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.
Not everything in ML is as rosy as the papers make it out to be, but to call it a "hoax" is going way too far.
For example, a rocket landing on moon looks incredible. But I don’t think physicists would consider moon landing notable progress.
So, with data and compute you could do applications.
The range is literally from discovering that unit tests are good in software to discovering the Van Allen belts to learning about the geology and history of the Moon from the rocks that we brought back.
Could we have learned more science by doing something else with the money? Of course. But it is a dramatic overstatement to say that the Apollo program didn't "constitute any progress scientifically."
If you don’t believe an anonymous person here, see what prominent physicists say clearly on this topic, e.g., Steven Weinberg.
This is not to dismiss experimental research which is quite important, but to distinguish (experimental or theoretical) science from product development.
In https://www.thespacereview.com/article/1037/1 you'll find that he is very critical of manned spaceflight in general, but about Apollo he says, "No, at the time of Apollo, the astronauts did do some useful things. They brought back Lunar samples. They placed a laser reflector on the Moon that has been used ever since to monitor the motion of the Moon with incredible accuracy."
Earlier in the same interview he criticized NASA for canceling Apollo 18 and 19 because he wanted the science that would have been done, to be done.
I guess he didn't say what you thought he said.
That said, his criticism of NASA's efforts with manned flight isn't because he doesn't think that it is useless to go to the moon. It is because it takes a lot of work to get humans there, and robots can do the job much more safely and cheaper. Which also explains why he thought Apollo was useful. At the time the technology of robotics was much worse so humans were the only way to do the job.
If I recall correctly, an interviewer asks him about the scientific impact of landing on moon. He says, “it was there, but it not that great“ and “I think it was money spent on public amusement, and from all money spent on public amusement this money was best spent”
I am not pushing this view; just a relevant comment.
— update, exact statement
I think this was not money spent on science. It was money spent on an extremely important aspect of technology, and it was money spent on public amusement. And from all money spent on public amusement this chunk of money was best spent. [The scientific value], it was there but it was not very great.
He mentions one area, but then says, “but actually that could have been done much cheaper using unmanned robots”
I agree costs are issue here; money that could have been better spent.
Those were useful things that could have been done by unmanned rockets, but in those days, the state of the art in computers and robotics was not what it is now.
The whole "the state of the art" bit I understand as saying that with modern computers and robotics, unmanned vehicles could have done the job. But they didn't have advanced enough computers and robotics at the time.
It doesn't even make sense, it's like saying marijuana is a hoax because my uncle smokes pot and still got cancer.
Here are some alternative statements that make more sense (and contain more truth):
* There is a lot of snake oil and outright fraud being sold to unwitting managers.
* There is a lot of empty hype being fed to general public through the pop sci media and mainstream news.
* Deep learning specifically has not borne fruit in all (edit: or even most) problem domains.
* Lack of good quality data (and qualified people to analyze it) is a bigger problem than lack of advanced models and computing power.
There's plenty of data and compute power, but what's often lacking in the ML field is precisely models that reflect reasonable priors for one's given use case. Good feature engineering (often relying on domain experts) is similarly underrated. You see this again and again when looking at how robust SOTA results are achieved. In a way, this means that good (non-"hoax") ML is ultimately a lot more similar to traditional statistics than most practitioners are willing to acknowledge.
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.
This is part of the problem. Finding a the value of a few hyperparameters is hardly something I consider interesting science.
That's like saying "finding the sequence of assembly instructions / nucleotides / ... is hardly something I consider interesting science"
Electric motors and lithium batteries aren't new either. So much for the EV revolution, nothing to see here.
Hybrid vehicles are the practical option today. Pure gasoline vehicles are outmoded and EVs are all hype.
As long as your technology is only usable by a high priesthood, you can make it look like magic.
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.
Yeah, people are annoying, with their demands to use software themselves. It would be much easier for everyone if computers were controlled by an elite group of engineers who could hide the complexity from the rest of us. Perhaps they could wear labcoats.
> What the fuck more do you want?
If I knew the answer to that, life would be a lot simpler.
What are you even talking about?
It sounds like you're upset that cutting-edge technology still requires training & expertise to use and deploy effectively in industry.
That's clearly not what nerdponx meant. Can you please stick to the site guidelines? "Please respond to the strongest plausible interpretation of what someone says, not a weaker one that's easier to criticize. Assume good faith." https://news.ycombinator.com/newsguidelines.html
> If I knew the answer to that, life would be a lot simpler.
That was a nice de-escalation.
I disagree. I made a straightforward interpretation of what was written. Given what came after the part I quoted, you really have to stretch to interpret it differently.
The OP underscored the same point using profanity.
I made a response that was clearly sardonic, attempting to be funny.
Luckily due to so many people using ML these days, what's useful vs. fluff gets sorted out over time.
This is a good list of promising work, but showing practicality would need more explanation.
(similar for Boston Dynamics, and IBM Watson)
alpha go, alphafold2, gpt-3, image recognition benchmarks etc?
i work in ML as well* and while its certainly overhyped and often sold prematurelt, a lot of the stuff is real and works very very well.
* i build stuff for cities to more efficiently monitor their citizens (no tracking or personal identification, just aggregate numbers)
I like this as a descriptive phrase. ML won't be "intelligence" per se, but it can do repetitive tasks that otherwise required thought.
With emerging digital tools at the time which made it possible to store & retrieve datasets that I was already interpreting in detail anyway.
Since it was programmable too, ended up using the memory to store the key points from many permutations of well-characterized raw training data, then running that against new datasets to give me advice on how to save time on the greatly reduced manual work remaining.