This is about society and economic systems. At no point did I say "you" although, given I used a term to describe competition which means pejoratively, something unreasonably treated as sacred enough to be unquestionable, your issue with my criticism doesn't seem like it's doing any favors.

You argued competition is part of life and the natural world. I'm not disagreeing, I'm adding that cooperation is a necessity and without it, there's nothing to benefit society, being a state of affairs more like Hobbes' war of all against all.

> They overcome overfitting using hundreds of billions of parameters

Increasing model size usually increases overfitting. The opposite effect comes from increasing the dataset size.

> Letting 5 people die because you don't want to personally be responsible by throwing the lever is still you being responsible for killing 5 people in the trolley problem.

I don't think you can justify this conclusion.

Today you could have done at least one thing that would stop at least one person from dying. Are you responsible for their death because you didn't give to a charity, or stop eating meat, or failed to take the bus to work?

This is a self defeating argument. No third party will ever gain traction if the argument against third party voting is that they won't gain traction.

Nothing wrong with that. Shoot, we have had to do that for many elections....

1. Take a look at neural scaling laws, figures 2 and 3 especially. Experiments show that more data and more compute are better. It's been a thing for a couple of years already, the paper has 260 citations, authored by OpenAI.

2. If you work with AI you know it always makes mistakes. Just like if you're using Google Search - you know you often have to work around its problems. Checking models not to make mistakes is big business today, called "human in the loop". There is awareness about model failure modes. Not to mention that even generative AIs like Stable Diffusion require lots of prompt massaging to work well.

3. sure

cognitive science - are we doing it right?

has anyone ever looked into how cognitive science is being approached? are we sure we even have the right/complete perspective to come to conclusions? are we sure the people studying it and influencing future tech are approaching it form the right end?

Observation/conclusion are key words here because really cognition is ultimately being used to derive these, so could it be that these experiments are biased/flawed to begin with?

> elevate competition to an extreme and once decently enjoyable (and beneficial) competitions become something else. ..

You keep suggesting I’m elevating competition simply by using it to describe one aspect of life. I’m not. I’m using it as a word because words are how we describe things.

If I describe the sky as being blue, I’m not elevating the word blue. I’m using it to describe the sky. That’s what words are for.

[deleted]

The main way its gonna make a statistical model worse is by increasing the computational power needed to run it. Thats not an argument about the quality of the model results though. I agree the author's understanding of statistics is really bad.

Low precision signals are crowding out high-precision signals - according to the good profs at HEC Finance faculty

I was trying to solve something? lol news to me.

Also, dont know what you are trying to say.

A bit tangential… but I’ve always thought the more data that is collected, the less likely we will be randomly targeted based on said data, but the more likely we might be intentionally targeted for said data. Meaning, if everyone has their SSNs leaked on the internet, you might be less likely to be randomly targeted as that number approaches the US population. However, you might be more likely to be intentionally targeted based on other demographic factors (assets, income, location, etc).

Another example: if Alexa listens to every word that everyone says, it seems to approach meaninglessness. Unless you begin to target particular pieces of information to isolate particular groups. Then it becomes increasingly beneficial. The key importance is to know how to sift through that information as the amount of information is amassed.

Your analogy only works if you have a moral framework that that can derive that killing you neighbor is bad. You haven't solved anything.

Nlp - natural language processing was around 10 years ago. But not commonly used 15 years ago

[deleted]

You know, as useful as it may be to think about extreme hypotheticals like survival strategies among shipwrecked sailors without any food, they aren't descriptive of ideal conditions in society or the economy. Extremes are what we generally want to avoid.

And they're certainly not descriptive of enjoyable past-times, like competitive sports where (hopefully) the competitors don't try to kill each other in the process.

That's my point: elevate competition to an extreme and once decently enjoyable (and beneficial) competitions become something else. ..

In walks AI NNs, excuse me?

Is OP TRIPPIN or what😱?

As someone who has a permanent severe mental illness - bipolar disorder - I have a strong view on your first question. 100% of our focus wrt laws on people such as myself should be on improving our lives. (My life is pretty sweet as it happens.) I've met many many people like myself and definitely never met a single one whose life could not be improved through medication, therapy - and you know, love.

This answer is a perfect example of what the OG author is talking about. This response does seem to come from a knowledgable person and the response seems well constructed but it does not address the point the author is making. But they are observant enough too state that the authors argument is unclear which in reality means that they did not understand it fully... Which is great at showing how 2 separate theories of Truth work for diferent people. Where the author is probably comming from some sort of relativism, the redditor comes from a theory where truth is objective and so claims not that the OG author's argument is difficult to understand but the argument is unclear, under a premise that they know what constitutes a clear argument. :D

Three takeaways:

1. The paradox of big data is that the more data we ransack for patterns, the more likely it is that what we find will be worthless or worse.
2. The real problem today is not that computers are smarter than us, but that we think that computers are smarter than us and trust them to make decisions for us that they should not be trusted to make.
3. In the age of Big Data and powerful computers, human wisdom, commonsense, and expertise are needed more than ever.

Thanks for the response. I would define the objective as "publicly observable" explainable and even a "shared subjective."

The fact that you are able to articulate your point and I can (try to) understand it , is what I mean by objective. Meanwhile, there are perceptions and experiences that you will have that I could never understand in the same way, no matter how much we were to communicate with one another. Thats the subjective.

Just like Kojima said, we need filters.

I can imagine that would be made easier using AI.

My girlfriend sent me this article and I responded with the same exact stance before reading your comment. This dude is a pseudo expert.