PS "Do the work" is a creepy phrase that is popping up everywhere in SocJus. I recommend describing the "work" that needs "doing" instead of just saying "the work" need be "done".
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.
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.
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.
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.
It has real world consequences that can drastically damage communities.
The current nightmare that is cyber security is caused by developers who do not understand that with great power comes great responsibility.
The culture of software “engineering” and development is fundamentally broken and based entirely on “its not my problem if someone else gets hurt, I just build things.”
There’s literally not a single other industry where this level of greed and willful neglect is acceptable.
Of course, this would be reflected in the AI community as well.
Blaming greed of engineers for security problems? Seriously?! That is like railing against EMTs as being responsible for the Coronavirus because they wanted to get rich without working hard. It conflates so many different areas and roles that it isn't even coherent logic and is nonsensical.
If you don’t understand that engineering is the fundamental bedrock of IT and the lack of security application during the engineering SDLC is a consistent failure then I don’t know what to tell you except maybe to gain more experience in software engineering and read more about data breaches.
Cyber security: https://en.m.wikipedia.org/wiki/Computer_security
But for arguments sake let’s just stick to network, application, mobile and IOT security.
- Why is MFA not on by default?
- How many devs have prod credentials published to Github.com right now?
- How many unsecured IOT devices are there?
- Why is email security such a dumpster fire?
- How many companies have an SSDLC?
- How many companies require separation of duties and approvals before a dev publishes another AWS bucket or some other unprotected data store to the web?
Go ahead and blame business but they don’t know jack about software engineering. We determine which corners to cut as opposed to stating to business that security is just a part of doing engineering. We’re the one’s who decide and thus it’s our responsibility despite denials of people like yourself.
Abstracting away the ethical issues of your (elite) employment is a personal choice that is anything but objectively neutral.
Does this apply to doctors taking care a murderer, rapist, [insert felony here]?
Should this apply to the doctor of Yann LeCun?
Should these "personal choices" be valid, allowing the doctors to judge YOU, negating a cure?
You can extend 'doctor' to other professions too, like journalists.
PS: all those professions require to be objectively neutral.
Although while everyone intuitively understands the load bearing capacity of a footbridge, not many understand the capabilities of ML models. So perhaps better advertising the capabilities of AI would help inform decisions.
On the other hand, nothing an engineer or scientist can do will stop the Chinese government from using their technology to predict and suppress dissidents and minorities.
OT, but shall we stop throwing casual references to China as the example of everything bad that can happen?
On the other hand, China is in a conflict with the Uighur population of Xinjiang. As far as I understand it, there are elements both of cultural clash (China doesn't like religion in general, and the Uighurs are muslim) and the Uighurs' reaction to an influx of ethnically chinese population in the region. Anyway, Uighurs engaged in terrorism: this BBC article seems pretty balanced and lists a number of terror attacks by Uighurs as well as the repressive actions by China: https://www.bbc.com/news/world-asia-china-26414014
In this context, I think that China might be using AI not to "round up" Uighurs, but as an intelligence measure to prevent more terror attacks. Similar to how the US intelligence is (I have no doubts about it) profiling muslims and middle eastern immigrants- not because it has anything against those groups per se, but because it has reasons to believe terrorists might hide in their ranks.
I agree, but then it's telling how in an abstract argument about the potential misuse of technology seems natural to throw in an offhand accusation against a specific country. I am pretty worried by how quickly China has become the new boogeyman- everyone thinks it's perfectly reasonable to display anger towards a huge country that only a few years ago was seen, despite its obvious issues and shortcomings, as successful and dynamic.
And I remember how it started: when a mainstream, trusted news outlet reported about the existence of "spy chips" in hardware sold by Chinese companies to the US "according to extensive interviews with government and corporate sources". Which later turned out to be fake news. It gives pause for thought.
https://www.bloomberg.com/news/features/2018-10-04/the-big-h...
That is exactly what I claimed they were potentially using it for.
I acknowledge that I am very much biased against China as I live in a country within it's sphere of influence and have friends in HK.
But in any discussion of using AI for unethical purposes, China is the ur-example, as Nazi Germany is to facism: an authoritarian government with a history of tech surveillance, censorship and media control, and minority oppression, and the tech to back it up.
If they don't like that, maybe they should stop using technology to oppress their own citizens.
I think he has some responsibility to at least acknowledge the complexity of this issue in such cases. Not speaking to the public is also a option if he doesn't like the leader role, so him deleting twitter is a completely ok thing in my eyes.
1) The model uses biased data when it could have used unbiased data.
2) The model uses biased data when no unbiased data exists and is incapable of correcting for these discrepancies.
The first case is most clearly and engineering/implementation issue, the second is obviously not. Biased data is a known failure case of ML, its the responsibility of researchers to design for it.
"'Once the rockets are up, who cares where they come down? That's not my department' says Wernher von Braun"