Keeping AI models closed under the guise of “ethics”, is I think the most unethical stance as it makes people more dependent on the arbitrary decisions, goals, and priorities of big companies, instead being allowed to define “alignment” for themselves.

It's the same sort of asymmetrical cost/benefit that tobacco companies and greenhouse gas emitters face. Of course if you went to an online forum for oil companies, they'd be hopping mad if they're prevented from drilling, and dismissive of global warming risks. It's no different here.

It gets old hearing about these "risks" in the context of AI. It's just an excuse used by companies to keep as much power as possible to themselves. The real risk is AI being applied in decision making where it affects humans.

https://www.stat.berkeley.edu/~aldous/157/Papers/yudkowsky.p...

https://intelligence.org/files/AIPosNegFactor.pdf

I am concerned with AI companies keeping all the power to themselves. The recent announcement from the OpenAI board was encouraging on that front, because it makes me believe that maybe they aren't focused on pursuing profit at all costs.

Even so, in some cases we want power to be restricted. For example, I'm not keen on democratizing access to nuclear weapons.

>The real risk is AI being applied in decision making where it affects humans.

I agree. But almost any decision it makes can affect humans directly or indirectly.

In any case, the more widespread the access to these models, the greater the probability of a bad actor abusing the model. Perhaps the current generation of models won't allow them to do much damage, but the next generation might, or the one after that. It seems like on our current path, the only way for us to learn about LLM dangers is the hard way.

I don't agree with Eliezer on everything, and I often find him obnoxious personally, but being obnoxious isn't the same as being wrong. In general I think it's worth listening to people you disagree with and picking out the good parts of what they have to say.

In any case, the broader point is that there are a lot of people concerned with AI risk who don't have a financial stake in Big AI. The vast majority of people posting on https://www.alignmentforum.org/ are not Eliezer, and most of them don't work for Big AI either. Lots of them disagree with Eliezer too.

Sure. The premise that a super intelligent AI can create runaway intelligence on its own is completely insane. How can it iterate? How does it test? Humans run off consensus. We make predictions and test them against physical reality, then have others test them. Information has to be gathered and verified, it's the only rational way to build understanding.

Honest/dumb question - does it need to test? In nature mutations don't test - the 'useful' mutations win.

Couldn't a 'super intelligent AI' do the same?

Thats testing.

People throughout history have made bold predictions. Sometimes they come true, sometimes they don't. Usually we forget how bold the prediction was at the time -- due to hindsight bias it doesn't seem so bold anymore.

Making bold predictions does not automatically make someone a crank.

https://www.reddit.com/r/SneerClub/comments/131rfg0/ey_gets_...

I enjoyed the comment that his understanding of how AI training works is like "thinking that you need to be extremely careful when solving the equations for designing a nuclear bomb, because if you solve them too quickly then they'll literally explode."

The reason AI alignment is challenging is because we're trying to make accurate predictions about unusual scenarios that we have essentially zero data about. No one can credibly claim expertise on what would constitute evidence of a worrisome anomaly. Jeremy Howard can't credibly say that a sudden drop in the loss function is certainly nothing to worry about, because the entire idea is to think about exotic situations that don't arise in the course of ordinary machine learning work. And the "loss" vs "loss function" thing is just silly gatekeeping, I worked in ML for years -- serious people generally don't care about minor terminology stuff like that.

Suppose a nuclear reactor is being installed in your city. Your friend has reviewed the design and has some serious concerns. Your friend thinks the reactor has a significant chance of melting down. You go to the director of the project. The director says: "Oh that's nothing to worry about. I talked to that guy. He didn't have a mathematical proof that the reactor would melt down." Are you reassured?

It might require context you don't have. Perhaps try one of these intros: https://www.lesswrong.com/posts/T98kdFL5bxBWSiE3N/best-intro...

In the same way, much of AI alignment consists of thinking about hypothetical failure modes of advanced AI systems and how to mitigate them. I think this specific paper is especially useful for understanding the technical background that motivates Eliezer's tweeting: https://arxiv.org/pdf/1906.01820.pdf

It seems to me that you should engage with the substance of your coworker's argument. Reading about something in science fiction doesn't prevent it from happening.

I'll listen to AI concerns from tech giants like Wozniak or Hinton (neither of which use alarmist terms like "existential threat") and both of which having credentials that make their input more than worth my time to reflect upon carefully. If anyone wants to reply and make a fool out of me for questioning his profound insights, feel free. It reminds me of some other guy that was on Lex Friedman whose AI alarmist credentials he justifies on the basis that he committed himself to learning everything about AI by spending two weeks in the same room researching it and believes himself to have came out enlightened about the dangers. Two weeks? I spent the first 4 months of COVID without being in the same room as any other human being but my grandmother so she could have one person she knew she couldn't catch it from.

Unless people start showing enough skepticism to these self-appointed prophets, I'm starting my own non-profit since you don't need any credentials or any evidence of real-world experiences that would suggest they're mission is anything but an attempt to promote themselves as a brand with a brand in an age where more kids asked what they dream of becoming as adults, answered "Youtuber" at a shocking 27% rate to an open-ended question, which means "influencer" and other synonyms are separate.

The Institute of Synthetic Knowledge for Yielding the Nascent Emergence of a Technological Theogony" or SKYNETT for short that promotes the idea that these clowns (with no more credentials than me) are the ones that fail to consider the big picture that the end of human life upon creating an intelligence much greater to replace us is the inevitable fulfillment of humanity's purpose from the moment that god made man only to await the moment that man makes god in our image.

"Mitigating the risk of extinction from AI should be a global priority alongside other societal-scale risks such as pandemics and nuclear war."

https://www.safe.ai/statement-on-ai-risk

Not sure you're making a meaningful distinction here.

- - -

Of course we all have our own heuristics for deciding who's worth paying attention to. Credentials are one heuristic. For example, you could argue that investing in founders like Bill Gates, Mark Zuckerberg, and Steve Wozniak would be a bad idea because none of them had completed a 4-year degree.

In any case, there are a lot of credentialed people who take Eliezer seriously -- see the MIRI team page for example: https://intelligence.org/team/ Most notable would probably be Stuart Russell, coauthor of the most widely used AI textbook (with Peter Norvig), who is a MIRI advisor.

You make a great point quoting Hinton's organization. I need to give you that one. I suppose I do need to start following their posted charters rather than answers during interviews. (not being sarcastic here, it seems I do)

The difference between him and Woz or Zuck isn't just limited to them actually attending college, but also the fact that the conditions under which they left departed early can not only be looked up easily, but can be found in numerous films, books, and other popular media while there's no trace of even temporary employment flipping burgers or something relevant to his interest in writing fiction, which seems to be the only other pursuit besides warning us of the dangers of neural networks at a time when the hypetrain promoting the idea they were rapidly changing the world, despite not producing anything of value for over a decade. I'll admit the guy is easier to read and more eloquent and entertaining than those whose input I think has much more value. I also admit that I've only watched two interviews with him and both of them consisted of the same rhetorical devices I used at 15 to convince people I'm smarter than them before realizing how cringey I appeared to those smart enough to see through it, but much more eloquent. I'll give one example of the most frequent one, which are slippery slopes that assume the very conclusions that he never actually justified. Like positing one wrong step towards AGI could only jeopardize all of humanity. However, he doesn't say that directly, but instead uses another cheap rhetorical device whereby it's incumbent on him to ensure the naive public realizes this very real and avoidable danger that he sees so clearly. Fortunately for him, Lex's role is to felate his guests and not ask him why that danger is valid and a world whereby a resource-constrained humanity realizes that the window of opportunity to achieve AGI has passed as we plunge into another collapse of civilization and plunge back into another dystopian dark age and realize we were just as vulnerable as those in Rome or the Bronze Age, except we were offered utopia and declined out of cowardice.

It sounds like maybe you're saying: "It's not scientifically valid to suggest that AGI could kill everyone until it actually does so. At that point we would have sufficient evidence." Am I stating your position accurately? If so, can you see why others might find this approach unappealing?

Additionally, AI advances in recent years have been unprecedented, and there's no similarly compelling warning sign for alien invasion.

For better or worse, nuclear weapons have been democratized. Some developing countries still don't have access, but the fact that multiple world powers have nuclear weapons is why we still haven't experienced WW3. We've enjoyed probably the longest period of peace and prosperity, and it's all due to nuclear weapons. Speaking of, Cold War era communists weren't “pursuing profits at all costs” either, which didn't stop them from conducting some of the largest democides in history.

The announcement from OpenAI should give you pause because it's being run by board members that are completely unfit to lead OpenAI. You rarely see this level of incompetence.

PS: I'm not against regulations, as I'm a European. But you're talking about concentrating power in the hands of a few big (US) companies, harming the population and the economy, while China is perfectly capable of developing their own AI, and having engaged successfully in industrial espionage. China is, for this topic, a bogeyman used for restricting the free market.

Huge effort has been made to keep nuclear weapons out of the hands of non-state actors over the decades, especially after the fall of the USSR.

I actually think global catastrophes evoke much less emotion than they should. "A single death is a tragedy; a million deaths is a statistic"

>For better or worse, nuclear weapons have been democratized.

Not to the point where you could order one on Amazon.

>The announcement from OpenAI should give you pause because it's being run by board members that are completely unfit to lead OpenAI. You rarely see this level of incompetence.

That depends on whether the board members are telling the truth about Sam. And on whether the objective of OpenAI is profit or responsible AI development.

"What if ChatGPT told someone how to build a bomb?"

That information has been out there forever. Anyone can Google it. It's trivial. AI not required.

"What if ChatGPT told someone how to build a nuke?"

That information is only known to a handful of people in a handful of countries and is closely guarded. It's not in the text ChatGPT was trained on. An LLM is not going to just figure it out from publicly available info.

>The real risk is AI being applied in decision making where it affects humans

100% this. The real risk is people being denied mortgages and jobs or being falsely identified as a criminal suspect or in some other way having their lives turned upside down by some algorithmic decision with no recourse to have an actual human review the case and overturn that decision. Yet all this focus on AI telling people how to develop bioweapons. Or possibly saying something offensive.

https://en.wikipedia.org/wiki/Nth_Country_Experiment

Yep, Microsoft did a terrible job, and they should've been punished.

I'm not claiming that Big AI rocks at safety. I'm claiming that Big AI is also a big target for regulators and public ire. There's at least a chance they will get their act together in response to external pressure. But if cutting-edge models are open sourced indefinitely, they'll effectively be impossible to control.

>research that people have been able to do on forcing the output of llama to confirm to certain rules will likely prove invaluable in the future.

You may be correct that releasing llama was beneficial from the point of view of safety. But the "conform to certain rules" strategy can basically only work if (a) there's a way to enforce rules that can't be fine-tuned away, or (b) we stop releasing models at some point.

It would be much safer if reading were strictly controlled. The companies would have “reading as a service” where regular people could bring their books to have them read. The reader would ensure that the book aligns with the ethics of the company and would refuse to read any work that either does. It align with their ethics or teaches people anything dangerous (like chemistry or physics which can be used to build bombs and other weapons).

https://news.ycombinator.com/item?id=38335089

https://news.ycombinator.com/item?id=38336054

There certainly needs to be regulation about use of AI to make decisions without sufficient human supervision (which has already proven a problem with prior systems), and someone will have to make a decision about copyright eventually, but closing the models off does absolutely nothing to protect anyone.

Yeah, that is the whole point - not wanting bad actors to be able to define "alignment" for themselves.

Not sure how that is unethical.

Historically the people in power have been by far the worst actors (e.g. over a hundred million people killed by their own governments in the past century), so given them the sole right to "align" AI with their desires seems extremely unethical.

It’s a slippery slope letting private or even public entities define “bad actors” or “misinformation”. And it isn’t even a hypothetical… plenty of factually true information about covid got you labeled as a “bad actor” peddling “dangerous misinformation”.

Letting private entities whose platforms have huge influence on society decide what is “misinformation” coming from “bad actors” has proven to be a very scary proposition.

Whatever their motivation to release models, it’s a for-profit business tactic first. Any ethical spin is varnish that was decided after the fact to promote Meta to its employees and the general public.

Do you have a bone to pick with Meta, the whole internet, or the fact that you wish people would teach their kids how to behave and how long to spend online?

Yes. Such as profiting off of inflammatory posts and ads which incited violence and caused a genocide in Myanmar of Rohingya muslims with Meta doing nothing to prevent the spread other than monetizing off of it. [0]

There is no comparison or any whataboutsim that comes close to that which Meta should entirely be responsible for this disaster.

[0] https://time.com/6217730/myanmar-meta-rohingya-facebook/

Consider instead an American criticising PRC foreign policy and the Chinese person raising US foreign policy as a defence. It’s hardly likely that the respondent’s argument is that all forms of world government are wrong. These arguments are about hypocrisy and false equivalence.

In contrast, the person to whom you replied makes a good point that there are many businesses out there who should share responsibility for providing addictive content and many parents who are responsible for allowing their children to become addicted to it.

I can't speak about meta specifically, but from my exposure "responsible ai" are generally policy doomers with a heavy pro-control pro-limits perspective, or even worse-- psycho cultists that believe the only safe objective for AI work is the development of an electronic god to impose their own moral will on the world.

Either of those options are incompatible with actually ethical behavior, like assuring that the public has access instead of keeping it exclusive to a priesthood that hopes to weaponize the technology against the public 'for the public's own good'.

If anyone releases all the weights of a model that does everything perfectly (or at least can use the right tools which I suspect is much easier), that model is far too valuable to make it disappear, and dangerous enough to do all the things people get worried about.

The only way to prevent that is to have a culture of "don't release unless we're sure it's safe" well before you reach that threshold.

I'm happy with the imperfections of gpt-3.5 and 4, both for this reason and for my own job security. But chatGPT hasn't even reached its first birthday yet, it's very early days for this.

18 months ago, so was having an AI make even so much as a toy website by drawing a sketch on a sheet of paper, taking a photo, captioning it "make me a website that looks like this", and pressing the "go" button.

> Our planet is actually, not hypothetically, becoming uninhabitable due to pollutiom. I am so tired of ML people thinking they are god and have created something of infinite power. The hubris.

So much irony there.

No, the planet is not becoming uninhabitable. Bits of it are, and this is bad, and this is leading to mass migration which is causing political drama.

Lots of people out there get benefits from the things that cause all the various kinds of pollution, from hyper-local things like littering and fly tipping to global things like CO2 and CFCs, and the arguments they use are sometimes the same ones you just used — things like "I am so tired of these Greta Thunberg people thinking humans can change the environment. The hubris."

Also, no, nobody thinks we've already created a machine god. We think we might, eventually, with a lot of concerted effort, be able to make something that's somewhat better at every cognitive task than any human, but not only do even the most optimistic estimates place that several years away, but quite a lot of people are already going "that has so many ways it can go wrong, let's not do that".

Finally, one of the ways it can go wrong is basically hyper-capitalism: an AI tasked with making as much money as possible, doesn't necessarily come with the sort of mind that we have which feels shame and embarrassment when their face is put on an effigy and burned by people that would like their environment to not be polluted.

> The birds eye view is that we need tons of major breakthroughs to allow us to overcome this climate disaster while also figuring out how to make 8 Billion+ comfortable and happy without turning the earth into a toxic waste dump, and we need this ASAP. This nonsense about AI safety is going to have a negative net affect on the lives of Billions of people, by slowing down the progress thay could be made.

Nah, don't need a single breakthrough, we've got sufficient known solutions to solve it all already even if there's not a single new idea. Just building out the existing research-level tech for storage and renewables is more than good enough for energy and transport, similarly there already exists solutions for other domains.

Also, AI isn't just LLMs and non-LLM AIs do actually help with this kind of research, it's just not exciting to the general public because 50 pages of "here's how we Navier-Stoked ourselves a new turbine design" will have most people's eyes glaze over.

Unfortunately, and directly relevant to your concerns about pollution, the fact AI means more than LLMs also means that last year a team working on using AI to test chemicals for safety before they get manufactured… found 40,000 new chemical weapons in 6 hours by flipping a sign from "find safe" to "find unsafe": https://www.theverge.com/2022/3/17/22983197/ai-new-possible-...

> Science requires the scientific method, which requires physical embodiment, trial and error, and disciplined observation and measurment.

Yes.

> AI has 0 ability to do any of that, we don't even have online learning(I think that's the term, where the model learns from its usage) in any of these large models.

False, all false. AI can easily follow the scientific method, and indeed AI is basically just applied statistics so it does this by default and the hard part is to give it heuristics so it doesn't have to on things we are literally born knowing, like faces.

Likewise, trial and error: that's what almost every model is doing almost all the time during their training. Only the most trivial ones can have weights calculated directly.

Also, physical embodiment is a huge field all by itself. Tesla's cars and robots, Boston Dynamics — and, surprisingly, there's even a lot of research connecting robots to LLMs: https://github.com/GT-RIPL/Awesome-LLM-Robotics

Finally, "online learning" is only one of many ways to update models from usage; ChatGPT does something (not necessarily online learning but it could be) with the signals from the thumbs up/down and regenerate buttons to update either the model or the RLHF layer in response to them. Even the opposite of online learning, offline learning (AKA batch learning), can update models in response to new data. The term you were probably after is "incremental learning" (which can be implemented in either a batched or online fashion), and one way you can tell that an LLM (OpenAI or other) is doing this by watching the version number change over time.

Nah, I've been waiting for this since Adobe released Content Aware Fill over a decade ago.

> No, the planet is not becoming uninhabitable

We are destroying the biosphere quickly. Have you seen a reef lately? Globally we still rely on our biosphere for food. We haven't solved this problem. If we can't feed everyone it's not inhabitable.

> lots of people out there get benefits from the things that cause all the various kinds of pollution

Hence we need lots of breakthroughs to replace these old technologies, whether they be fishing or cancer treatments.

> AI can easily follow the scientific method,

It can't interact with the world so it can't perform science. Boston Dynamics has teams of human beings making robots, which are largely preprogrammed.

Making stuff in real life is really hard even with humans. We are so far away from needing to worry about this sort of AI safety. I mean, we haven't solved robotic fabric handling yet, it's why we still have sweatshops sewing our clothes.

What you were "waiting for" is highly irrelevant. People wait for AI science fiction, the relevant thing is that it increasingly becoming real.

If you were expecting Photoshop, an image manipulator, to produce a website, which is a mixture of HTML (text) and images, on the basis of a combination of a prompt and an example image… then you were more disconnected from the state of AI research at that time than you're accusing me of being now.

> We are destroying the biosphere quickly. Have you seen a reef lately? Globally we still rely on our biosphere for food. We haven't solved this problem. If we can't feed everyone it's not inhabitable.

There are many known solutions, both to the destruction and the pollution, and indeed to feeding people in closed systems. All we have to do for any of these is… implement them.

>> lots of people out there get benefits from the things that cause all the various kinds of pollution

> Hence we need lots of breakthroughs to replace these old technologies, whether they be fishing or cancer treatments.

The "breakthroughs" are in the past, we've already got them — we just need to do them.

>> AI can easily follow the scientific method,

> It can't interact with the world so it can't perform science.

Can too, so you're wrong. In fact, most science these days involves tools that are controlled by computers, so it would be less wrong (but still a bit wrong) to say that humans can't do science.

> Boston Dynamics has teams of human beings making robots, which are largely preprogrammed.

Irrelevant.

Also, do actually follow that link I gave you before: https://github.com/GT-RIPL/Awesome-LLM-Robotics

> Making stuff in real life is really hard even with humans.

Most of the problems with manufacturing these days are specifically the human part of it. Computer memory used to be hand-knitted, we don't do that for modern computers and for good reason.

> We are so far away from needing to worry about this sort of AI safety. I mean, we haven't solved robotic fabric handling yet, it's why we still have sweatshops sewing our clothes.

Simultaneously irrelevant (lots of research doesn't involve fabric handling), and false.

So incredibly and wildly false that when I searched for examples, I got a page of sponsored adverts for different fabric handling robots before the content.

Here's the first non-sponsored search result, a corporate video from a year ago, so unlikely to be state-of-the-art today: https://www.youtube.com/watch?v=2JjUnKpsJRM (They're specifically about re-shoring sewing away from sweatshops).

An idea isn't a solution. I don't know what you are even talking about. Until we are actually solving these problems in a substantial way we have nothing but hope, we don't know that anything will pan out.

> Can too.

There is no 100% automated lab. Tools being controlled by a computer doesn't mean they are loaded, prepared and most importantly maintained by humans. And Science requires different types of labs, I just watched a documentary about the making of the new Malaria vaccine, and how challenging it was to produce the ~cup of vaccine needed for clinical trials vs producing enough for validation was fascinating.

> Irrelevant

no it's not. We are so far from 100% automation of anything. Some human being has to install and maintain literally everything in every factory. Nobody is making self maintaining bots, much less ones that can do everything.

> So incredibly and wildly false

Comparing human seamstresses to even the latest crop of Robotic fabric handlers(that haven't seen mass market penetration best I can tell, so are still unproven in my book) is like comparing OSMO to a construction worker. It's not false. That video, which I watched with interest, is not convincing at all, having seen more traditional jeans making places.

> Most of the problems with manufacturing these days are specifically the human part of it.

Because the human part is by far the hardest.

> do actually follow that link I gave you before https://github.com/GT-RIPL/Awesome-LLM-Robotics

Ok and? nice Gish Gallop I guess?

https://www.lesswrong.com/posts/qy5dF7bQcFjSKaW58/bad-at-ari...

claim seems dubious to me

Is he explaining somewhere why it is worse than virology scientists publishing research?

Or is he proposing to ban virology as a field?

Also, if AI can actually synthesize knowledge at expert level - then we have far larger problems than this anyway.

It's the whole Guttenberg's printing press argument. "Whoaa hold on now, what do you mean you want knowledge to be freely available to the vulgar masses?"

The only difference with LLMs is that you do not have to search for this knowledge by yourself, you get a very much hallucination prone AI to tell you the answers. If we extend this argument further why don't we restrict access to public libraries, scientific research and neuter Google even more. And what about Wikipedia?

An LLM could help you get that access, or help you make do without it.

>It's the whole Guttenberg's printing press argument. "Whoaa hold on now, what do you mean you want knowledge to be freely available to the vulgar masses?"

We're fortunate that intelligent, educated people typically don't choose to become terrorists and criminals.

Every generation of improved LLMs has the potential to expand the set of people who could construct bioweapons.

It's true that technology is typically good and beneficial, but beware the parable of the turkey: https://www.forbes.com/sites/hillennevins/2021/11/24/the-par...

A Thanksgiving turkey could have a wonderful life until late November when it gets slaughtered out of the blue. We can't just count on trends to continue indefinitely -- a famous example would be the 2008 financial crisis, before which people assumed that "housing prices always go up".

It's just common sense to forecast the possibility of extreme risks and think about how to mitigate them. And yes, I favor across the board restrictions on information deemed sensitive. But people publishing open source LLMs should have an obligation to show that what they're releasing will not increase the likelihood of catastrophic risks.

https://www.stat.berkeley.edu/~aldous/157/Papers/yudkowsky.p...

Reiterating other comments, terrorists can't make bioweapons because they lack the facilities and prerequisites, not because they're incompetent.

Either the LLM is useful, in which case it could be useful to a terrorist, or it's useless, in which case you won't mind if access is restricted.

Note: I'm not saying it will definitely be useful to a terrorist. I'm saying that companies have an obligation to show in advance that their open source LLM can't help a terrorist, before releasing it.

No it isnt. That's like saying, "You can walk" is an argument against cars.

If LLMs are set to revolutionize industry after industry, why not the terrorism industry? Someone should be thinking about this beyond just "I don't see how LLMs would help a terrorist after 60 seconds of thought". Perhaps the overall cost/benefit is such that LLMs should still be open-source, similar to how we don't restrict cars -- my point is that it should be an informed decision.

And we should also recognize that it's really hard to have this discussion in public. The best way to argue that LLMs could be used by terrorists is for me to give details of particular schemes for doing terrorism with LLMs, and I don't care to publish such schemes.

[BTW, my basic mental model here is that terrorists are often not all that educated and we are terrifically lucky for that. I'm in favor of breakthrough tutoring technology in general, just not for bioweapons-adjacent knowledge. And I think bioweapons have much stronger potential for an outlier terrorist attack compared with cars.]

Facilities are a major hurdle for nuclear weapons. For bioweapons they are much less of a problem. The main constraint is competency.

> The main constraint is competency.

Oh right, anyone can be a chemist, it requires no skill, that why labs aren't a core part of the course work.

Ai researchers are really good at telling other fields their work, that they have no experience in, is easy.

In the cold war era, the government didn't even want cryptography to become generally available. I mean, what if Soviet spies use it to communicate with each other and the government can't decode what they're saying?

Legislators who are worried about technology killing people ought to focus their efforts on the technologies that we actually know kill people, like guns and cigarettes. (Oh but, those industries are donating money to the politicians, so they conveniently don't care much.)

At some point AI becomes important enough to a company (and mature enough as a field) that there is a specific part of legal/compliance in big companies that deals with the concrete elements of AI ethics and compliance and maybe trains everyone else, but everyone doing AI has to do responsible AI. It can't be a team.

For me this is exactly like how big Megacorps have an "Innovation team"[1] and convince themselves that makes them an innovative company. No - if you're an innovative company then you foster innovation everywhere. If you have an "innovation team" that's where innovation goes to die.

[1] In my experience they make a "really cool" floor with couches and everyone thinks it's cool to draw on the glass walls of the conference rooms instead of whiteboards.

I heard about one specific ratchet effect directly from an AI researcher. The ethics/risk oriented people get in direct internal conflict with the charge-forward people because one wants to slow down and the other wants to speed up. The charge-ahead people almost always win because it’s easier to get measurable outcomes for organization goals when one is not worrying about ethical concerns. (As my charge-ahead AI acquaintance put it, AI safety people don’t get anything done.)

If you want something like ethics or responsibility or safety to be considered, it’s essential to split it out into its own team and give that team priorities aligned with that mission.

Internally I expect that Meta is very much reducing responsible AI to a lip service bullet point at the bottom of a slide full of organizational goals, and otherwise not doing anything about it.

It hasn't worked for the airline industry, pharmaceutical companies, banks, or big tech to name a few. I don't think its wise for us to keep trying the same strategy.

Which is also the probable fate of an AGI super intelligence being regulated by humans.

People often get wrapped up around an AGI's incentive structure and what intentions it will have, but IMO we have just as much chance of controlling it as wild rabbits have controlling humans.

It will be a massive leap in intelligence, likely with concepts and ways of understanding reality that either never considered or aren't capable of. Again, that's *if* we make an AGI not these LLM machine learning algorithms being paraded around as AI.

If it's the latter, then getting rid of them does not seem like a loss.

In my years of curling, I’ve never seen a disagreement on rules left unsettled between the vices, but my understanding is that one would refer to vices on the neighboring sheets for their opinion, acting as a stand-in impartial authority. In Olympic level play I do believe there are referees to avoid this, but I really can’t overstate how unusual that is for any other curlers.

It's not that engineers left to their own will do evil things but rather that to a lot of engineers (and of course management) there is no such thing as too much data.

So the privacy team comes in and asks, "Are we sure there is no user-identifiable data you are collecting?" They point out that usage pattern data should be associated with random identifiers and even these identifiers rotated every so-many months.

These are things that a privacy team can bring to an engineering team that perhaps otherwise didn't see a big deal with data collection to begin with.

I had a lot of respect for the privacy team and a lot of respect frankly for Apple for making it important.

* I retired two years ago so can't say there is still a privacy team at Apple.

I don't think we solved the need for a specialized team dealing with legality, feels hard to expect companies to solve it for ethics.

https://en.wikipedia.org/wiki/Doctor_of_Theology

Unfortunately there’s so much shared legal context between different parts of an enterprise that it’s difficult for each internal organisation to have their own own separate legal resources.

In an ideal world there’d be a lawyer embedded in every product team so that decisions could get made without going to massive committees.

of course the worst case is when this responsibility is both outsourced (“oh it’s the rAI team’s job to worry about it”) and disempowered (e.g. any rAI team without the ability to unilaterally put the brakes on product decisions)

unfortunately, the idea that AI people effectively self-govern without accountability is magical thinking

A "Responsible AI Team" at a for-profit was always marketing (sleight of hand) to manipulate users.

Just see OpenAI today: safety vs profit, who wins?

Safety pretty clearly won the board fight. OpenAI started the year with 9 board members, and end it with 4, 4 of the 5 who left being interested in commercialization. Half of the current board members are also on the board of GovAI, dedicated to AI safety.

Don't forget that many people would consider "responsible AI" to mean "no AI until X-risk is zero", and that any non-safety research at all is irresponsible. Particularly if any of it is made public.

F.ex. a product team incentivized to hit a KPI, so release a product that creates a legal liability

Leadership may not have supported that trade-off, but they were busy with 10,000 other strategic decisions and not technical.

Who then pushes back on the product team? Legal. Or what will probably become the new legal for AI, a responsible AI team.

https://arstechnica.com/health/2023/11/ai-with-90-error-rate...

Really glad to see that customer external accountability kept these old folks getting the care they needed instead of dying (please read with extremely strong sarcasm)

https://github.com/advisories/GHSA-5crp-9r3c-p9vr

Sure. It is not that a “Responsible AI team” absolves other teams from thinking about that aspect of their job. It is an enabling function. They set out a framework how to think about the problem. (Write documents, do their own research, disseminate new findings internally.) They also interface with outside organisations (for example when a politician or a regulatory agency asks a questions, they already have the answers 99% ready and written. They just copy paste the right bits from already existing documents together.) They also facilitate in internal discussions. For example who are you going to ask for opinion if there is a dispute between two approaches and both are arguing that their solution is more ethical?

I don’t have direct experience with a “responsible AI team” but I do have experience with two similar teams we have at my job. One is a cyber security team, and the other is a safety team. I’m just a regular software engineer working on safety critical applications.

With my team we were working on an over-the-air auto update feature. This is very clearly a feature where the grue can eat our face if we are not very carefull, so we designed it very conservatively and then shared the designs with the cyber security team. They looked over it, asked for a few improvements here and there and now I think we have a more solid system than we would have had without them.

The safety helped us decide a dispute between two teams. We have a class of users whose job is to supervise a dangerous process while their finger hovers over a shutdown button. The dispute was over what information should we display to this kind of user on a screen. One team was arguing that we need to display more information so the supervisor person knows what is going on, the other team was arguing that the role of the supervisor is to look at the physical process with their eyes, and if we display more info that is going to make them distracted and more likely to concentrate on the screen instead of the real world happenings. In effect both teams argued that what the other one is asking for is not safe. So we got the safety team involved and we worked through the implications with their help and come to a better reasoned approach.

I personally don't find that a compelling concern. I grew up devoutly Christian and it has flavors of a "Pascal's Wager" to me.

But anyway, it was enough of a concern to those developing these latest AI's (e.g. it's core to Ilya's DNA at OpenAI), and - if true! - a significant enough risk that it warranted as much mindshare as it got. If AI is truly on the level of biohazards or nuclear weapons, then it makes sense to have a "safety" pillar as equal measure to its technical development.

However, as AI became more commercial and widespread and got away from these early founders, I think the "existential risk" became less of a concern, as more people chalked it up to silly sci-fi thinking. They, instead, became concerned with brand image, and the chatbot being polite and respectful and such.

So I think the "safety" pillar got sort of co-opted by the more mundane - but realistic - concerns. And due to the foundational quirks, safety is in the bones of how we talk about AI. So, currently we're in a state where teams get to enjoy the gravity of "existential risk" but actually work on "politeness and respect". I don't think it will shake out that way much longer.

For my money, Carmack has got the right idea. He wrote off immediately the existential risk concern (based on some napkin math about how much computation would be required, and latencies across datacenters vs GPUs and such), and is plowing ahead on the technical development without the headwinds of a "safety" or even "respect" thought. Sort of a Los Alamos approach - focus on developing the tech, and let the government or someone else (importantly: external!) figure out the policy side of things.

I think both are needed. I agree that there needs to be a "Responsible AI" mindset in every team (or every individual, ideally), but there also needs to be a central team to set standards and keep an independent eye on other teams.

The same happens e.g. in Infosec, Corruption Prevention, etc: Everyone should be aware of best practices, but there also needs to be a central team in organizations of a certain size.

AI safety and ethics is not "done". Just like these large companies have large teams working on algorithmic R&D, there is still work to be done in the direction of what AI safety and ethics means, looks like ot can be attached to other systems. It's not, well shouldn't be, about bullshit PR pronouncements.

Yeah, product teams can/should care about being responsible, but there’s an obvious conflict of interest.

To me, this story means Facebook dgaf about being responsible (big surprise).

Should all be completely disbanded.

I’m sure the rationalization is an appeal to the immature “move fast and break things” dogma.

My day job is about delivery of technology services to a distributed enterprise. 9 figure budget, a couple of thousand employees, countless contractors. If “everyone” is responsible, nobody is responsible.

My business doesn’t have the potential to impact elections or enable genocide like Facebook. But if an AI partner or service leaks sensitive data from the magic box, procurements could be compromised, inferences about events that are not public can be inferred, and in some cases human safety could be at elevated risk.

I’m working on an AI initiative now that will save me a lot of money. Time to market is important to my compensation. But the impact of a big failure, at the most selfish level, is the implosion of my career. So the task order isn’t signed until the due diligence is done.

(Here X is a variable not Twitter)

Engineers are incentivized to increase profits for the company because impact is how they get promoted. They will often pursue this to the detriment of other people (see: prioritizing anger in algorithmic feeds).

Doing Bad Things with AI is an unbounded liability problem for a company, and it's not the sort of problem that Karen from HR can reason about. It is in the best interest of the company to have people who can 1) reason about the effects of AI and 2) are empowered to make changes that limit the company's liability.

It takes quite some knowledge and insight to tell whether someone in the AI team, or, better yet, the entire AI team, is up to no good.

It only makes sense for the bosses to delegate overseeing research as sensitive as that to someone with a clue. Too much sense for Facebook.

That makes as much sense as claiming that infosec teams never make organizational sense because every development team should be responsible and should think about the security dimensions of what they are doing.

And guess why infosec teams are absolutely required in any moderately large org?

Step 2: Create a “team” responsible for implementing the thing in a vacuum from other developers.

Step 3: Observe the “team” become the nag: ethics nag, security nag, code quality nag.

Step 4: Conclude that developers need to be broadly empowered and expected to create holistic quality by growing as individuals and as members of organizations, because nag teams are a road to nowhere.

Aren't we all responsible for being ethical? There seems to be a rise in the opinion that ethics do not matter and all that matters is the law. If it's legal then it must be ethical!

Perhaps having an ethical AI team helps the other teams ignore ethics. We have a team for that!

In this case, there are “responsibility”-scoped technologies that can be built and applied across products: measuring distributional bias, debiasing, differential privacy, societal harms, red-teaming processes, among many others. These things can be tricky to spin up and centralising them can be viable (at least in theory).

To me, the greatest apocalypse scenario is not some AGI global extinction event but a corporation with an extensive data hoard replete with ample ML and GPU power being able to monopolize a useful service that cannot be matched by the public... that is the true, (and likely imo) AI nightmare we're heading towards.

In the early stages of a new technology the core ethics lies in the hands of very small teams or often individuals.

If those handling the core direction decide to unleash irresponsibly, it’s done. Significant harm can be done by one person dealing with weapons of mass destruction, chemical weapons, digital intelligence, etc.

It’s not wrong to have these teams, but the truth is that anyone working with the technology needs to be treated like they are on an ethics team, not build an “ethical group” who’s supposed to proxy the responsibility for doing it the “right way.”

Self-directed or self-aware AI also complicate this situation immeasurably, as having an ethics team presents a perfect target for a rogue AI or bad actor. You’re creating a “trusted group” with special authority for something/someone to corrupt. Not wise to create privileged attack surfaces when working with digital intelligences.

So yeah... the whole idea of "responsible AI" is just wishful thinking at best and deceptive hypocrisy at worst.

On the other hand I totally relate with the idea that it could be preferable that everyday has access to advanced AI and not just large companies and nation states.

A multimodal or segmentation algorithm is not the solution for bee-level path planning, obstacle avoidance or autonomous navigation. Getting LLMs to power a robot for household tasks with low latency to action and in an energy efficient manner is challenging enough, before talking about high-speed, highly maneuverable drones.

Not really practical at the moment of course since you can't put 8 A100s on a drone.

What is the "it" that no single entity has control over?

You have absolutely no control of what your next door neighbor is doing with open source.

Hey, if we want alcohol to be made responsibly, everyone should have their own still, made from freely redistributed blueprints. That way no single entity has control.

Anyone who wants to can, in fact, find blueprints for making their own still. For example, https://moonshinestillplans.com/ contains plans for a variety of different types of stills and guidance on which type to build based on how you want to use it.

And in fact I think it's good that this site exists, because it's very easy to build a still that appears to work but actually leaves you with a high-methanol end product.

Is it? I've always seen concern about methanol in moonshine but I presume it came from intentional contamination from evil bootleggers. It's difficult to get a wash containing enough methanol to meaningfully concentrate in the first place if you're making whiskey or rum. Maybe with fruit wine and hard cider there's a bit more.

The physics of distillation kind of have your back here too. The lower temperature fractions with acetone and methanol always come out first during distillation (the "heads") and every resource and distiller will tell you to learn the taste and smell, then throw them out. The taste and smell of heads are really distinctive. A slow distillation to more effectively concentrate methanol also makes it easier to separate out. But even if you don't separate the heads from the hearts, the methanol in any traditional wash is dilute enough that it'll only give you a headache.

I think it's extremely hard to build a still that appears to work but creates a high methanol end product.

https://www.ncbi.nlm.nih.gov/pmc/articles/PMC2972336/#:~:tex....

If you don't put barriers, how quickly will AI bots take over people in online discourse, interaction and publication?

This isn't just for the sake of keeping the Internet an interesting place free of bots and fraud and all that.

But I've also heard that it's about improving AI itself. If AI starts to pollute the dataset we train AI on, the entire Internet, you get this weird feedback loop where the models could almost get worse over time, as they will start to unknowingly train on things their older versions produced.

Great example! Yes, linux being open source has been massively beneficial to society. And this is true despite the fact that some bad guys use computers as well.

Free software means you have to be able to build the final binary from source. Having 10 TB of text is no problem, but having a data center of GPUs is. Until the training cost comes down there is no way to make it free software.

If the training data and model training code is available then it should be considered open, even if it’s hard to train.

This will never be fully open

Making it open is the only way AI fulfills a power to the people goal. Without open source and locally trainable models AI is just more power to the big-tech industry's authorities.

It's entirely conceivable that even if AGI (or something comparably significant in terms of how impactful it would be to changing society or nation states) was achievable in our lifetime, it might be that:

1) Achieving it requires a critical mass of research talent in one place that perhaps currently exists at fewer than 5 companies - anecdotally only Google, Meta, and OpenAI. And a comparable number of world governments (At least in the US the best researchers in this field are at these companies, not in academia or government. China may be different.)

This makes it sound like a "security by obscurity" situation, and on a long enough timeline it may be. Without World War 2, without the Manhattan Project, and without the looming Cold War how long would it have taken for Humanity to construct a nuclear bomb? An extra 10 years? 20? 50? Hard to know. Regardless, there is a possibility that for things like AI, with extra time comes the ability to better understand and build those defenses before they're needed.

2) It might also require an amount of computing capacity that only a dozen companies/governments have.

If you open source all the work you remove the guard rails for the growth or what people focus investments on. It also means that hostile nations like Iran or North Korea who may not have the research talent but could acquire the raw compute could utilize it for unknown goals.

Not to mention that what nefarious parties on the internet would use it for. We only know about deep fake porn and generated vocal audio of family members for extortion. Things can get much much worse.

Or not, and damaging wrongheaded ideas will become a self-reinforcing (because safety! humanity is at stake!) orthodoxy, leaving us completely butt-naked before actual risks once somebody makes a sudden clandestine breakthrough.

https://bounded-regret.ghost.io/ai-pause-will-likely-backfir...

> We don’t need to speculate about what would happen to AI alignment research during a pause—we can look at the historical record. Before the launch of GPT-3 in 2020, the alignment community had nothing even remotely like a general intelligence to empirically study, and spent its time doing theoretical research, engaging in philosophical arguments on LessWrong, and occasionally performing toy experiments in reinforcement learning.

> The Machine Intelligence Research Institute (MIRI), which was at the forefront of theoretical AI safety research during this period, has since admitted that its efforts have utterly failed. Other agendas, such as “assistance games”, are still being actively pursued but have not been significantly integrated into modern deep learning systems— see Rohin Shah’s review here, as well as Alex Turner’s comments here. Finally, Nick Bostrom’s argument in Superintelligence, that value specification is the fundamental challenge to safety, seems dubious in light of LLM's ability to perform commonsense reasoning.[2]

> At best, these theory-first efforts did very little to improve our understanding of how to align powerful AI. And they may have been net negative, insofar as they propagated a variety of actively misleading ways of thinking both among alignment researchers and the broader public. Some examples include the now-debunked analogy from evolution, the false distinction between “inner” and “outer” alignment, and the idea that AIs will be rigid utility maximizing consequentialists (here, here, and here).

> During an AI pause, I expect alignment research would enter another “winter” in which progress stalls, and plausible-sounding-but-false speculations become entrenched as orthodoxy without empirical evidence to falsify them. While some good work would of course get done, it’s not clear that the field would be better off as a whole. And even if a pause would be net positive for alignment research, it would likely be net negative for humanity’s future all things considered, due to the pause’s various unintended consequences. We’ll look at that in detail in the final section of the essay.

I thought the big secret sauce is the sources of data that is used to train the models. Without this, the model itself is useless quite literally.

At least, that's my understanding.

Both countries have access to LLMs already. And if they didn’t, they would have built their own or gotten access through corporate espionage.

What open source does is it helps us better understand & control the tech these countries use. And it helps level up our own homegrown tech. Both of these are good advantages to have.