And this is from the Cato Institute. I guess we found out who is the BS master.

I suspected at first, but it vanished on a second read.

On a related topic, there are some who argue meritocracy is bad. It makes those who are rich think they earned it and overvalue it as proof of their greatness.

Let me explain: I am well aware that current hype AI is not a big deal capability-wise and cannot really do much more than previous generations.

While the usefulness of LLMs is not quite clear (they will probably be useful for verifiable tasks like coding suggestions), I think it's indisputable that the image models are doing things much stronger than the previous generations and will be useful. Media and ad companies will be able to replace many of their artists. Whether this is a good or bad thing depends on your job.

And how do you know it's rewarded those who worked hard? Well, they're the rich ones of course.

More likely they are the descendants of the ones who worked hard. Unless by hard work, you mean lobbying to decrease any type of tax that could impact their dynasty inheritance.

I say the fine and what not belongs on the Judge for not checking the referenced cases.

I disagree. It's the laywer's responsibility to not lie in court. Using chatGPT is fine, but the laywer knows where he used it and can easily search to verify the claims. This is a situation where the LLM are ideal since it's relatively easy to verify.

"but we shouldn't wait for perfection before we start getting dangerous human drivers off the street"

I find it best to interpret something with the assumption the author is saying something intelligent. I read this more in terms of probability. Assuming the average autonomous car is significantly safer than the average driver then, on average, every human replaced is going to lower risk. Waiting until the autonomous cars are perfect (waiting forever) will unnecessarily kill people.

Who says (or why do you assume) I'm driving unsafely?

It's all relative. The point of the article is to claim that eventually everyone will be unsafe compared to a computer.

Articles like this assume that the only reason to get a college education is to get a job

And what do the students assume? How many are independently wealthy and are doing it for enrichment? Even the ones who don't care about money are probably looking to get a career out of it. Just because you are happy they have trained their brains doesn't mean they are happy with the results.

The real standard should be vs unimpaired, average or better adults.

While interesting from a research perspective, in practice it should be based on harm reduction. If 90% of the fatalities are caused by impaired, below average drivers then doing significantly better than them is a big win. (However, I doubt 90% of fatalities come from those categories.) Having a standard based on a good, constantly focused drivers will result in more harm.

This is absolutely false. There are seven states (and the District of Columbia) who have no restrictions on abortion whatsoever, up to and including abortion on the date of delivery

Do you have any numbers/examples of these abortions on or close to the date of delivery? Do you really think doctors are killing viable, healthy babies that have reached full term. This is already against the law, so their is no need to add a special law.

The kindest thing to say about the biological connections is that NN's were initially inspired by toy abstractions of neurons,repeated pointlessly and ad nauseam by successive researchers with no actual domain knowledge in the introductions of their papers and later books.

Which is why they worked in somewhat obscurity for so many years, particularly after the rise of the more practical theory. However, history might show that their toy models captured something essential particularly with things like convolutional neural networks. After all that really is the essence of a good model.

Beyond the dubious value of intriguing a new reader, biological analogies were neither useful nor actually used in most papers in the field of neural networks.

The usefulness is an architecture that works. And most papers are not useful.

The truth about ML is that it has almost nothing to do with mimicking brains or intelligence, and almost everything to do with statistical models and likelihood maximization scaled to large datasets.

Those statistical approaches are mathematically viable which is partially why they are studied. That makes them good for papers that look significant. However, the current, good empirical results are based on deep learning and are not so tractable. In fact, they work so well that the neuroscience people are now studying the connections between real brains and deep learning though it is controversial.

This is no criticism of ML, that foundation is sound and represents the best scientific guess at a collection of rational decision making mechanisms over the last 100 years.

The foundation is not sound. The theory doesn't explain why these neural networks work so well. I suppose we're lucky these NN researchers stuck with it. Hopefully the math will catch up.