Richard Stallman Talks Red Hat, AI and Ethical Software Licenses at GNU Birthday

IMHO RMS’s socially abrasive attitude and communication style, and the overall unapproachability of the FSF, goes a way toward ensuring that anyone that can stomach advocating for “the cause” is similarly abrasive. This is to the point where some people conflate being a jackass with supporting Free Software as a movement or even as a concept. Like, aspects of some dude called Richard’s personality and even his proclivities wrt how he chooses to go about his computing life are cargo-culled by this decentralised group of fans. You see it here all the time. Someone will be talking about Free Software, and use this as a license to be an asshole to people.

There is in my experience a large silent majority that simply won’t engage with these conversations, not because they don’t see a legitimate place for the Free Software movement, but because the culture is so unnecessarily toxic and unapproachable on account of the people that it puts on a pedestal. It certainly puts others off from the actual principles of the movement altogether.

Time was, computing was for entirely socially adjusted - primarily - women. I as much as possible try to see the period of dominant industry voices being overrun with socially awkward asshole nerds to be a blip rather than an origin story or an ongoing necessity. At this stage I think we are at the point where continuing to give these people a social platform just because of some sense of prestige is not the way to go.

What I'm saying is that "intelligence" didn't mean "guessing the best answer by averaging many known, externally generated answers" before neural network approach became popular. Intelligence was always about finding good solutions to problems.

In rare cases doing nothing is a good solution to a problem, in less rare cases, statistically guessing is a good solution, but it still doesn't answer the question of how to find a good solution. No amount of improving the accuracy of statistical guess will answer this question because what we need is a proof that the claim is correct, but the guess is the opposite of a proof -- it's asking you to take the claim on faith.

In practical terms, compare these two scenarios:

1. A neural net AI is diagnosing breast cancer in a patient. Suppose, it was a male patient (a very rare occurrence, that's likely to fail statistically guessing AI). AI fails to find cancer and the patient dies.

2. A radiologist diagnosing breast cancer in a patient. Same circumstances as before. The radiologist fails to identify cancer, the patient subsequently dies.

In the first case, we don't even expect the AI to be correct. Nobody's taking the blame, because, well... we've constructed this AI to be guessing statistically. It couldn't have enough data to guess reliably because in order to do so it needed to see more breast cancer in male patients than we have data for.

In the case of a radiologist, we expect the radiologist to understand that they are treating an exceptional case. We will blame them, and probably sue for malpractice because we expect them to be able to reason, to be able to explain why what they see "makes sense" and why their response to what they saw "made sense". We will hold them responsible for failing this patient.

In common understanding of the word "intelligence", the radiologist is intelligent (or, at least, we expect them to be), while neural net isn't.

Just 12 months ago if you polled here you would find much more naive opinions. But now we are learning fast to evaluate AI and human work critically. We have had our hundred hours of AI play and all tried to make it stumble, then to make it work, and in the end we are wiser for it.

Regular citizens even know not to trust the bunch of benchmark scores every new model shoves in our eyes, like hardened ML researchers. And basic AI concepts are infiltrating and getting root in the public - model, parameters, tokenisation, datasets, fine-tuning, vector search, and their complex interplay.