> No

It seems like the two of you mostly agree, no?

> Why do you sneer at the process that has resulted in immense wealth and better lives for billions of people?

I am suspicious of anyone who speaks of their industry and every single practitioner within it as being purely rational, or essentially flawless. Of course, this "wasn't what you meant", but that's kind of my complaint.

Another aspect: presumably you're on Hacker News - I've observed people there "telling it how it is" for way over a decade, so I have a decent amount of exposure to how (a substantial sampling of) tech people think across a wide variety of ideas (including how thinking styles change depending on the topic), and how confident they can be in various beliefs (perceived as knowledge) they hold.

I am sure he had something, but I don't believe it was comparable to what we have today. Lots of research has been done in the last 10 years.

It is valuable, but in a different sense. I would learn more given my subjective goal of learning more. But I already assumed that all my goals (whether you call them moral or not) are just configurations of ions, synaptic receptors etc. Nothing in the description has yet justified the value.

The thing keeping third parties from being relevant is the voting system itself. One cannot sanely advocate for third parties without first advocating changing the voting system itself to something else that would allow for the relevance of third parties like some kind of ranked choice voting. The Nash Equilibrium for a first past the post, single vote system where voters have some information about past and current voting patterns is a two party/candidate hegemony.

I've been using want since my first post to define happiness. Happiness is the way we want to feel.

I do believe I am sidestepping the is ought gap.

I didn't say that satisfying wants is right. I said happiness is the way we want to feel.

Thank you for your reply.

Perhaps I phrased it poorly. You are correct, of course, that increasing model size tends to increase overfitting in the normal sense. Overfitting in this case means a failure of generalization. This would also lead to bad results in new data.

In spoke in the context of this article, which claimed that spurious generalizations are found. LLMs move two parameters up in parallel in order to produced the amazing results that they do. They increase both the quantity of data and the numbers of parameters.

Yes we cooperate when that is more sensible than competing. I’m simply saying that describing life as being competitive doesn’t elevate the concept of competition.

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Yes, I agree the recent breakthroughs are staggering, and NLP is moving along rapidly. But my point still stands: this guy's firm had it working well in 2012, and it was being secretly used by the US government.

I read before that at least a big part of the replication crisis is cherry-picking of data by researchers themselves. What would you say are other reasons? Badly recorded protocols/methodologies?

Not giving to charity so that the charity doesn't have enough money so that one person is ultimately bumped from their services so that they eventually go on to starve is such an insanely far cry from you could have thrown a lever that right then and there would have prevented 5 deaths that they are not equivalent, they aren't even in the same country. Unless there was a switch on the bus that says don't kill people and no one else was on that bus it isn't even close to a fair version of the trolley problem. There's nothing special about you or I that just us riding the bus is going to save someone's life, generally. Now, if I happen to be the only person who could have performed an emergency tracheotomy(thanks boy scouts) and I could have been on the bus when it was necessary to know how to do that and so someone died, no I wouldn't consider myself responsible because I wasn't literally in the room. If on the other hand I was on that bus and I can do what's needed and I don't, I would consider myself culpable in their death. I don't think that it is wrong to allow a death to happen but I do think I'd have some of the responsibility for their death.

If you are driving today and someone jumps out in front of you and you could swerve away but you don't are you responsible for that person's death? You didn't do anything to actively cause the death like steer towards them or accelerate as they tried to cross but you probably could have braked or swerved and not hit them. That's the real version of the trolley problem. The trolley problem can't exist because unless you know an evil demon bent on driving you crazy you don't just suddenly appear on a trolley a few seconds away from killing people. That can't happen. We don't spontaneously appear in locations. We choose where we go and what we do. We choose to put ourselves in situations or at least in the places that those situations can happen.

You aren't supposed to swerve away from an accident, did you know that? If you could avoid hitting another car by swerving away from it, generally, you are supposed to still hit the car because by swerving away you might hit something and then it would be your fault. If you, for instance, tried to avoid a deer in the road and thus ended up swinging your car and crossing the yellow line and hitting a car you are liable for the crash where before hitting the deer you might not have been liable and or even if you are liable for that crash you at least didn't hit another car.

I was referring to myself as a layman.

I am a layman as well but as far as I understand the article, as it talks about meaning and relation, variance mentioned by the commentor is not relevant. And I can see how it can be misconstrued as relevant when talking about meaning. It depends if meaningfull is understood as data extrapolation itself or its corelation to factual aplication.

As a layman your supposed alternative interpretation of the article’s arguments makes them sound quite simplistic and not at all difficult to understand. Reductive even. Which makes me suspect your suggestion that OP didn’t understand the article came directly from your butthole.

1. Ok, you’ve just claimed the opposite of what OP claimed, and provided far less evidence (ie none) to back it up compared to what OP provided.

2. This sounds like it has nothing to do with having more or less data.

3. Ditto

Many Aristoteleans (virtue ethics) I know of thought very systemically…

Taleb has said that like 10 years ago.

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Your vote is part of a signal. It is unfortunate that one cannot simultaneously signal "I don't like the B option" without also signaling "I'm okay with the A option" when there are practically 2 options + abstaining.

Nevertheless, you should think on the margin - the people most likely to skip voting for B are the people who dislike B the most and are okay with A the most, and will probably not be swayed by the usual argumetns. If anything, they're part of the reason they'll skip.

If I understand the point of the article it really isn’t talking about “more data” in the sense of a bigger data set to train the model on a given task. He is more commenting that given a nonsense task (like predicting criminality from a photo of a face) you can find some data to train your model. You might then be seduced by your results into thinking that the task makes sense and that your model does something meaningful. That it found a modern theory of skull shapes or whatever, when all it really did was classify mugshots.

In other words he is not addressing the cutting edge of AI research, but rather the wide eyed practitioners (and - inevitably - charlatans) who will gladly sell a model for an impossible task.

Every scientific paradigm is a model. We do not have direct access to underlying reality, we only have maps put together based on the observations we can make.

Real data is biased and unbalanced. The "long tail" is hard to learn, for example there are papers trying to balance it out for those rare classes. Unfortunately most datasets follow a power law so they have many rare classes.

No, models are tools, it's how you wield them. What I noticed is that models tend to attract activist types that have an agenda to push, so they try to control it. Not just in AI, also in Economics and other fields.

In 2012 NLP was in its infancy. We were using recurrent neural nets called LSTMs but they could not handle long range contextual dependencies and were difficult to scale up.

In 2017 we got a breakthrough with the paper "Attention is all you need", suddenly long range context and fast/scalable learning was possible. By 2020 we got GPT-3, and in this year there are over 10 alternative models, some open sourced. They all trained on an amazing volume of text and exhibit signs of generality in their abilities. Today NLP can solve difficult problems, in code, math and natural language.