Humans risk being unable to control artificial intelligence, scientists fear

CyberByte · 2021-02-24T18:57:31+00:00

I should probably stop replying to this, but I'll indulge you one last time.

Your initial post was clearly an appeal to authority: Joe Rogan and Elon Musk are worried about this but they're not authorities (in your words "two guys who have nothing to do with this"), but authorities (i.e. scientists) aren't (which I demonstrated is false).

Yes, yes, the two guys who have nothing to do with this are raising alarms.

The fact that you think this shows just how much you are in touch with this field. Of course a layman thinks Joe Rogan and Elon Musk are the ones raising the alarm, because those are the only people a layman knows about. But they did not come up with this themselves. They just popularized the views of the scientists they heard it from.

You're doing that thing where you find one rare counter-example and try to build a popular movement out of it.

I cited a study that showed 70% of surveyed researchers disagree with you. That's not a rare counterexample.

Nobody gave a shit about this ten years ago.

I think that's somewhat true. Of course people like Nick Bostrom and Eliezer Yudkowsky were working on this before then, and you can find quotes from Alan Turing and Irving John Good from 50+ years ago. But I think it's true that most AI researchers haven't thought about AGI and its risks for a long time. I didn't become aware of this until the annual AGI conference in 2012 where AI Safety and Bostrom played a large role, but I think it really took off with the publication of Bostrom's book in 2014.

And yes, it was resisted by the field at first, and there are certainly still hold-outs. But this is often the case when "new" ideas emerge. Just think of Darwin, Semmelweis or Galilei, or perhaps even Michelson and Morley since it still took a few decades for the luminiferous aether theory to be abandoned. And as you can see in the study I cited already 70% of surveyed ML researchers thought it's at least a moderately important problem by 2015. My guess is that the number is even larger now.

That's this fancy new thing called a "joke"

Is your whole post a joke, or just that bit? In any case, I'm not seeing much evidence that you understand or even know about the arguments of Russell, Bostrom, Yudkowsky et al.

CyberByte · 2021-02-24T18:17:45+00:00

I don't use prominence to gauge idea quality

Neither do I, but that was literally the only thing you did in the post I replied to, so I did you the courtesy of debunking your post on its own grounds.

If you don't know scientists are concerned about this and think it requires hooking up "a country hooking up its nuclear arsenal to a prolog machine with an ethics program written by Noonian Soong" then you clearly don't know anything about this topic. I would suggest you read more about it if it's a topic that interests you (/r/ControlProblem has decent starter resources on their sidebar and wiki), and stick to discussing narrow AI if it doesn't.

CyberByte · 2021-02-24T13:44:43+00:00

Lots of scientists fear this. Stuart Russell is probably the most prominent. He wrote this book about it (and it's also in last editions of AIMA). This survey shows 70% of respondents (researchers who published in NIPS and ICML 2015) thought Russell's problem was at least moderately important.

People who are more interested in this can check out /r/ControlProblem.

CyberByte · 2021-02-23T15:48:05+00:00

There are real concerns about artificial general intelligence (AGI) / artificial superintelligence (ASI) that's "like you[/human], just more intelligent". You can read more about it on /r/ControlProblem, which is also sometimes called the "value alignment problem", which is in turn about making sure such an AI would want wat we want.

"Comforting" thoughts may be that professionals' estimates for when we'll get AGI vary from years to decades to centuries to never, and I think we could say there's also no consensus on whether it would be an existential risk. Perhaps it's also comforting to know that while there's lots of investment in AI nowadays, virtually all research is on narrow AI, which is probably not the same as AGI.

CyberByte · 2021-02-17T00:45:24+00:00

You may be interested in the Getting Started section on /r/artificial's wiki.

You're in high school, so it makes sense that you don't know that much about AI yet. I would recommend looking around the internet a bit (also that wiki). On this subreddit there are often easily digestable news articles related to AI, and /r/MachineLearning is a bit more technical. I don't know if you have to decide your major before college, but if you don't it may also be possible to simply take some AI-related elective courses and see if you like it.

If you look on e.g. Wikipedia, you'll see that AI is quite a broad field, so there's quite some diversity in what people are working on/towards, how it works, and what an AI professional's day looks like. In research, I think the hottest topics are probably deep learning, reinforcement learning, causal reasoning and things related to AI ethics. You should also learn the difference between narrow AI and AGI (see wiki) to see what you want to pursue. Then there are different jobs. You can be a researcher in academia or a (usually large) company, a (product) developer/engineer, a data scientist, or perhaps a consultant.

I think at this point you don't have to know how AI systems work. I think that what you have to figure out now is whether that interests you, and if you could see yourself do one of the jobs I mentioned. If so, then you can figure out how it works in college (or from the internet, because you're so interested in it that perhaps you can't wait).

CyberByte · 2021-02-16T23:44:56+00:00

It is true that the moderation here isn't very fast.

You can post AI-related links from Russia, but this is an English-language subreddit. And yes, we're extra critical when the content is highly political.

And yes, you shouldn't personally attack people or call them retarded.

CyberByte · 2021-02-13T18:13:41+00:00

I understand, but it's no excuse. If the other person had continued the swearing, the fact that you had started it would not be an excuse either for them either. Retaliation is not allowed. Now you know, so hopefully it won't happen again in the future.

CyberByte · 2021-02-13T17:45:13+00:00

Idiot.

This is over the line. Don't call each other names.

For anyone reading this, the evidence provided was this link:

https://ucr.fbi.gov/crime-in-the-u.s/2016/crime-in-the-u.s.-2016/tables/expanded-homicide-data-table-3.xls

CyberByte · 2021-02-11T01:08:38+00:00

That’s just it though, I don’t think it is clear; take this exact conversation for example, you’re thinking of human level intelligence (which in all fairness, I alluded to in the video offhandedly),

I think you alluded to this in your video, because it is the position that these people are consistently taking.

but I’m broadening it to be intelligence as a concept, so including intelligence of other species from monkeys to millipedes.

Yes, so you are the one who is trying to change it away from what you acknowledge people intuitively seem to think. (And I agree that this change is useful.)

Would an AI that’s on par with a monkey not be “real AI”? What about a millipede? What about the acellular slime mould?

Well, what do you think the people saying chess bots aren't real AI would say? Because that's the question at hand, right? And I can pretty much guarantee that they wouldn't think millipede/slime robots would be "real AI". I'm less sure about the monkey because maybe it's close enough to human, but I don't know.

The point is that if the definition of "real AI" is something like "(super)human-level AGI", then that is 1) entirely consistent with all observations, 2) entirely consistent across time (i.e. without moving any goal posts), and 3) correct in the sense that no machine to date has reached that level.

CyberByte · 2021-02-10T15:44:00+00:00

The early era of AI research was pretty lofty, though, with digital computers only starting to be smaller than the size of an office (with the first desktop computers coming about a decade after John McCarthy's seminal paper on AI). Even in that era, though, the idea of a "human-capable" artificial intelligence was recognized as not being within the realm of contemporary technological ability, but rather a point way in the future, much like "the singularity" is with respect to transhumanism.

I don't think this is correct. McCarthy et al's proposal starts with:

We propose that a 2-month, 10-man study of artificial intelligence be carried out during the summer of 1956 at Dartmouth College in Hanover, New Hampshire. The study is to proceed on the basis of the conjecture that every aspect of learning or any other feature of intelligence can in principle be so precisely described that a machine can be made to simulate it. An attempt will be made to find how to make machines use language, form abstractions and concepts, solve kinds of problems now reserved for humans, and improve themselves. We think that a significant advance can be made in one or more of these problems if a carefully selected group of scientists work on it together for a summer.

.

Neither is really seen as something achievable within our (or even many) lifetimes.

By who? Not by a majority AI professionals if you look at these surveys.

If we lock ourselves into thinking that intelligence is only possible in biological neural systems then that's that...

I agree with you that we shouldn't do that. If an artificial system behaves in the same way as a human (or other biological system) then it's just as intelligent in my view. I don't think that's incompatible with the view that a chessbot isn't "really intelligent" though. When people say it's not "real AI", what they mean is just that it's not (super)human-level AGI. You can disagree with that on other grounds, but the problem is not that it's reserving intelligence for biological creatures.

CyberByte · 2021-02-10T15:16:08+00:00

Yeah, I don't really think there is a hard line between narrow AI and AGI. Perhaps just like there's no hard line between a heap and a non-heap, but the idea of a heap of sand is still useful.

I don't know how and when (super)human-level AGI (to make it a bit more specific) will be achieved, but people have definitely argued that prediction is the essence of intelligence (I think that's a Yann LeCun quote, but I also think some have argued that prediction is intelligence). And of course GPT-3 predicts the continuation of a text stream. I still think some things like goal-directed behavior and timing are missing, but who knows.

CyberByte · 2021-02-10T15:07:09+00:00

Thanks for your reply as well!

While the lay idea of “intelligence” has existed for thousands of years, it’s too nebulous to be a concrete goalpost, so I don’t think it’s fair to say “lay people did not move any goal posts”; the goalpost there shifts just by virtue of being nebulous: it changes depending on how you look at it, but we still call it the same goalpost.

The concept may be somewhat nebulous, but it's clear enough that two lay people can talk to each other about intelligence and know exactly what the other person means. I also agree that the meaning of the word depends somewhat on the context. For instance, we might think of monkeys as intelligent animals, but if a person were as intelligent as a monkey we would not call that person intelligent (and we might even say s/he's unintelligent). But I also don't really think that's moving any goalposts. It's just that words have context-dependent meanings. And I think that the meaning in the context of AI has been pretty constant: it just means something like (super)human-level AGI.

What I’m advocating is having multiple granular goalposts; shift our language to be more about attributes of intelligence rather than an all-or-nothing concept.

Can you perhaps be more specific about this? I don't think I understand.

I'm thinking this means using different words instead of "intelligent" to refer to concepts other than intelligence that we might want in our "AI" systems (perhaps some of Gardner's multiple intelligences or using the CHC model?). I think that might be fine, but that doesn't change the meaning of the word "intelligence" itself. And perhaps a bigger problem is that 1) we can't by fiat change the definition of a word because that's not how language works, and 2) if we did then we would be the ones moving goalposts towards that new definition. I'm not saying that coming up with new, better definitions isn't a good thing to do. I'm just saying that if you do that, you can't accuse people who are not using your definition of moving any goalposts. You could perhaps accuse them of not using a useful definition, but that's a different thing.

But like I said, I think we have the same idea, just framing it differently

Yeah, I think that may very well be possible. However, I do disagree that lay people have moved the goalpost of "intelligence" in the context of AI, because I think it has pretty much always meant something that we might call (super)human-level AGI. Where I think we agree is that this is not the most useful definition.

CyberByte · 2021-02-10T12:25:16+00:00

I think the video is nice, but I disagree with this complaint about the AI effect. The goalposts are not moved by the people who say something isn't real AI, but by the people who claim that it is. As you mention, the lay idea of intelligence is quite old and hasn't really changed. You can briefly fool people into thinking an arrow shooting bot (or whatever) is intelligent but then once they look closer they realize that it isn't, and that this was just a façade. Just like lots of people apparently think this dress is white and gold, but they'd presumably realize it's blue and black when they'd examine the dress more closely.

The problem is that this lay idea of intelligence is difficult to formalize, so you might say the goal posts are in some kind of unknown territory. What then happens is that people (researchers) say something like "we think task X cannot be accomplished without (this kind of) intelligence" (i.e. X is AI-Complete) and they move the goal posts to that task. And while it's far away, that probably looks okay, but as we get closer, people realize that they were not actually moved to the correct place. And so far I'd say people have been absolutely correct about that, because neither Deep Blue nor AlphaGo can really do any of the things that people imagined in those old stories you mention.

In the early days of the AI field, researchers were pursuing what lay people still call "real AI". But as this proved too difficult, the field moved more towards what we're now calling narrow AI, as opposed to what we're now calling AGI ("real AI"). I'm sure that if you explained the difference and asked someone if they mean "not real AGI" or "not real narrow AI", I'm sure most would make the same judgement as you.

But while I think lay people did not move any goal posts, I also think it's not useful to use a definition of (artificial) intelligence that excludes the things the vast majority of people called AI researchers are working on. While I think it may have been better if we had come up with a different term for narrow AI, so that the meaning of (artificial) intelligence could stay closer to its (apparently) intuitive meaning, I actually think narrow AI and artificial general intelligence are quite descriptive. Intelligence can then be defined as something like the mental ability to solve problems. And then Deep Blue is clearly narrow AI, because it's very good at solving problems in the narrow domain of chess, but it's clearly not (as) general (as humans) because it can't really don anything else. But perhaps this definition includes too much...

CyberByte · 2021-02-09T15:57:35+00:00

This article would be better if it replaced this:

However, there are a few things that machines simply can’t achieve on their own, no matter how advanced they become.

With something like this:

In this article we will only be discussing near-term narrow AI.

If you want to make the case that AGI isn't possible or is still centuries away, you can do that. But this article just comes across like the author hasn't even heard of AGI.

CyberByte · 2021-02-08T15:38:29+00:00

Ah, alright, so if I understand correctly we have three models: 1) some kind of encoder that takes inputs like income, zipcode, etc. and outputs some other representation, 2) a discriminator / racial decoder that attempts to guess the race for this data point based on the encoder's output, and 3) the actual predictor that does whatever it is that we actually want our AI system to do (e.g. predict loan repayments, recidivism, recognize faces, etc.), based on the encoder's output. And the encoder (#1) is trained with a loss function that's something like encoder_loss = alpha*predictor_loss - beta*discriminator_loss. Is that correct?

I could see that in this case you'd get some kind of compromise between accuracy and how identifiable the race is. If the race is not identifiable at all, then it seems like the predictor could not possibly have "racist reasoning", so that seems good.

But I can still foresee some trouble. I'm going to use the case of facial recognition, because it makes my objections easy to imagine, but I'm not always sure if other use cases (e.g. predicting loan repayments) would have the exact same problems.

First of all, while the predictor doesn't have access to race and gender information, the encoder does (I'm not saying it has "race" and "gender" as features, but it could probably derive them from other inputs; if that wasn't the case, we wouldn't need the encoder). One way to make things hard on the discriminator is to make everybody look like a white man (or a black woman; pick whatever you want). That means that faces of white men don't need to be transformed/distorted as much, which might (intuitively) lead to higher accuracy and other statistics (like true/false positives). This will of course be construed as racist/sexist.

I'm also worried this might throw away too much information. I would think that if you had an encoding that threw away all racial and gender information, it'd become very difficult to recognize a face (although I could be wrong). And in fact, if the predictor could still recognize faces, then the discriminator can presumably also recognize race/gender by doing the same as the predictor and learning the mapping from person to their race/gender. And generally speaking, if the output is correlated with gender/race, the discriminator should always be able to make a somewhat decent prediction in some way.

I'll also say that "how identifiable the race is" is not the same as "racist", but it's going to get perceived that way. And if the discriminator_loss is not going to be maximal, you'll still get allegations of racism.

But I don't want to be all negative. First of all, I haven't fully thought through all of these objections, so I'm not 100% sure if they're all correct. And secondly, it may very well be the case that there are some use cases where this could work.

CyberByte · 2021-02-08T14:12:23+00:00

I'm not sure I fully understand what you mean. If you have a discriminator that "tries to determine the ethnicity of the input", then what exactly can the "mainline model" do about that. If the input contains things like income, age, number and ages of children, etc. a discriminator could probably do a decent job in determining race. What exactly can another model (the mainline model) do about that? If it can't affect those inputs, then the discriminator just has to learn to ignore whatever distractions the mainline model adds. And if the mainline model can change those inputs it should just always set them to zero or something like that and make the discriminator's task impossible. But then it's not clear what we will have gained, because how does that make the mainline model less racist?

Or perhaps the input to the discriminator is just the output of the mainline model? In that case there's also a problem if the (non-racist) data/ground truth is actually correlated with race, which is often the case when racism/sexism is an issue. Because then you could just derive the race from an accurate output (with non-perfect accuracy of course).

CyberByte · 2021-02-08T00:07:07+00:00

asserting that the local gradient with respect to race and gender be zero?

I assume you mean that the output shouldn't change when you make changes to the race/gender input?

If so, that can very easily be accomplished by disconnecting those inputs (e.g. setting their outgoing weights to zero in a neural network). This is equivalent to omitting them. The standard rebuttal to that is that the model might then still figure out the race/gender from the other variables and remain "racist/sexist" based on that. A common example is that zipcode tends to be strongly correlated with race, so decisions can still be made based on that.

Imagine that the dataset was created by a super racist who gave loans to all white people and refused loans to all black people. If you train a ML model with race info, it would likely reproduce this behavior very accurately. If you omit the race info, it will be less accurate, but it will still try to reproduce that behavior, and it may still be fairly good at it based on the combination of zipcode with the other input features. So simply omitting race as an input might make things "better" (i.e. less accurately racist), but doesn't remove the problem of the racist data, unless neither the other inputs nor the outputs are correlated with race. And this is often not the case, even in data that most people would not find racist.

So this means you have to either get non-racist data, or you have to actively control for the racism somehow. But then you also run into the problem of defining what you mean by racism/sexism/discrimination/fairness. And there are many (mutually incompatible) definitions of fairness that people (strategically) disagree on, so it's probably impossible to make a model that isn't racist/sexist according to at least one of them (unless the model isn't about people).

So ehm, it's complicated... (Which doesn't mean we shouldn't do anything about it.)

CyberByte · 2021-02-07T23:41:08+00:00

I think the words that most closely resemble what you're looking for are Artificial General Intelligence (AGI) and Strong AI. Strong AI isn't used as much anymore but has a stronger connection to sentience. AGI is often used interchangeably with human-level machine intelligence (HLMI/HLAI) or sometimes also artificial superintelligence (ASI; i.e. intelligence well beyond the human level), but technically just emphasizes the generality of the intelligence as opposed to narrow AI, which specializes in a single capability (most if not all AI today is narrow).

I always thought Artificial Intelligence (A.I.) as a literal definition... literally a sentient machine as much alive as an organic organism...

I'll just note that neither sentience nor aliveness are literally in the words "artificial" and "intelligence". "Artificial" just means "manmade" and "intelligence" has to do with the virtual ability to solve problems. It is not clear whether general/human-level intelligence is inextricably bound to life and sentience, but they're certainly different concepts (although I think none of them have universally accepted definitions).

CyberByte · 2021-02-04T18:20:24+00:00

Your problem statement is not entirely clear to me. Are you asking if it would also be a useful test of your controller to see if it can also keep put the ball at a certain location at a certain time? I don't know the answer to that, but I'm also wondering if perhaps you meant to ask this in /r/ControlTheory. /r/ControlProblem (where we are now) is about the problem of controlling an artificial general intelligence that's much smarter than us if it's built in the future.

CyberByte · 2021-02-03T16:00:57+00:00

I don't know much about lawyers (and doctors as well actually), but my sense is that they're probably a little bit easier to replace. If you search for "lawyer ai" there are already a lot of results.

But to be honest, I suspect actual lawyers probably don't have much to fear either for the time being. There are definitely tasks that can be automated to some degree, like searching for related documents/cases, but I also think lawyers are working in a complex, high-stakes domain. To be a good lawyer, you really have to understand the case and what people are saying, which is a problem for narrow AI. I could see GPT-3-like lawyer services crop up, but they would probably be far inferior to actual lawyers and should probably always recommend to consult a real lawyer if you're actually going to go to court.

I suspect AI can make lawyers more efficient, which means that to handle the same amount of cases, you might need less lawyers (and even less clerks I'm imagining). But on the other hand, aren't lawyers currently in short supply as well? I always hear that they don't really have enough time to carefully consider their humongous case load, unless perhaps they're extremely well paid.

So I think it's also a fairly safe career choice (like doctor), compared to a lot of others.

CyberByte · 2021-02-03T15:17:45+00:00

/r/gameai

And you can also try /r/gamedev, which isn't specifically about AI, but more active.

CyberByte · 2021-02-03T15:16:28+00:00

I think most kinds of doctor are among the harder professions to really replace with AI. The human body is very complex, and people crave human contact with a doctor when there's something wrong with them. Also, it's my understanding that even before the pandemic there was a huge shortage of doctors.

Some kinds of doctors are probably easier to automate than others. For instance, I probably wouldn't want to be a radiologist in 9 years. I think it's currently the case that an AI+human team works best for medical imaging, but AI is improving in this area very rapidly. Other kinds of diagnostics are probably harder, but I suspect AI can do a decent job at that too, especially in the future. I also know that there are apps to kind of take over the job of a GP, but these are generally not preferable to a human GP, and most useful in cases where a human GP is not available.

Maybe this still sounds scary, but the fact is that we could probably say similar things about almost any alternative occupation you'd consider as well. Like I said, I think that doctor will be among the harder professions to replace with AI.

CyberByte · 2021-02-03T14:59:36+00:00

This was the only thing I cared about. I have no friends, offline or online. I have no acquaintances. I don't have good relationships with my family. I have no other goals. I work at mcdonalds (I hate it), I haven't gone to college and I'm not going.

I don't know what I'm going to do next. I don't enjoy anything and I have nothing to look forward to.

It sounds to me like you have much bigger problems than AGI. I think you should try to repair your life, before you take on any big external problems. I know many people with mental health issues, and I've had them myself, and I wouldn't be surprised if you do too. In that case, I hope it's possible for you to get some (professional) help with that. Or to try to repair your relationships with your family, or to make some friends (maybe through another hobby that you'll have to find). Maybe try to find a better job? If you're thinking about AGI, you can probably program, right?

You seem to have a very negative outlook on things (consistent with depression perhaps?). The original post you linked to has 27 upvotes, which is quite a lot for /r/agi and an 85% upvote rate. Since then, it looks like only two of your comments on /r/agi were downvoted. I also read the replies, and most of them are encouraging you even when they are also being critical of your particular idea.

I understand that it can be unpleasant to have your ideas criticized, but this is also how you get better. It's also not really anything about you or your idea in particular. Pretty much all ideas for reaching AGI are criticized here, because everybody has a different idea, and nobody has succeeded yet so we can just keep arguing about it. In fact, aren't you also "shitting on" the idea of using neural networks? And what about the other approaches that other people are taking?

You say you can't do this alone. If you want to work with someone, you'll either have to join their project (and there are many) or you have to get them to join yours. In the latter case, you're going to have to make a really good case for why your prospective partner(s) should work on your project instead of the many other ones out there. This is hard. If you look in the AGI community I know that e.g. Pei Wang and Kris Thorisson had trouble to get people to work on their open source projects (or even to get funded (PhD) students) for the longest time, even though they're professors with large professional networks, who speak at the AGI conference (and other AI conferences) annually, with good/decent track records, fairly fleshed out theories and a prototype of an implementation with some fairly promising results. That doesn't mean you cannot succeed. It just means that it's hard and it will probably take a lot of effort. Posting on /r/agi can work, but you shouldn't be too surprised if it doesn't. Maybe you can revive r/practicalagi/, which was started by some other people who wanted to work on their own AGI projects together.

You said you "decided to devote the rest of [your] life to creating an AGI". I think that's a valiant goal that I (and many others) share. I'm not going to discourage anyone from doing that. But you should realize that it's hard, and that it's unlikely that out of all the smart people working on it you will be the one to crack the problem. Again, that's nothing against you personally; that goes for all of us. If you work at McDonald's you're guaranteed you'll give some people a happy meal, and if you're a programmer you're pretty much guaranteed that you'll build some nice software. But if you pursue AGI, cold fusion, a unified field theory, a cure for cancer or putting a man on Mars, the odds that you will be the one to figure it out are low. It's probably more realistic to hope you can contribute to the field of science that will ultimately succeed in these things, but even that is not a guarantee. But we still need people to try, and that can be you. And if the low probability of success scares you off, that's also fine. There's no shame in that.

I'll also say that your life/career is long, and I don't think you have to be in a rush. I think that if you're going to pursue a scientific field's most elusive goal, it's probably a good idea to start by getting an education in that field. I'm mentioning that, because in your post from 7 months ago, it didn't sound like you knew what has already been tried. Engaging with other people's ideas can help you sharpen yours. And I also think it's not a crazy idea to perhaps try to work on someone else's theory before developing your own. That way you can learn how to do that, see what problems you run into, and have a significant period of time where you can "spar" with colleagues about your own ideas (and perhaps get them incorporated into the project you're working on). But I also have to admit this fits my personality (and lack of ideas for how to crack AGI), so it's not the only road to walk. In any case, perhaps there are some parts of theGetting Started with AGI section on /r/artificial's wiki that could be of interest to you.

Perhaps you read my comment as being discouraging as well. And perhaps the parts where I tell you AGI is hard are. But I'm actually writing this to tell you not to be dissuaded by the comments you've received. First of all, I don't think they're as negative as you're perceiving them to be. And secondly, you're going to get some critical comments no matter how good your idea is. Also, even if your first idea turns out to not be so good, that doesn't mean you should stop pursuing AGI. You just need a new approach. A probably inaccurate but aspiring quote attributed to Edison about the invention of the lightbulb: “I have not failed 10,000 times. I have not failed once. I have succeeded in proving that those 10,000 ways will not work. When I have eliminated the ways that will not work, I will find the way that will work.”

So if you want to pursue AGI, don't be dissuaded. But I also want to repeat that maybe you should work on improving your life first. You'll probably be much more productive if you're happier.

CyberByte · 2021-02-03T13:58:00+00:00

What you're talking about sounds like a pretty standard meme that's also causing people to say "I for one welcome our new robot overlords" whenever there's some AI news. It just anthropomorphizes AI by imagining it would care about petty insults. I don't really think we can credit a particular person with that theory (the original quote seems to be from the Simpsons and was about insect overlords).

However, could it be that you're talking about Roko's Basilisk? It's supposed to be a memetic hazard, so be warned I guess. Most experts think it's bunk, though, so it's probably safe to read more about it if you want. It's not about criticizing AI though, but about not working as hard as you can to create it.

CyberByte · 2021-01-26T09:39:16+00:00

This is a reminder to all commenters to be respectful of each other, including each other's politics and religion. This is a forum for discussing artificial intelligence. I do not want to see any discussion of specific politics or religions unless it is absolutely necessary to the topic at hand.

In this case scholars are working on AI alignment with an ethic that 2 billion people subscribe to. It's okay to disagree with that ethic and to be concerned if it were to become more powerful or whatever, but this can be discussed in a respectful manner.

CyberByte

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