Is there another forum where all the hackers went, I think I want to go somewhere else.
Edit: I take the part about wood working, I think that's a reasonable response to the way today's tech jobs make us feel. I know many people who'd like to quit and just work on side-projects (or wood working), but they don't hate technology itself, that's the difference.
Yes, it's called experience. After one has seen all those "new breakthroughs" hyped to high heaven and amounting to nothing, they have learned not to take salesmen at face value.
Of course some people remain eternally starry eyed, as if they've just came down from a bus from Nebraska to L.A. to become movie stars. Sadly, they're still not the ones doing the real innovation. Just the ones taken for a ride by one hype after another.
Tech is a means to an end; specifically a way to make a living and hopefully buy myself out of the rat race.
If that isn’t “real hacker” enough for you then that’s fine. In my book, lifestyle design is the hacker ethos applied at the meta level.
"Means to an end", "make a living", "rat race", yeah I don't think that would be considered "hacker" by any definition of the word (I think the word is "corporate"), and this site is called "Hacker News".
You don't have to be excited and bullish about every single new trend, but at least be interested enough to take a closer look. I was interested in crypto a few years ago, now I don't care much about it. Still excited about VR and AI though. I mean, it's a free country, but if this pessimism is the default attitude here on anything, I think it's time to find where all the actual hackers are hanging out.
> I don't think that would be considered "hacker" by any definition of the word (I think the word is "corporate")
Tempered passion != corporate.
> You don't have to be excited and bullish about every single new trend, but at least be interested enough to take a closer look
No, I don't owe trends anything. It's easy to trend, and much harder to deliver value over time. I wait for the wheat to separate from the chaff before investing time. This is not pessimism, this is triage of emotions and attention. It isn't a belief that what we have is best, but rather a preference to be doing the work as much as possible. Time, unfortunately, is zero sum.
I have stopped chasing relevance, and in doing so, found contentment and focus. I'll reiterate: the self-work I do is my current manifestation of the hacker ethos.
I am excited by PL stuff: Rust, Haskell, compilers, LLVM, etc. I write Rust and Haskell at work, so I scratch a lot of that itch on the job. It took a long time to get to that point. I still have too many tabs open on my computers about cool PL stuff like the u-combinator, GC implementations, cache friendliness, and other low-level esoterica.
Yet, life is much better when tech is a part of it, not the centerpiece of identity. Perhaps that is the main difference that is surfacing here.
Ah, if only that was the case... One can dream, however...
Mind you, when real hackers walked the earth (MIT, AT&T, Xerox, and so on), they weren't that hot about hypes either, and they couldn't be further from today's marketing types.
Those were hackers: programmers, scientists, enthusiasts, and so on.
Not today's used-car-salemen types touting AI, big data, VR, and the rest...
Are you really trying to convince me that the original hackers were tech-conservatives? They literally set the trends and built the future.
Who are these marketing types you talk of? I don't dispute there is a lot of fluff and hype going around, but you chose yourself who you listen to. I follow true hackers like John Carmack, Jeri Ellsworth, Andrej Karpathy, George Hotz and prominent scientists like Yann LeCun. If all you find is hype and marketing that's on you. It's like people complaining that Twitter is bad, you decide who to follow!
It's better to not extrapolate S-curves into exponential functions when you don't know what you are talking about, yes. A good advice for both DL researchers and "techno-optimists" who haven't seen unsubstantiated hype they didn't jump on yet.
At this point, even a rolling average is labeled as AI.
There fixed it for you ...
However, that is the cycle of society. 1990's all you had to do wat put '.com' in your company name and you were a paper millionaire overnight. 2000's "Social, 2010's mobile first. Metaverse, AI and beyond.
Is this sarcastic or something? Math understanding is a fractal, the closer you look at a branch the bigger it gets. Statistics in particular seems to have countless real world applications.
In my experience, non-AI models tend to outperform the AI ones in many applications.
This is different than how AI is marketed.
Which is why things like manifolds and topology are useful for analyzing our models, because all of the ways to encode mathematics are equivalent and you can transport ideas between them.
The model building process for these translates well to other types of models.
Decision trees and random forests show up too.
I think data science is great in that it exposes one to programming and technology moreso than a focus in math or stats, I was mostly complaining about the marketing I’ve seen surrounding AI. I recall listening to a few mba consultants claiming that they could improve a business process significantly by using ML even though there was sparse inaccurate data available and they had no modeling background.
Overall I think AI models are interesting and often useful (although not sure what fits under the umbrella), but am tired of the hype, strong opinions, and amount of claimed expertise.
It is much more then a small branch of math given it's foundational relation to the real world.
Intuitively AI is much more just a branch of math. We are getting closer and closer to understand theoretically how our own brains work. The philosophical connections are profound and you are using language categories to limit your thinking.
Of course if they overpromise, then reality will catch them and the company will fail.. but this has nothing to do with statistics.
First, statistics is not a small branch of math, proability theory is (not a small branch by the way, not by a long shot, just a branch, one of the fundamental and very large ones). Statistics is the art and science of dealing with data, it uses probability theory like physics uses math, but is far more than probability theory just like physics is far more than math.
Second, Neural nets and machine learning in general is an extremly powerful way of solving problems of certain structure and form, and it solves problems not encountered in statistics. Claiming it's a small branch of statistics is like claiming that the entirety of computer science is a small branch of logic.
How significant are these top 3 things?
Have you ever built a production model for a large company or have a tier 1 phd? If not, what are your qualifications for labeling a claim ridiculous.
Off the top of my head and in no particular order :
1- Playing human games at superhuman performance from raw pixels
2- Recognizing objects and people in images at human or better performance
3- Recognizing human voice in real world noisy environments and parsing it into text at human or better performance
>How significant are these top 3 things?
They generate billions of dollars of profit and beat human knowledge and expertise that took thousands\millions of years to craft and\or evolve with nothing but a GPU and a lot of electricity.
>Have you ever built a production model for a large company or have a tier 1 phd?
No.
>what are your qualifications for labeling a claim ridiculous.
I have a brain that can spot ridiculous hyperbolic claims, and experience-backed knowledge with neural networks and machine learning that can explain with examples why they are ridiculous and hyperbolic.
If you do have the qualifications you request, start with using them to support your claims. Here's a small challenge that should pose no difficulty to you.
Show me, with derivations and citations, how an RNN is "just" an obscure statistical model. Which statistician first published or applied it ? Is it taught in a statistics course you know of ? Which ones ? Are research statisticians publishing on it right now or ever ? In what sense is it part of the science of statistics like, say, the normal distribution is ?
Number 1 has probably generated ~0$ in profits. It's not immediately clear that number 3, when weighting its contribution apart from everything else in the products it's integrated in, has reached 10-digits in $ profits.
So you're only left with number 2.
I used games as examples just because they are the easiest to understand and most popular applications of RL, and also very general, but that would be like saying that CNNs are useless because recognizing cats is not a bussiness, recognizing cats is just 1 example of the vast array of things CNNs can do. For every optimization algorithm pioneered by RL research in order to play a useless $0-return game, you can bet that it's later implemented and used in other areas to save and generate millions or billions of dollars. Games are just the test playground for new research.
I don't understand how #3 can't be credited with the billions of dollars the products and services based on it generate. According to [2], the global market of offering a speech-to-text API is valued at 1.3 billion dollars in 2019, if you assume that just 20% of that is actual customer value (to account for hype, errors in valuation, etc...), that's about 260 million dollar per year. Even an extremly conservative value estimate tells you the technology generates a billion dollar every 4 year (if it stays constant, the forecast projects the market will reach >3 billions by 2027). Keep in mind that this does not include any value generated from any other component or product, this is just the profits from the APIs alone, the generous 20% discount is to account for hype, analysis errors and bad data.
But you know what ? the broader point is, these are just 3 examples that I pulled off the top of my head while I was making a 5 minute comment without consulting google. And considering the person I'm replying to never provided a single example or any kind of specific clarification till now, or anwered any of my questions or otherwise offered any kind of rebuttal, I think it's pretty fair to say they hold up nicely.
[0] https://developer.nvidia.com/blog/designing-arithmetic-circu...
[1] https://www.nature.com/articles/s41586-021-04301-9
[2] https://www.fortunebusinessinsights.com/speech-to-text-api-m...
No, you used it in exactly this context:
" >How significant are these top 3 things?
They generate billions of dollars of profit "
and the claim was incorrect regarding example 1.
I also don't think you understand what profit is and the difference between profit and revenue. Regardless, this exchange has become pretty pointless.
So by this reasoning, something like Linux has exactly $0 value in monetary terms. Linux is never an application, it never does something that actual users pay for (the "users" who pay for Linux support are just developers and corporations who use it to develop something else).
>I also don't think you understand what profit is and the difference between profit and revenue.
Yes, I don't understand an elementary distinction between terms that anybody with a dictionary understands. This is quite a fair and productive point for you to make.
>this exchange has become pretty pointless.
Probably the one thing you're right about in this, I'm indeed not interested in debating incredibly insecure people fond of accusing others of ignorance (quite often a projection technique) instead of engaging with their arguments or citing specific examples or research.
For the sake of analogy, perhaps I should read some articles on equine veterinary practices and start talking down to practitioners in the field, all for the sake of making my internet ego feel better.
Yeah, 15 years in industry and academia, and you don't know how statistics and probability theory are different.
May you have a nice happy day\night my guy.
Yes, you actually don't understand them. Which is why you said this
>the global market of offering a speech-to-text API is valued at 1.3 billion dollars in 2019
which is about revenue when you previously were talking about profits. It's obvious who is the insecure one in this exchange.
The very same ones did it with 'open-source' (hijacking 'free-software' and creating companies that develop closed-source products). The same happened with the Web (and has been ruined by the ads industry thanks to Big Tech), IoT (with phones being the worst IoT device), Blockchains (web3 being yet another hyped up narrative duping retail investors), and now AI once again (with Deep Learning) is already creating a new grift after the release of Stable Diffusion.
All together have been a total disaster towards privacy and are quite frankly dystopian creations and tools for the technologist grifter.