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.

Perhaps I should rephrase to say that it historically originated from brain research and probably wouldn't exist without that connection. While things like backprop are not biologically motivated, that was necessary to learn arbitrary functions. Current research is deviating in other ways based on what works using intuition and trial and error.

The neural network researchers worked in somewhat obscurity for many years presumably with the motivation that the best way to build something intelligent was to try and copy an already working system.

You should read up on brain science before making random unsupported statements about AI mimicking actual minds.

What he wrote looked fine. Much of the popular deep learning stuff is motivated by brain research. On the flip side, the neuroscience people are now studying connections between the ML models and real brain activity.

conservative media is not engaged in a discussion of subtleties. It's knowingly and deliberately engaged in a plan of mass deception.

While all 24/7 news sources are bad, I agree that conservative media is worse since they tend to argue in bad faith. Historically this goes back to Roger Ailes famous memo to Nixon about creating a propaganda network for conservatives. He was too late for Nixon, but its worked well in recent times. Perhaps too well...

Private equity exists to make profits. Hospitals exist to help sick people. Combine the two and you get "how can we profit from sick people"

Agreed. In the context of this study, medical errors generate more billable procedures, so they are incentived to cut corners especially if it increases the chance of billable problems. How many professions out there where you make more money when you screw up?

Given that Tucker can get away with it, I'm sure ChatGPT will skate free. https://www.npr.org/2020/09/29...

Weird that we cannot create such sets when it comes to scientific papers and doctorate theses...

The strong LLMs come from companies, and I'm sure they save their training sets and could retrain with data that existed before any poisoning of the well. (Assuming courts don't eventually force them to delete this data.) Presumably, they will consider the trade-offs of using newer data versus the older data along with architectural and training technique advancements to get better LLMs.

So no, the LLMs have no reason to get worse.

Also, we now have improved techniques to determine if a LLM generated some text. Roughly, you give the LLM some of the initial text and see how it completes the text. By testing with all the popular LLM models, with high accuracy you determine if a LLM from your test set generated the text. https://arxiv.org/abs/2305.173...

With Netflix unwilling to pay them enough to make up for declining broadcast/theater earnings, the studios had to start their own streaming services.

Corporations want to maximize money not just keep things the same. They started their own streaming services because they thought they could make more money than licensing to Netflix. They set their licensing to ridiculously high levels to match the ridiculously high values they though they could make off their own streaming service.

THIS got into Nature? Were the peer reviewers sleeping?

No offense, but I'll take those blind reviewers over your speculation based on a blurb about the paper and a search for Internet.

The abstract says "We find that the observed declines are unlikely to be driven by changes in the quality of published science, citation practices or field-specific factors", so it looks like they attempted to control for such factors.

Do you have an even longer list where the doctor got it right?

Not that I don't agree with you. Doctor's work well for statistically common problems, but are often frustratingly bad for more rare issues. I always found it validating how House got it wrong three times before he got it right. Normally when a doctor gets it wrong, you are off to the next doctor, and they never know/care they got it wrong.

Also, even when you get a correct diagnoses, most doctors are not up on the latest research. If you really research the problem yourself, you might not have the full picture a doctor has from their training and experience, but you will probably have better knowledge of some details and the current research. However, beware of single studies showing a result since we now know that various issues like publication bias and p hacking make much more than 5% of those invalid. It's probably closer to 50%.

I worked in that field (and atmospheric science / climatology) for 15 years. It used to be you needed to study thermodynamics, atmospheric chemistry and a whole range of related fields to be able to write a halfway decent prediction model.

Sounds like you are out of the field. Just because people are doing it differently than you did doesn't make them idiots. People will now have to study ML, math, and physics to understand how to improve ML generalization in the face of chaotic behavior. I'm sure many are trying to devise hybrid approaches that include invariants from physics in the ML.

Now I suppose it's true that someone can just cobble some ML together that gets surprisingly good performance and that the marginal gains beyond that with chaos are somewhat small. However, even in your day, someone could use an existing model if they weren't proprietary models that needed supercomputers for timely execution.

You need to take your meds, a literal socialist ran for president in 2016.

I assume you are talking about Bernie. First he lost, second he is a self-described democratic socialist not a socialist. https://en.wikipedia.org/wiki/...

Bernie's more in line with Nordic social democracy than some abstract evil notion of socialism. The Democrats consistently in power are more in line with moderate Republicans of the past.

In my opinion, the problem on the right is the political media machine that is built to pander at best and manipulate at worst. They purposely generate bad faith arguments meant to win talking points. Maybe this is not surprising from politicians, but the right needs a more honest media that attempts to hold people accountable and get closer to the truth.

While 24 hour cables networks on the left are mostly fluff, I feel the left media, in its many forms, has more people who actually care about making good faith arguments. These types of people on the right seem to have disappeared...