bhntr3

Anyone interested could also take a look at Popper (https://github.com/logic-and-learning-lab/Popper) or this overview of the first 30 years of ILP (https://arxiv.org/abs/2008.07912)

Many people are very resistant to the idea that their particular work can have a negative impact or that they should take responsibility for that. See Yan Lecun quitting Twitter (https://syncedreview.com/2020/06/30/yann-lecun-quits-twitter...)

Other people are very aware of the dangers of their work. But, when the money gets big enough, they take their concerns to the bank and their therapist. See Sam Altman's concerns about the dangers of machine intelligence before he invested in OpenAI (https://blog.samaltman.com/machine-intelligence-part-1) Contrast that with his decision to become the CEO, take the company private and license GPT-3 exclusively to Microsoft. (https://www.technologyreview.com/2020/02/17/844721/ai-openai...) He had reasons. He posts here. He might defend himself. But to me it seems like the kind of moral drift I've seen happen when people in silicon valley have to make hard choices about money and power.

There are also applications of ML that are generally safe and can be of benefit to society. See the many medical uses including cancer detection.(https://www.nature.com/articles/d41586-020-00847-2) Most of the work being done to expose the risks and biases of ML is being done by researchers who are at least somewhat within the field. In my math and computer science program, two and a half of the 25 students are doing their thesis in safe ML. (I'm giving myself a half because I'm working on logic based ML.) I don't think it's fair to believe that every person working in ML is participating in something negative for society.

Ultimately, I think we need some reasonable regulation and a lot more funding for research into safe ML. Corporations and governments want ML for purposes that can be unethical. Unfortunately they also control a lot of the research grants. So they have a disincentive to fund AI ethics or safe ML over pushing the boundaries of what ML can accomplish.

Finally, I think many engineers would like their work to be positive for society. Unfortunately, with what we know now, a lot of the edge cases we run into are unfixable. When Google Photos started classifying black people as gorillas, Google just removed primates from the search terms. Years later, they hadn't fixed it. (https://www.wired.com/story/when-it-comes-to-gorillas-google...) I'm sure most engineers on the project knew that was a hack. When faced with an unfixable issue like that, the engineer either tries to get the company to stop using ML for that problem, compartmentalizes and ignores the issue, or they quit. Where do you draw the ethical line? It's good to hold people accountable but it's unrealistic to expect that to solve the problem.

This isn't how science works though. Quoting the wikipedia page for Thomas Kuhn's "The Structure of Scientific Revolutions" (https://en.wikipedia.org/wiki/The_Structure_of_Scientific_Re...):

"Kuhn challenged the then prevailing view of progress in science in which scientific progress was viewed as "development-by-accumulation" of accepted facts and theories. Kuhn argued for an episodic model in which periods of conceptual continuity where there is cumulative progress, which Kuhn referred to as periods of "normal science", were interrupted by periods of revolutionary science."

I think this is the accepted model in the philosophy of science since the 1970s. That's why I find this argument about AI so strange, especially when it comes from respected science writers.

The idea that accumulated progress along the current path is insufficient for a breakthrough like AGI is almost obviously true. Your second point is important here. Most researchers aren't concerned with AGI because incremental ML and AI research is interesting and useful in its own right.

We can't predict when the next paradigm shift in AI will occur. So it's a bit absurd to be optimistic or skeptical. When that shift happens we don't know if it will catapult us straight to AGI or be another stepping stone on a potentially infinite series of breakthroughs that never reaches AGI. To think of it any other way is contrary to what we know about how science works. I find it odd how much ink is being spent on this question by journalists.

My most upvoted comment on this website was discussing this exact scenario. https://news.ycombinator.com/item?id=23655487

Could you perhaps clarify the generalization you're making about me and people like me so I can understand it?

I always end up linking this silly blog post. https://medium.com/@webseanhickey/the-evolution-of-a-softwar... It sums up the seniority growth cycle better than I can in words.