Just because someone understands variational autoencoders doesn't mean they have a clue about how the field of AI will look like 5 years from now, and it certainly doesn't mean they can anticipate the societal and political impacts of those technologies any better than the average (intelligent) Joe.

I'm not comfortable with the idea that philosophy is intrinsically nebulous and poor quality, any more than observing amateur footballers being not very good would lead you to assume that there can be no such thing as a good footballer; of course there are, but there are a damn sight rarer than your local kids having a kick around for fun.

AI philosophy wasn't a very popular / widely-researched field, though, until recently, when AI ethics (ethics is considered part of philosophy) — and a specific subfield of that called "alignment research" — became something that a good number of philosophers became very concerned with.

Now there are many AI philosophers, employed not just in academia, but also in think-tank-like arms of AI technology companies like OpenAI (mostly because the AI tech companies know they're perceived as being irresponsible with how quickly they're iterating toward more-powerful AI, and so use employing AI philosophers as something like carbon credits to offset that perception.)

There is a lot of very good work done in AI philosophy; with many of the insights from alignment research specifically, being incorporated into the work that the AI tech companies are doing.

But none of this really "surfaces" in articles about AI that you might see floating about, because alignment research is really high-context — it's not really something you can write a fluff piece about; it's all stuff that requires knowledge of both philosophical (e.g. linguistics, decision theory) and computer science concepts to understand. Instead, all the normal articles about AI philosophy, are written by people doing amateur AI philosophy — usually resulting in them badly retreading the same ground that was already thoroughly explored in the 1970s.

There are many people who write deep nuanced articles too, but since they have lower virality, we are usually exposed to the first type of shallower articles and it might seem that there are more of them.

On some levels this feels like our Prometheus moment, where we are able to create perhaps new forms of intelligence but we cannot at this point say what it will look like or what impact it will have, and we might certainly be ill equipped to handle the impact on society.

When I don't know something, I think of different possibilities, guess at probabilities, learn more, revise what's possible and not, revise probabilities, etc. Call it circling around the topic.

Other people, like this author, seem to pick one view and assert it as certain and incontrovertible, and wait for rebuttals to debate X or Y or Z externally rather than with themselves. Cann it a pinball style, relying on exterior factors to redirect.

My intuition is the same as the author, I feel like we are at a limit (how much data can we really feed the models?), and most llm (except gpt4 apparently) are often too wrong to be useful (unless you need intern-level work). I think the next step is deepening the LLMs and specialize them (at least for code generation).

we know exactly how they work. all they do is use statistical probabilities with limited randomization to synthesize new outputs from massive samples of inputs.

essentially a machine for plagiarizing spam.

gpt, and its general approach, will never have much value other than a few limited fields, because it can never produce anything that wasn't in its original input set and it is incapable of reasoning and analysis.

the world is already drowning is junk information. gpt has very limited commercial applicability because it is the opposite of what people actually want and need, which is a way to filter and analyze the massive pile of information they already have. gpt does the opposite and creates more junk information based on an analysis free synthesis of existing junk.

really inspiring take here. this is like saying fentanyl is a great product because people really like it. but i think you are wrong. these apps are mostly used by children and young adults who will grow out of them and turn away from using digital experiences in general because they are addictive and harmful. that is not a good thing for the industry in the long term.

https://wallaroomedia.com/blog/social-media/tiktok-statistic...

Of course it is not good for anything and in my view it should be forbidden outright, but plenty over 18 want to consume this crap and don’t grow out of it until middle aged.

But also ‘news’ people like sensationalist copy/paste news and they don’t grow out of that. Not sure how many people watch fox, but I would be surprised if most of that is not already written by AI.

Almost none, they all have a specific task that a specifically trained bot could do way cheaper.

The counter to this is AI is so cheap and easy people lazily use it for everything even though it is using .1% of its training data.

i dont think thats fair to say tbh. clearly language has alot of patterns in it. so it should be possible to come up with a network that finds them. if the loss converges to something reasonable and the amount of training data is greater than what the network is able to memorize, then the only possibility is that the network learned whatever general patterns the data happens to have.

We don't know. That's the place the author started before the article. So he wrote the article. Why oppress attempts at understanding the unknown?

There is no wisdom in wilfully darkening the mind. We have to make stabs in the dark to get some where.