drzzhan

For most aspects my opinions don't change. But on coding, it has changed a lot. Two years ago I would just dismiss at the possibility that llm could write up a simple function. Now it has proven me wrong.

This is so fun. I wonder if there is a similar page for other animals,

I don't know if "AI agrees" is reliable enough to count. Do you have a particular scientific article that you based on? And what are your example? Your post is mostly describing without concrete example so I can't follow.

An open model that is competitive to commercial models is a big deal if true. I hope someday I can find a way to run such high performance model locally on my laptop.

There is an article said that a DOGE member had write access as well (See [1]). But it was quickly changed back to read only. So there was a risk, but I can only hope nothing happened.

[1] https://www.foxnews.com/politics/treasury-mistakenly-gave-mu...

I think there is an article said that a DOGE member has write access as well.

The best analogy I've ever seen. Thank you for sharing!

ImageNet dataset is the main thing AFAIK. But even so I find Dr. Li's contribution big enough. For a context, datasets for computer vision at her time were mostly small, so nn was rarely considered a good method for CV. Not until AlexNet won the challenge, and the world changes after that. I remember many people initially scoffed at ImageNet, arguing that the dataset was flawed and that a “bad” method like NN (AlexNet) could only win because of those flaws. Simply saying “paying” is an understatement because we also need to account for the academic politics of her time. A little fun fact, even if most research papers nowadays try to propose new dataset, if we take imagenet and pretrain the backbone, we usually end up with a very strong baseline.

Btw, not sure why you think Karpathy has a bigger impact than Fei-Fei Li. I can't think what he is doing that is actually changing the playing field.

A very good and solid guide.

When I was a college student, I attended a conference regarding IoT. I still remember a developer of a face ID software for a safe box was so sure that their technology was secure because Apple deployed face ID on iPhone. Then a few days later, an article about fooling iPhone's face ID with a printed paper with the victim's face got published.

I am starting to think the only one stay at OpenAI in the end is Sam Altman at this rate.

Wow. Good insight! I have never viewed vacation this way. I always thought the only reason vacation exists is because companies have to give vacation due to laws and well being of their employee. So when I get some days off, I always feel a bit guilty because the company is losing out something.

RIP.

Yeah I was confused for a moment there.

Thank you for your answer! TIL a new word: qualia I like your two examples. They have different perspectives. Though I must say the "qualia to manifestation" is still a bit abstract for me now. I'll keep them in mind.

That is such a good quote to remember. Thanks for your answer! It actually opened my understanding a lot. I searched your quote and found some other good ones.

Wow. Thank you for your thoughtful answer! > “human creativity is the ability to create an infinite range of outputs from a finite range of inputs that nonetheless pertain to our motivations/context in some useful way” Do you think this can be used as a metric? Like the more useful answer we can come up with, the more "creative" we are. The constraint of outputs and "some useful way" is such a good insight.

That's interesting. I am young so I don't know what actually is creativity. Could you explain that part for me?

Wow that's cool.

> “With the addition of the far-infrared measurements from PREFIRE, we’re seeing for the first time the full energy spectrum that Earth radiates into space, which is critical to understanding climate change.”

Does anyone know what the other measurements are, besides far-infrared? I wonder how they will deal with those complex data to understand cloud and heat. This sounds vague and complicated to me.

I see. I was sad but you are right. This is an understandable decision.

True. Actually, researchers know about it. In a sense, i feel "hallucination" is a way to keep the hype up, that we could fix it in the future, given enough data and compute and money. On the other hand, "generating false information by design" is too strong and negative. At least that's what I hear around in my research community.

I learned scrum back in college and I hated it so much. I remember we had to meet 2-3 times a weak. And for each meeting we had to present something, with slides as well. We spend more time thinking about usercase than writing the code. I guess that's good in its own way but honestly I don't have such superpower to report something new every meeting. In fact most of my meeting back then were "I am still coding X" and that was it, then the scrum master or the leader would talk about big picture over again.

What??? Oh no. I love that feature so much. What should I use in the future then? IA can be a solution but often the link I am interested in is not there. For example, foreign news from developing country.

This is not related to Lean or Haskell. I'm just wondering why when people are curious about a new general-purpose language, the first thing they test is an HTTP server.

It's always nice to know that people can store their data for so long. In my research lab, we still only use separate external HDD drives due to budget reasons. Last year 4 (over 8) drives failed and we lost the data. I guess we mainly work with public data so it is not a big deal. But, it is a dream of mine to research without such worries. I do keep backups for my stuff though, but only me in my lab.

Great tool! But the City, Country box is a bit unintuitive. I am not sure how to fill my place in. I live in the US so it is "city, state, country"

C# is so painful to find what I want. Last time I want to write a quick GUI, I gave up and go to youtube because they are simply easier.

So it's like the coding agent of gpt4. But instead of actually running the script and fix if it gets error, this one check with something similar to "are you sure". Thank for the link.

"hidden chain of thought" is basically the finetuned prompt isn't it? The time scale x-axis is hidden as well. Not sure how they model the gpt for it to have an ability to decide when to stop CoT and actually answer.

Maybe they really meant LLVM? Large language vision model.