Do you think it is possible to that without any background in anything? I mean could someone apply black box frameworks without understanding them. How would they be caught?
To do machine learning research? Or work in some random domain?
> I mean could someone apply black box frameworks without understanding them. How would they be caught?
Machine learning is rapidly becoming commoditised, but lots of people still don't understand just how much effort it is to get a good dataset and to prep
Domain experts scoff at machine learning people who are trying to solve Big Problems using unrepresentative toy datasets, but also tend to have much higher expectations of what ML can do. Machine learning people scoff at domain experts for using outdated techniques and bad data science, but then propose ridiculous solutions that would never work in the real world (e.g. use our model, it takes a week on 8xV100s to train and you can only run it on a computer the size of a bus).
There are also a lot of people (and companies) touting machine learning as a solution to problems that don't exist.
Overfitting models is probably the most rampant crime that researchers commit.
From the second half of your comment it seems that the answer is yes?
Maybe a comparison would help: someone pretending to be an experienced iOS/Android developer without any qualifications or ability would quickly be caught. Since they couldn't produce any working app or use a compiler, and anyone can judge an app for themselves. You can't really just make it up out of whole cloth, people judge the results. You would have to start actually doing that, and if you couldn't or didn't want to, then unless you outsourced your own job or something the jig would be up pretty much instantly. (Unless you caught up.)
So, how about machine learning? Do you think a fraud could land and keep such a job, without any knowledge, qualifications, ability, or even interest in getting up to speed? Just, a pure, simple fraud.
What's your guess?
Fake it til you make it isn't a terrible strategy. But pure fraud? If you didn't even make an attempt to learn on the job? You'd get caught pretty fast as soon as someone started asking any kind of in depth questions about the models you were supposed to be training.
I'm not sure you could land a job knowing nothing. Maybe. Depends how hard you get interviewed and whether they know about machine learning. If you could fake a portfolio and nobody questioned it perhaps? I can see that happening in academia for sure.
There are a few problem classes where you could throw stuff into a black box and get great results out. Image classification for example. Fast.ai have made that three lines of code.
So maybe there are a bunch of applications where you could fake it, especially if you were willing to Google your way round the answers.
Would be harder in industry I think, but you find incompetent people everywhere.
>But pure fraud? If you didn't even make an attempt to learn on the job? You'd get caught pretty fast as soon as someone started asking any kind of in depth questions about the models you were supposed to be training.
That's just what I mean. It would depend on someone asking you about it, right? (As opposed to being an iOS or Android developer or running microservices on the backend: in those domains nobody has to ask you anything, it's instantly obvious if you're not building and can't build anything.)
For machine learning, who is asking these questions?
If you throw data into a black box (3 lines of code) and are incompetent, can you please tell me a bit more about where you would get found out?
Let's use your example, ecology.
I show up, I get a dataset, and I put it into tensorflow using three lines of code I copy from stackoverflow.
I lie and bullshit about the details of what I'm doing, by referencing papers from arxiv.org that I don't read, understand, or actually apply. It's just the same 3 lines of code I copied on day 1. I don't do anything on the job.
How long could I last? An hour? A day? A week? A month?
Assuming I am outputting 0 useful work. I'm not doing any machine learning. Just 0 competence, or I make something up by hand or in excel.
I am trying to understand how people are judged.
If you really wanted to you could fabricate results and in lots of cases nobody would be any the wiser unless you were supposed to be releasing software. Despite emphasis on peer review and repeatability, science relies heavily on etiquette. If you don't release code or a dataset a lot of times it's extremely difficult to repeat paper results, and that also means it's hard to disprove the work.
It's quite hard to get rid of incompetent people in academia, so I imagine you could get away with at least a year or two.
It's difficult to get any true researcher position without a PhD. It doesn't mean that PhD has to be in AI. Research involves a lot of reading and writing papers, which a PhD is supposedly training you how to do.
That said most places will say "equivalent practical experience" and it's entirely possible to be competent in AI/ML without a PhD.
I did a PhD in space science, I now do machine learning in ecology and spent the summer working on machine learning for disaster management. The interesting jobs (to me) are where domains cross, and it's also (hint hint) much easier to get a job doing AI for X than it is doing "fundamental AI". In any case, you're often doing stuff that nobody has done before anyway, but you don't need to spend your life hunting for the new ResNet.
> Also, why is NEURIPS or ICML papers is not a hiring guarantee?
What the OP probably might be implying is that everyone has a publication in NeurIPS nowadays.
I think it goes deeper than that though, publishing in machine learning is broken. Having 10k people at one conference is not an efficient way to distribute research. You have to submit a full paper in November for a conference next Summer - pretty much only computer science does this madness.
What's interesting is how unique this attitude is. In astronomy, for example, conferences are a fun place to catch up with folks in your niche. There might be a few hundred people and probably it'll be single-track. We publish whatever journal is the most relevant and they're generally all considered equivalent. Nobody cares if you publish in ApJ vs A&A vs MNRAS, if your research is good.
There are also concerns that the quality of these venues is decreasing because the pressure to publish in them is so high.
See https://arxiv.org/abs/1807.03341