are not very precise and full of ambiguity. I suspect that different models would interpret wordings and phrases slightly differently, leading to behaviors in the resulting code that are difficult to predict.
Note that many universities still have DEI offices. I believe that they are interpreting as described here: https://www.governmentcontractorcomplianceupdate.com/2025/08.... So as long as they can show that they are not doing any of those, they seem to believe that they will be okay.
I agree that the goals are worthwhile, and also feel that requiring every proposal to include this is not efficient and/or very effective. They should take all the funds and time spent on this every year as part of every award, and just fund programs specifically designed to attract inner-city kids to science, or funnel talented, low-income, high school students to be mentored, taught advanced classes, etc.
I would be happy to spend time mentoring URM, etc. But it'd work a lot better if others managed such a program, thought about how to attract them, etc. Specialization is good.
The bank is context that gives us a prior probability. However, MLE does not consider a prior. So MLE can give results that are not very helpful in the real world. All it does is answer: What parameter value (in case the probability) of a head, makes the observed outcome most likely? But it considers all parameter values equally likely. In reality, we know that it is highly likely that a random coin from a bank is a fair coin. Thus, if we flip two heads, we are almost certain that it's still a fair coin. If, on the other hand, we flipped 10 heads in a row, we might start to wonder if somehow the bank gave you a trick coin. MAP is an alternative to MLE, arguably better in many situations: [https://www.cs.cmu.edu/~aarti/Class/10701_Spring23/Lecs/Lect....
My favorite MLE example: Suppose you walk into a bank and ask them to give you a quarter. You flip the quarter twice and get two heads. Given this experiment, what do you estimate to be the probability p of getting a heads when you flip this coin? Using MLE, you would get p = 1. In other words, this coin will always give you a heads when you flip it! (According to MLE.)