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I don't think most of ML papers are to be labeled science. But it's some kind of scholarship. Maybe engineering, maybe just reports of whatever people worked on
by bonoboTP 3y ago
I don't think most of ML papers are to be labeled science. But it's some kind of scholarship. Maybe engineering, maybe just reports of whatever people worked on in the last 6 months and here's what happened.
I don't know exactly what kind of knowledge you want. Cybernetics and AI from the 40s and 50s onwards didn't aim at learning the laws of intelligence like the laws of gravitation, they were practical goals of building a thing. Like the Manhattan project wasn't about leaning stuff but about going kaboom.
Chomsky complains the same, that ChatGPT teaches us nothing about human language at all, it's not science, it's not advancing linguistics, but sure these things are good for captioning videos for those hard of hearing or translating between languages. And I'm like dude, nobody is stoked because they think we learned something about human language or the nature of intelligence. People are stoked because the computer can talk back at them.
And again I get where you are coming from, I see the same picture but see the rabbit instead of the duck. I see it as very important and consequential that these things work so well. I'm very big on eyeballing. I don't need to measure the voltages under the hood and stare at the speedometer to tell whether a car runs or not, may it run on electricity, natural gas or petrol, the wheels are rolling and we get from A to B.
Science isn't the outermost frame of existence, and has only been here for a few centuries. Journals and professional academia rat race of the shape as today, publish and perish etc, only since like half a century. The Romans built all their infrastructure without journals and h-indexes.
I get it, the hype often goes too far. But I'm now contrarian about the contrarianism. Denying that translation or transcription or image segmentation etc don't work is an increasingly silly hill to die on. So many have moved on to say stuff like yeah it works, but it's biased, it is wasteful and has a big carbon footprint, or can fail suddenly and unexpectedly out of distribution. But denial doesn't help.
I mean, the Internet wasn't a scientific discovery but TCP/IP, Ethernet and the Web still revolutionized society. Now maybe my grandma would deny this and say she gets by just fine gossiping with the neighbors and never using the Internet (though even her soap operas are delivered via IPTV). But just because it's not science, just "building stuff" it's still impactful. Don't be distracted by whether it fits a definition of science or not, that's a separate question from whether the thing works, by the definition of whether it gets jobs done that people want to get done and will pay for.
- YeGoblynQueenne 3y agoAnd I get where you're coming from but we don't know when something is working until science. See the recent frontpage article on LK-99. If you got jets and airliners, sure you can look up at the sky and see the thing working. But if you're at the stage where people are jumping off hills with makeshift wings tied to their arms, then you need some other way to know what works, and which way to keep digging. That other way, that is science.
- YeGoblynQueenne 3y ago@bonoboTP: This is a bit late now and you'll probably not see it, but it occurred to me that we are talking about different things, throughout. When you're talking about progress, you mean neural nets now work better than before in classifying images, recognising speech, translating text, etc. What I mean on the other hand is progress towards artificial intelligence. I don't have any difficulty accepting that image classifiers work better now than 20 years ago (although see my comments about why) and we perhaps don't necessarily need science to understand this. What we do need science to understand is whether that kind of progress is progress towards artificial intelligence. And I would very much like to say that that is irrelevant because personally I don't care much about creating artificial intelligence, but a) that's the name of the field and b) many people in machine learning seem convinced that artificial intelligence is what they are trying to build (see e.g. how "the AI" became a common way to refer to neural net systems in recent years, even appearing in scholarly articles by people who should really know better, but, unfortunately, don't seem to). In other words, is progress in classification at all connected to progress towards artificial intelligence? That one is a scientific question. The problem of course is that we have plenty of metrics to measure the performance of classifiers, but no good tests to detect the presence of anything like intelligence. I've thought of writing a paper to propose some but every time I'm like, nah, that's not what I'm working on, let someone else do it.