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IMO the real issue is that Facebook wanted a commercially-usable product, and they thought Kaggle had all the safeguards for that, but no: Because of RGPD and

by phh 6y ago

IMO the real issue is that Facebook wanted a commercially-usable product, and they thought Kaggle had all the safeguards for that, but no: Because of RGPD and friends, Facebook can't store those photos, even though their license are permissive, and respects Kaggle's rules. This clearly shows what Kaggle is: It is a way to get very cheap and high quality data-science work. It's not for hiring people, not for truly helping the research community, not for to help people learn. Nope, just cheap workers. It really feels like Facebook have there whole deepfake detection strategy here! They put like 2M$ on the table to solve an issue that will(?) plague their whole multi-billion platform.

Majestic121 6y ago
Do Facebook need to store the photos to use the model commercialy ? I would expect the algo + weight of the model to be enough, and that it is all fine RGPD-wise since you definitely can't identify people from the weight of a model trained with your picture.
- willcipriano 6y ago
  Presumably you'd want to retrain the model from time to time and that would require the original material.
mnky9800n 6y ago
I think this is the main takeaway. Kaggle, corporate sponsored hack-a-thons, etc. are all very cheap ways for companies to get a lot of work that is typically very expensive done for very cheap. Who benefits from these situations except for companies? Even if you get a job out of your kaggle contributions, what value was created for a company by kaggle in comparison to the value that was created for you?
- KarlKemp 6y ago
  We would arguably all benefit from a robust method to detect deepfakes, and from its availability, as open source, to all platforms large and small.
  - CodeWriter23 6y ago
    That’s a benefit to the collective and I believe mnky9800n is referring to the authors of the extremely beneficial work being exploited via inadequate compensation.
  - smolder 6y ago
    Perhaps for only about a week, until the GANs can be optimized to bypass detection. It is an arms race with no end in sight if we're being honest.
KarlKemp 6y ago
Kaggle very obviously is useful for the research community, has led to quite a few people getting jobs they otherwise wouldn’t, and (as I can personally attest) is an excellent resource for learning. It is also a way for companies to get work done. And maybe the competition format allows them to do so cheaper than people are comfortable with? But one does not exclude the other. Indeed, it’s the premise for Kaggle’s, and really any marketplace’s, success that it found a model that is beneficial for all participants. Competitions on Kaggle tend towards goals that are of universal value, of which the detection of deepfakes is one example. There are others that are rather far from any business case to support the idea that companies’ are looking to cheaply get something done, such as Google’s yearly Basketball score prediction competition (that comes with a relatively big pool of prizes). In fact just the low number of competitions would seem to speak against the idea that cheapness is a primary motivator here. Because getting one challenge done for, say, half of what it might cost in-house just doesn’t register on the scale of these companies. What I would assume to be the officially stated motivation, i. e. that it is a way to get a number of different approaches, possibly with ideas that wouldn’t come up within the far more homogeneous workforce of these companies, makes a lot more sense, intuitively.

by phh 6y ago

Majestic121 6y ago

Do Facebook need to store the photos to use the model commercialy ? I would expect the algo + weight of the model to be enough, and that it is all fine RGPD-wise since you definitely can't identify people from the weight of a model trained with your picture.

willcipriano 6y ago
Presumably you'd want to retrain the model from time to time and that would require the original material.

mnky9800n 6y ago

I think this is the main takeaway. Kaggle, corporate sponsored hack-a-thons, etc. are all very cheap ways for companies to get a lot of work that is typically very expensive done for very cheap. Who benefits from these situations except for companies? Even if you get a job out of your kaggle contributions, what value was created for a company by kaggle in comparison to the value that was created for you?

KarlKemp 6y ago
We would arguably all benefit from a robust method to detect deepfakes, and from its availability, as open source, to all platforms large and small.
- CodeWriter23 6y ago
  That’s a benefit to the collective and I believe mnky9800n is referring to the authors of the extremely beneficial work being exploited via inadequate compensation.
- smolder 6y ago
  Perhaps for only about a week, until the GANs can be optimized to bypass detection. It is an arms race with no end in sight if we're being honest.

KarlKemp 6y ago

Kaggle very obviously is useful for the research community, has led to quite a few people getting jobs they otherwise wouldn’t, and (as I can personally attest) is an excellent resource for learning. It is also a way for companies to get work done. And maybe the competition format allows them to do so cheaper than people are comfortable with? But one does not exclude the other. Indeed, it’s the premise for Kaggle’s, and really any marketplace’s, success that it found a model that is beneficial for all participants. Competitions on Kaggle tend towards goals that are of universal value, of which the detection of deepfakes is one example. There are others that are rather far from any business case to support the idea that companies’ are looking to cheaply get something done, such as Google’s yearly Basketball score prediction competition (that comes with a relatively big pool of prizes). In fact just the low number of competitions would seem to speak against the idea that cheapness is a primary motivator here. Because getting one challenge done for, say, half of what it might cost in-house just doesn’t register on the scale of these companies. What I would assume to be the officially stated motivation, i. e. that it is a way to get a number of different approaches, possibly with ideas that wouldn’t come up within the far more homogeneous workforce of these companies, makes a lot more sense, intuitively.