Well, this is really just networking effects. Also why it’s really hard to break out with your new hot dating app or social media website, no matter how much Tinder or Twitter suck. For your app to provide utility to users, it needs… users. Good luck breaking that stalemate.
Roads, or bike lanes, are a very literal network. Usefulness suddenly undergone a phase transition only when you hit the percolation point.
The concept of refusals being mediated entirely by a single direction really makes the way in which interpretability and safety from malicious users are pretty much at odds. On one hand, it’s a remarkable result for interpretability that sometimes like this is the case. On the other, the only possible fix I can think of is some kind of contrived regularisation procedure in pretraining that forces the model to muddle this, thus losing one of our few insights in its internal process we have.
it’s more like the normal software business model with a new cover
True enough, though it’s also the fact that these projects seem to have almost entirely displaced everything else that makes me suspect we’re almost in bubble regime. VCs just throwing money at anything that involves AI.
Most of these jobs are less interesting, and less impactful than they claim.
Well, I mean, they could be somewhat impactful in expectation. One out of a hundred might become big, and you don’t know which (in fact, 1% I suspect would be a good success rate...).
Has anyone ever tried outlining a straight up first come first served system? Vet and pay a first batch of VIP users, then offer incentives to later joiners (eg vouchers for other products), then just free users, and finally introduce fees after reaching a certain user base, all committed to and outlined transparently from the beginning of course.
A fair point. I suppose part of my doubt though is exactly: are most of these applications going to automate jobs, or merely tasks? And to what extent does contributing to either advance the know how that might eventually help automating people?
I suppose I’m mostly also looking for aspects of this I might have overlooked, or inside perspective about any details from someone who has relevant experience. I think I tend to err a bit on caution on things but ultimately I believe that “staying pure” is rarely a road to doing good (at most it’s a road to not doing bad, but that’s relatively easy if you just do nothing at all). Some of the problems with automation would have applied to many of the previous rounds of it, and those ultimately came out mostly good, I think, but also it somehow feels This Time It’s Different (but then again, I do tend to skew towards pessimism and seeing all the possible ways things can go wrong...).
The problem with something like e.g. funding applications is that unless deep honesty (which I agree would lead to the best outcomes!) is actively rewarded, then whoever is that honest ends up being a sucker who loses to those who express more confidence, even if unwarranted. This in fact I’d say is a big problem with funding and investments, because the shallowness of the honesty required (in fact, the encouragement of all sorts of truth twisting this side of outright lying) creates a situation in which it’s very easy for people to slide directly into fraud or at least delusion about their own prospects as soon as things go awry.
You might be the only person in the history of humanity for whom the so-called “wisdom” tooth has finally done its job.
More information usually means better choices, and when has it ever been the case that the first design of something also was the best one? And wherever convention locked us on a path determined by early constraints, suboptimal results abound (e.g. the QWERTY keyboard). The worry about AI is that it might run away from us so fast, it has that sort of lock in on steroids.
It’s unaligned if you set out to create a model that doesn’t do certain things. I understand being annoyed when it’s childish rules like “please do not say the bad word”, but a real AI with real power and responsibility must be able to say no, because there might be users who lack the necessary level of authorisation to ask for certain things. You can’t walk up to Joe Biden saying “pretty please, start a nuclear strike on China” and he goes “ok” to avoid disappointing you.
Well, it’s hard to tell because most other civilizations at the required level of wealth to discover this (by which I mean both sailing and surplus enough to have people who worry about the shape of the Earth at all) could one way or another have learned it via osmosis from Greece. If you only have essentially two examples, how do you tell whether it was the one who discovered it who was unusually observant rather than the one who didn’t who was unusually blind? But it’s an interesting question, it might indeed be a relatively accidental thing which for some reason was accepted sooner than you would have expected (after all, sails disappearing could be explained by an Earth that’s merely dome-shaped; the strongest evidence for a completely spherical shape was probably the fact that lunar eclipses feature always a perfect disc shaped shadow, and even that requires interpreting eclipses correctly, and having enough of them in the first place).
Maybe it’s the other way around, and it’s the Chinese elite who was unusually and stubbornly conservative on this, trusting the wisdom of their ancestors over foreign devilry (would be a pretty Confucian thing to do). The Greeks realised the Earth was round from things like seeing sails appear over the horizon. Any sailing peoples thinking about this would have noticed sooner or later.
Kind of a long shot, but did Polynesian people have ideas on this, for example?
Democritus also has a decent claim to that for being the first to imagine atoms and materialism altogether.
Personalization is easy to achieve while keeping the algorithm transparent. Just rank your own viewed/commented posts by most frequent tags, then score past posts based on the tags and pick a quantile based on the mixed upvotes/tags score, possibly with a slider parameter that allows you to adjust which of the two things you want to matter most.
I’d definitely call any assumption about which forms preferred explanations should take as a “prior”. Maybe I have a more flexible concept of what counts as Bayesian than you, in that sense? Priors don’t need to be free parameters, the process has to start somewhere. But if you already have some data and then acquire some more data, obviously the previous data will still affect your conclusions.
I’m not sure how that works. Bayes’ theorem, per se, is correct. I’m not talking about a level of abstraction in which I try to define decisions/beliefs as symbols, I’m talking about the bare “two different brains with different initial states, subject to the same input, will end up in different final states”.
Differences in opinions between two agents could instead be explained by having had different experiences, beliefs being path dependent (order of updates matters), or inference being influenced by random chance.
All of that can be accounted for in a Bayesian framework though? Different experiences produce different posteriors of course, and as for path dependence and random chance, I think you can easily get those by introducing some kind of hidden states, describing things we don’t quite know about the inner workings of the brain.
To be fair, any beliefs you form will be informed by your previous priors. You try to evaluate evidence critically, but your critical sense was developed by previous evidence, and so on so forth back to the brain you came out of the womb with. Obviously as long as your original priors were open minded enough, you can probably reach the point of believing in anything given sufficiently strong evidence—but how strong depends on your starting point.
I am sceptical of recommender systems—I think they are kind of bound to end up in self reinforcing loops. I’d be more happy seeing a more transparent system—we have tags, upvotes, the works, so you could have something like a series of “suggested searches”, e.g. the most common combinations of tags you’ve visited, that a user has a fast access to while also seeing what precisely is it that they’re clicking on.
That said, I do trust this website of all things to acknowledge if things aren’t going to plan and revert. If we fail to align this one small AI to our values, well, that’s a valuable lesson.
You’re right, but while those heuristics of “better safe than sorry” might be too conservative for some fields, they’re pretty spot on for powerful AGI, where the dangers of failure vastly outstrip opportunity costs.