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> It's not the job of the AI researcher to solve "social biases" in every field, it's their job to build the AI. No, this is only technically correct but actua

> It's not the job of the AI researcher to solve "social biases" in every field, it's their job to build the AI. No, this is only technically correct but actually wrong. In the example, they did in fact fail to build the AI, as is their job. "Recognize white faces" is a lame research goal, "recognize human faces" is the real thing. So if somebody builds an AI system that fails on out-of-sample data, then says that had they tried to do it properly they would have succeeded, that's a pretty lame excuse for a poor AI system. They didn't even do their job in the narrow sense that you're using, you don't even need to consider the "social biases" or whatever, it's just a system that didn't work. In fact, many years into this research program, "focussing on his task and correcting it" (working on all human faces) is still not done given the current performance of these systems, but they are quite sure it can be done if they tried.

deep_etcetera 6y ago
Imo these are researchers. Their job was to validate their algorithm for doing generative super-resolution on a dataset, they chose the largest and most well-known dataset, it worked reasonably well on their dataset. The model itself is not productive ready for at least the reason that the dataset is not representative. This is ubiquitous in ML papers, they validate their idea on not completely realistic but widely available datasets. The outcome is a piece of knowledge about the behavior on that dataset not a product.
- joshuamorton 6y ago
  The point that many are making is that this is a myopic view of research. Why is the generic goal to optimize against some particular dataset? That ends up being a narrow and unhelpful goal. As someone put on Twitter : we should be rethinking the meta learning algorithm of the ML academic sphere, and a leader like Yann is the kind of person who should be spearheading that.
  - Decker87 6y ago
    It is true that better datasets are sorely needed, but what would you suggest Yann and other researchers do about it in this situation?
    - joshuamorton 6y ago
      Proactively doing the things that the ethicists are calling for. Use Model cards for any model. Actively think about and discuss the limitations the model, intended uses, etc. That goes for everyone. For someone like Yann specifically? Publicly state that the ML scene is optimizing in a myopic way, and invest in doing so less myopically. For example, I think the translation space has a clear goal and the right goal and is making strides in improving language models in many useful ways. Ultimately, if there aren't ethical and useful ways to apply facial recognition, leaders should be steering people away from those research topics.
6gvONxR4sf7o 6y ago
> No, this is only technically correct but actually wrong. In the example, they did in fact fail to build the AI, as is their job. "Recognize white faces" is a lame research goal, "recognize human faces" is the real thing. The researcher's job is to make progress towards the research goal. Progress that doesn't solve the problem is still progress. Nobody's saying that the PULSE authors have solved their research goal of generating white faces. It's why papers have a "discussion and future work" section, which touched on the issue in the first version of their paper. It's why in their revised paper, they added a full discussion of bias and the issues in the current model. They did their job, but didn't solve the whole problem, which is too high a bar for any researcher. Science is incremental. That's how I interpreted LeCun's distinction between the researcher's job and the product builder's job.

deep_etcetera 6y ago

Imo these are researchers. Their job was to validate their algorithm for doing generative super-resolution on a dataset, they chose the largest and most well-known dataset, it worked reasonably well on their dataset. The model itself is not productive ready for at least the reason that the dataset is not representative. This is ubiquitous in ML papers, they validate their idea on not completely realistic but widely available datasets. The outcome is a piece of knowledge about the behavior on that dataset not a product.

joshuamorton 6y ago
The point that many are making is that this is a myopic view of research. Why is the generic goal to optimize against some particular dataset? That ends up being a narrow and unhelpful goal. As someone put on Twitter : we should be rethinking the meta learning algorithm of the ML academic sphere, and a leader like Yann is the kind of person who should be spearheading that.
- Decker87 6y ago
  It is true that better datasets are sorely needed, but what would you suggest Yann and other researchers do about it in this situation?
  - joshuamorton 6y ago
    Proactively doing the things that the ethicists are calling for. Use Model cards for any model. Actively think about and discuss the limitations the model, intended uses, etc. That goes for everyone. For someone like Yann specifically? Publicly state that the ML scene is optimizing in a myopic way, and invest in doing so less myopically. For example, I think the translation space has a clear goal and the right goal and is making strides in improving language models in many useful ways. Ultimately, if there aren't ethical and useful ways to apply facial recognition, leaders should be steering people away from those research topics.

6gvONxR4sf7o 6y ago

> No, this is only technically correct but actually wrong. In the example, they did in fact fail to build the AI, as is their job. "Recognize white faces" is a lame research goal, "recognize human faces" is the real thing. The researcher's job is to make progress towards the research goal. Progress that doesn't solve the problem is still progress. Nobody's saying that the PULSE authors have solved their research goal of generating white faces. It's why papers have a "discussion and future work" section, which touched on the issue in the first version of their paper. It's why in their revised paper, they added a full discussion of bias and the issues in the current model. They did their job, but didn't solve the whole problem, which is too high a bar for any researcher. Science is incremental. That's how I interpreted LeCun's distinction between the researcher's job and the product builder's job.