I finished my master's thesis in Inductive Logic Programming recently at Oxford. I'd say that the field has continued improving since Aleph, which was written in the late 90s.
This isn't an anti-AGI argument and it doesn't disprove humans. Humans have the same problem. It's harder to write a program to do a thing than it is to just do the thing.
It's appealing to think we can just make a program that learns programs and then use that to learn to do anything computable. But this is a well studied field and it turns out that when you generalize a learning problem that way you make the learning problem a lot harder.
The space of programs that could possibly.identify dogs in images is much much much larger than the space of images that contain dogs. The images are bounded by the number of pixels in the image times the color depth. What is the space of programs bounded by? 10TB? That's roughly 256^10000000000 programs. That's just a stupidly large number.
Obviously not every 10TB string is a valid program. You can reduce that number. But what current research in program synthesis tells us is that you can't reduce it as much as you might hope.
So the point is that, just like for humans, it's easier to learn to do a thing than it is to learn to write a program to do a thing.
This was part of the lawsuit. They communicated with each other about offers made to each other's employees and agreed not to counter-offer above the initial offer.
That link is a wild ride. I expected a design blog. Instead I got a communist indictment of startup culture through the lens of the commodification of illustration.
I felt like the part that wasn't in line with Kuhn was the idea that there was something wrong with a field if incremental improvement couldn't lead to a breakthrough like AGI. You're right. He's arguing Kuhn's point. But he seems to use it to conclude that machine learning is a dead end when it comes to AGI. Further, he seems to think this means AGI won't happen any time soon.
But, if I'm not misinterpreting Kuhn again, knowing that a revolution is necessary to overturn the current dogma (which I would argue is deep learning) doesn't tell us anything about when the revolution will occur. It could be tomorrow or 50 years from now or never. So, specifically, it doesn't tell us anything about machine learning in general, whether AGI is possible, or when AGI will happen.
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.
I believe they are disagreeing whether "engineers working in this space are out to lunch" and since I have been "an engineer working in this space" I was asking for more clarification about what it meant to be "out to lunch".
"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.
I love the implication that there's this shadow company, Fronk. Seemingly defunct, they're actually thriving secretly behind the facade of a failed startup.
Every marketing manager has engaged them privately to boost their numbers. Every developer secretly works for them on the side.
But no one anywhere ever talks about it until one day a former consultant notices an expired NDA.
>Is it worth reading them already or does it feel unfinished?
Mistborn is a complete trilogy although he continues to publish other novels set in the same world.
Way of Kings is ongoing. It's on book four now. Each book is over a thousand pages so there's a lot there. I don't think it's a problem to start. Books 1-3 are great and stand alone pretty well. It's started to drag with book 4 in my opinion. Like so many other huge epic fantasies, it has too many characters, too many plotlines, too huge of a world, and it's difficult to maintain the epic feel with all that sprawl. I'm worried for book 5.
> Mistborn and The Way of Kings take place in the same universe?
They take place in the same universe (literally) but they are on different worlds. So they don't have anything (much?) to do with each other (yet?)
Maturity (or seniority) is a form of growth and it has stages. I would call this article Stage 2: Do the approved thing.
It's an important stage. But people who stay in this stage too long become intolerable technical architects who yammer away about best practices and high level company goals, produce technical documents no one reads and plan team building events no one wants to attend.
To me stage 3 involves thinking critically about tradeoffs. Applying best practices in relevant contexts and rejecting them in others is a good example.
This essay seems to have been published 20 years ago. In the modern version, the experimentalists use terms like "deep learning" and "neural nets" and the theorists use terms like "fuck all this deep learning bullshit"
Arthur's pass is where I had my best Kea encounter! I made it to the top of Avalanche Peak. The hike just about killed me. As I was sitting there admiring the view and trying to recover, a kea flew in out of nowhere and just hung out with me for about five minutes. It really felt like he was making sure I was ok. But he could have been waiting for me to pass out so he could chew up my daypack.
I knew enough not to feed him. Kea are trolls. You don't feed the trolls.
Kea are very playful. They're also annoyingly naughty. But they're beautiful and I love them.
Here's a story about Kea I heard from a park ranger when I was in New Zealand. The rangers have traps for non-native predators scattered throughout the park. The traps are basically a big mouse trap in a box (I think.) When going around to reset the traps they found that someone was setting them off with nothing inside. They thought it was hikers. So they set up some cameras.
Instead, it was the keas. They would come to the trap with a stick in their beaks. Then they'd use the stick to trigger the trap. It made a loud boom when it closed. They'd laugh and move to the next trap. They actually have a distinctive laugh (https://www.youtube.com/watch?v=N37rN29nUIc)
I think they're one of those species, like octopi or maybe elephants, that are a lot smarter than we understand or know how to quantify. It's cool that they're getting the recognition they deserve. Even though they're little bastards.
When no one wants to make a suggestion, propose the worst possible one you can come up with. People will race to improve it because, while they don't know what they want, they know it's not THAT.
Plus their idea can't be worse than mine. So I've stolen all the embarrassment and disdain for myself.
If you host user-generated content, you will have to decide on community standards and do your best to enforce them. This will be expensive. You are legally obligated to police things like terrorism, child pornography, money laundering and even DMCA violations. You are morally obligated to do more than just that, not just by your conscience, but also by the global zeitgeist.
Of course some absurdity lies in making believe your corporation has a conscience. You may have a conscience. But ultimately, no matter what corporate culture you instill, your corporation is a psychopath.
So your corporation will be Dexter and you do your best to operate it like it cares about people and issues and is a good citizen (since after all, it is legally a person.) If you do this poorly, it is revealed to have no soul and punished. If you do it well, you get a brand.
If covering for a psychopath whose only motivation is money and whose opinions are the amalgamation of half-formed thoughts from hundreds or thousands of different individuals isn't your idea of a good time, then maybe running a business isn't the best idea.
Well, you can't undelete data you didn't collect. So I think there's this natural tendency toward omnivorous data collection in every tech company.
Then we rationalize it by telling ourselves that we use it ethically. It's almost always true . . . except when it's not. If 99% of the time the data is used ethically, it's easy to write off that 1% even when the 1% is all that matters.
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)