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What I Worked On
71–80 of 421 posts
Re: What I Worked On
#72Quote: "This was in 9th grade, so I was 13 or 14"
You went to school at 4 years old?
Re: What I Worked On
#73Earlier quoted context omitted.
Something can be both legitimately revolutionary/interesting, but also significantly over-hyped and misrepresented, often with strong for-profit incentives. Some recent good examples of this include progress in cryptocurrencies, decentralization, and ML/AI.
Sure, but many people disagree that ML itself is revolutionary. The basics of it were known (referred to, quite appropriately, as 'data mining') as early as the 1990s and perhaps earlier. We've added a smattering of new techniques since then, and compute power has been expanded via GPGPU, but there was no "revolutionary" shift in the field. Even multi-layer ("deep") neural networks are very old tech.
As evidenced by gpt3 we still don’t know the best way to use deep learning. More data improved the outcome dramatically. What else might surprise us?
Re: What I Worked On
#74Earlier quoted context omitted.
Frankly, Paul Graham has a lot of money.
I know he does now but I’m talking about the earlier sections of the essay. He mentions taking time off from art school to work because he needed more money. What’s interesting to me is how he just decided to go to art school, study philosophy, or work on projects that had nothing to do with his career when he wasn’t already financially secure. The common narrative around really successful people is that they work re…
Wikipedia shows him as getting a BA from Cornell.
So is 'art school' Cornell?
Also he's 56, which means he still comes from an era where you might be able 'pay as you go' through college. So as to the question of 'was it money, class, or generational?' I think the answer is 'all three'.
Re: What I Worked On
#75Earlier quoted context omitted.
Something can be both legitimately revolutionary/interesting, but also significantly over-hyped and misrepresented, often with strong for-profit incentives. Some recent good examples of this include progress in cryptocurrencies, decentralization, and ML/AI.
Sure, but many people disagree that ML itself is revolutionary. The basics of it were known (referred to, quite appropriately, as 'data mining') as early as the 1990s and perhaps earlier. We've added a smattering of new techniques since then, and compute power has been expanded via GPGPU, but there was no "revolutionary" shift in the field. Even multi-layer ("deep") neural networks are very old tech.
The smattering of new techniques seem to have made the difference between success on toy problems vs. being able to match or exceed human performance on many difficult tasks. So while naysayers are correct that "the math hasn't changed since the 90s!", enough has changed to make calling DL a paradigm shift accurate.
For reference, I can now get an intern to images for a few hours, then train a black box algorithm to automate their efforts in another few hours. This algorithm is sensitive, brittle, and may have perfomance issues, but it's still already orders of magnitudes better than what took days or months of effort prior. That to me is a revolution, regardless of the math.
Re: What I Worked On
#76Quote: "This was in 9th grade, so I was 13 or 14" You went to school at 4 years old?
People start school at as young as 5, and sometimes they skip grades.
I was still 14 going when starting grade 9, no skipped grades.
Re: What I Worked On
#77Earlier quoted context omitted.
Funny because I do have the same feeling these days: that ML is a hoax. Even funnier: I do have a master's degree in ML.
It isn't a hoax, but the OP is exactly right: if it were more usable, people would see it for what it is, and not for what the silly media narrative makes it sounds like. As long as your technology is only usable by a high priesthood, you can make it look like magic.
Why do people feel entitled to "usable" ML at all?
In the last 5 years, we have made incomprehensibly huge improvements in power and usability. It's an active field, and improvements are still coming at a steady pace.
We have already revolutionized search, natural language processing & machine translation, image/audio/video processing, robotics, game AI, and advertising (for better or worse).
And on top of all this, we have significantly reduced the "time to first useful model", and we have significantly lowered the math and programming requirements for building and implementing models. And now we have transfer learning, which lets any old Joe Schmo benefit from massive computing power and datasets to build small on-device models that blow away SOTA accuracy from even a few years ago.
Oh, and the ML tooling ecosystem has become a substantial source of innovation in programming language design, "developer UX", and "data ops".
What the fuck more do you want? The people who seem the most upset that ML isn't magic seem to be the most confused about what ML even is and does.
Re: What I Worked On
#78I don't share the sentiment here. I find the essay too long and boring. I usually enjoy reading his valuable essays but this one is too much about his life which is not that interesting to be honest, given I don't know him personally. But that's OK, not every essay needs to appeal to everyone.
Re: What I Worked On
#79This was a good read. I wish I could understand the mindset of people who can live spontaneously like this. I always plan so far in the future because I feel like it’s way too easy to ruin my life financially. Is this a generational thing? A class difference?
check out “the middle class curse” for a class-based reflection on this
Link? The best Google result I could find given context was:
https://www.thefearlessman.com/the-curse-of-the-middle-class...
but there was a full page of different results under that title so I'm not sure it's what you were referring to.
Re: What I Worked On
#80Earlier quoted context omitted.
Something can be both legitimately revolutionary/interesting, but also significantly over-hyped and misrepresented, often with strong for-profit incentives. Some recent good examples of this include progress in cryptocurrencies, decentralization, and ML/AI.
Sure, but many people disagree that ML itself is revolutionary. The basics of it were known (referred to, quite appropriately, as 'data mining') as early as the 1990s and perhaps earlier. We've added a smattering of new techniques since then, and compute power has been expanded via GPGPU, but there was no "revolutionary" shift in the field. Even multi-layer ("deep") neural networks are very old tech.
Come on, really?
Electric motors and lithium batteries aren't new either. So much for the EV revolution, nothing to see here.