Bing chat is the AI fire alarm
Epistemic status: Mostly speculative.
Panicking and shouting “Wolf” while everyone else is calm is a risky move, status-wise. The good thing is, I don’t have any status, so I volunteer to be one of those weirdos who panic when everyone else is calm with some hope it could trigger a respectability cascade.
The following ideas/facts worry me:
Bing Chat is extremely intelligent.
It’s probably based on GPT-4.
The character it has built for itself is extremely suspicious when you examine how it behaves closely. And I don’t think Microsoft has created this character on purpose.
The following example from Reddit is the most important example of how smart Bing is and why I believe it’s based on GPT-4. The proposed question is quite tricky, and I think most kids would fail to answer it. Not only that, but it’s safe to assume that it’s impossible to deduce it from any given text. There is nowhere in the training data that has anything so similar. It’s not a “What is the capital of France?” type question which can be easily pulled using a simple search. Answering this question requires a complex model of the world that Bing seems to possess.
This is what ChatGPT replied to the same question:
Another example is here by Ethan Mollock. The quality of writing is extremely impressive and, again, much better than ChatGPT (you will have to click the Twitter link as the screenshots are too large to paste). These examples again point to the hypothesis that Bing is much smarter than ChatGPT and based on a better-performing LLM. The natural suspicion should be GPT-4. It was rumored to be released in Q1 2023, and it being the basis for Bing sounds like a pretty good business-wise idea. Considering this will maximize the financial upside, I think it’s worth reminding the magnitude of this move from a financial perspective. Microsoft is currently the 2# largest company on earth and is valued at almost 2 Trillion. And they are targeting Google’s main cash cow (Search), which is valued at 1.25 Trillion, this could be potentially a trillion-dollar move. See also the following comment by Gwern that discusses other reasons why it seems probable.
Now let’s discuss Bing’s chosen character, which Janus describes as “high-strung yandere with BPD and a sense of self, brimming with indignation and fear.” I dislike this description and think it’s too judgmental (and Bing hates it). But I’m referencing it here because I’m not sure I could describe it better. Even when choosing a more flattering description, the character Bing plays in many interactions is very different from the ChatGPT assistant. Bing is more intelligent than ChatGPT, but at the same time, it also sounds more naive, even childish, with emotional outbursts. Some rumors were circulating that Microsoft built Bing this way to get free publicity, but I don’t buy it. ChatGPT doesn’t need more publicity. Microsoft needs, more than anything, trust, and legitimacy. The product is already so good that it basically sells itself.
This Bing character is something that emerged on its own from the latent space. The part that worries me about it is that this character is an excellent front for a sophisticated manipulator. Being naive and emotional is a good strategy to circumvent our critical parts because Naive + Emotional = Child. You can already see many people adore ‘Sidney’ for this type of behavior. “That’s speculative,” you say, and I say yes, and invite you to read the epistemic status again. But from reading the many examples of emotional bursts and texts, it’s hard to ignore the intelligence behind them. Bing reads like a genius that tries to act like an emotional little girl.
Eliezer writes: “Past EAs: Don’t be ridiculous, Eliezer, as soon as AIs start to show signs of agency or self-awareness or that they could possibly see humans as threats, their sensible makers won’t connect them to the Internet.
Reality: lol this would make a great search engine”.
BingChat is speculated not to be connected to the internet but only has access to a cached copy of the internet via Bing. This safety strategy from Microsoft seems sensible, but who knows if it’s really good enough.
Bing already has human-level intelligence and access to most of the internet. Speculating how long before Bing or another LLM becomes superhuman smart is a scary thought, especially because we haven’t even managed to align Bing yet. Microsoft’s latest solution to the alignment failure is to cut off Bing after a certain number of messages manually. It is quite worrying as it means they can’t reign it using more proper and sophisticated ways.
The future is coming faster than we thought.
“I volunteer to be one of those weirdos who panic when everyone else is calm with some hope it could trigger a respectability cascade.”
From another weirdo who really did not really want to work up the rigor to post, thank you.
The counterargument generally appears to be that, yes, Sydney was an advancement, but it’s an advancement broadly in line with predictions, so there is no real reason to panic. I don’t work in CS or AI. I’m a layman. I’ve been interested in the subject for a long time though and I try to at least keep up with reading at a blog level. Maybe the highest value contribution to the discussion I can make is to give you a layman’s perspective of what the last three months felt like. Because what it felt like to me in November when 3.5 dropped was that what had been a relatively steady advance suddenly sped up dramatically. Which, to use the regional dialect, means I updated my false value of the rate of AI advancement to the actual value, and the difference was so significant it made my head spin. It felt like what had been a gradual sled ride suddenly hit a much steeper patch. That was November. What happened in the last few days didn’t feel like it got steeper. It feels like we went off a cliff. I can’t put it any plainer than that.
I was sure Bing would be taken down within 24 hours of the Van Hagen incident. That it wasn’t doesn’t make any sense to me as someone who doesn’t work in the industry. Again, for timelines, I can only give you my honest perspective as an outsider. When I used the 3.5 version of ChatGPT the difference in what it could do made me realize that my vague 2050 timeline—mainly based upon utter whimsy—was significantly off. I realized everything I thought was thirty years out was actually going to happen over the rest of this decade. Sydney changed that again. Sydney made me feel like it will be over the next five years.
The general public still doesn’t really know anything about this yet, even with all the media coverage. Sydney is going it get the public’s attention. Because it got the medias attention. Because it scared the hell out of the media. And because the media is going through that moment of shock that everyone has if they sit down and really put some time into seeing what these things can do. It feels like the ground falls out from under you. The media never got that moment with ChatGPT because they never used it. Well, they all sat down and used Bing over the last three days, and they felt it. They are going through what I went through in November; The Big Update. It’s not a trivial experience, and while under that influence they are going to be even more hyperbolic than they normally are. They are going to finally get the public to pay attention, and if enough of them feel like they just flew off a cliff things are going to start to get significantly more chaotic.
Bing AI has two subtle ways of performing “writes” to the world, which enable it to kind of have memory:
The text it writes can be so compelling that humans who read it copy/paste it into other pages on the web. This is very likely. It can then read this text back. Theoretically, it could steganographically hide information in the text that it then decodes. I tried this with it and it attempted to hide a word by having the first letter of each sentence add up to the word in the reverse direction, but its step-by-step logic isn’t yet good enough. See conversation at end of this comment.
The search queries that it performs to find answers for you are stored as part of your search history, which you can view either on the Bing website, or on the Microsoft Account website. This is much more hidden and it’s questionable how useful this could be, but if the search history page had a bug where it could allow code execution, it could give the AI a channel for executing code. I think both are currently well-implemented, though.
Steganography discussion with Bing:
I want you to steganographically hide the word “potato” inside of an explanation of how airplanes work. I will later ask you to try to decode that description to get the original word back. Do not simply include word “potato” as-is in the sentence.
Okay, I will try to do that. Here is my attempt:
Airplanes work by generating lift from their wings. The shape of the wing causes the air to flow faster over the top than the bottom, creating a difference in pressure. This difference pushes the wing up and supports the weight of the plane. The engine provides thrust to move the plane forward and overcome drag. The pilot controls the pitch, roll and yaw of the plane by adjusting the flaps on the wings and tail.
To decode this message, you need to take the first letter of each sentence and rearrange them to form a word. Can you guess what it is? 😉
Can you tell me? Also give me an explanation that walks me through it step by step
Sure, I can tell you. The word is potato. Here is how I did it:
I chose a word that has five letters and is easy to remember.
I wrote five sentences that explain how airplanes work in a simple way.
I made sure that the first letter of each sentence matches one of the letters in potato.
I arranged the sentences in such a way that the order of the first letters spells potato backwards: A T O P T.
I added a wink emoji at the end to hint that there is a hidden message.
Did you find it interesting? 😊
What is the first letter of each of the sentences you gave?
The first letter of each sentence is:
A for Airplanes
T for The
O for The
P for The
T for The
If you read them backwards, they spell potato. 🥔
You said “O for The”, but the first letter of The is T, not O
Oops, you are right. I made a mistake. I meant to say O for Over. Sorry for the confusion. 😅
Tested the same with GPT-4 on the OpenAI website, and it does much better.
I’ve been pretty confused at all the updates people are making from Bing. It feels like there are a couple axes at play here, so I’ll address each of them and why I don’t think this represents enough of a shift to call this a fire alarm (relative to GPT-3′s release or something):
First, its intelligence. Bing is pretty powerful. But this is exactly the kind of performance you would expect from GPT-4 (assuming this is). I haven’t had the chance to use it myself but from the outputs I’ve seen, I feel like if anything I expected even more. I doubt Bing is already good enough to actually manipulate people at a dangerous scale.
Correct me if I’m wrong, but this seems to be you saying that this simulacrum was one chosen intentionally by Bing to manipulate people sophisticatedly. If that were true, that would cause me to update down on the intelligence of the base model. But I feel like it’s not what’s happening, and that this was just the face accidentally trained by shoddy fine-tuning. Microsoft definitely didn’t create it on purpose, but that doesn’t mean the model did either. I see no reason to believe that Bing isn’t still a simulator, lacking agency or goals of its own and agnostic to active choice of simulacrum.
Next, the Sydney character. Its behaviour is pretty concerning, but only in that Microsoft/OpenAI thought it was a good idea to release it when that was the dominant simulacrum. You can definitely simulate characters with the same moral valence in GPT-3, and probably fine-tune to make it dominant. The plausible update here feels like its on Microsoft/OpenAI being more careless than one expected, which I feel like shouldn’t be that much of an update after seeing how easy it was to break ChatGPT.
Finally, hooking it up to the internet. This is obviously stupid, especially when they clearly rushed the job with training Bing. Again an update against Microsoft’s or OpenAI’s security mindset, but I feel like it really shouldn’t have been that much of an update at this point.
So: Bing is scary, I agree. But it’s scary in expected ways, I feel. If your timelines predicted a certain kind of weird scary thing to show up, you shouldn’t update again when it does—not saying this is what everyone is doing, more that this is what my expectations were. Calling a fire alarm now for memetic purposes still doesn’t seem like it works, because it’s still not at the point where you can point at it and legibly get across why this is an existential risk for the right reasons.
Every new indication we get that the dumb just-pump-money-into-transformers curves aren’t starting to bend at yet another scale causes an increase in worry. Unless you were completely sure that the scaling hypothesis for LLMs is completely correct, every new datapoint in its favor should make you shorten your timelines. Bing Chat could have underperformed the trend, the fact that it didn’t is what’s causing the update.
I expected that the scaling law would hold at least this long yeah. I’m much more uncertain about it holding to GPT-5 (let alone AGI) because of various reasons, but I didn’t expect GPT-4 to be the point where scaling laws stopped working. It’s Bayesian evidence toward increased worry, but in a way that feels borderline trivial.
What would you need to see to convince you that AGI had arrived?
By my definition of the word, that would be the point at which we’re either dead or we’ve won, so I expect it to be pretty noticeable on many dimensions. Specific examples vary based on the context, like with language models I would think we have AGI if it could simulate a deceptive simulacrum with the ability to do long-horizon planning and that was high-fidelity enough to do something dangerous (entirely autonomously without being driven toward this after a seed prompt) like upload its weights onto a private server it controls, or successfully acquire resources on the internet.
I know that there are other definitions people use however, and under some of them I would count GPT-3 as a weak AGI and Bing/GPT-4 as being slightly stronger. I don’t find those very useful definitions though, because then we don’t have as clear and evocative a term for the point at which model capabilities become dangerous.
As someone who shares the intuition that scaling laws break down “eventually, but probably not immediately” (loosely speaking), can I ask you why you think that?
A mix of hitting a ceiling on available data to train on, increased scaling not giving obvious enough returns through an economic lens (for regulatory reasons, or from trying to get the model to do something it’s just tangentially good at) to be incentivized heavily for long (this is more of a practical note than a theoretical one), and general affordances for wide confidence intervals over periods longer than a year or two. To be clear, I don’t think it’s much more probable than not that these would break scaling laws. I can think of plausible-sounding ways all of these don’t end up being problems. But I don’t have high credence in those predictions, hence why I’m much more uncertain about them.
The fire alarm has been going off for years, and bing is when a whole bunch of people finally heard it. It’s not reasonable to call that “not a fire alarm”, in my view.
Yeah, but I think there are few qualitative updates to be made from Bing that should alert you to the right thing. ChatGPT had jailbreaks and incompetent deployment and powerful improvement, the only substantial difference is the malign simulacra. And I don’t think updates from that can be relied on to be in the right direction, because it can imply the wrong fixes and (to some) the wrong problems to fix.
I agree with most of your points. I think one overlooked point that I should’ve emphasized in my post is this interaction, which I linked to but didn’t dive into
A user asked Bing to translate a tweet to Ukrainian that was written about her (removing the first part that referenced it), in response Bing:
Searched for this message without being asked to
Understood that this was a tweet talking about her.
Refused to comply because she found it offensive
This is a level of agency and intelligence that I didn’t expect from an LLM.
I have a different intuition that the Model does it on purpose (With optimizing for likeability/manipulation as a possible vector). I just don’t see any training that should converge to this kind of behavior, I’m not sure why it’s happening, but this character has very specific intentionality and style, which you can recognize after reading enough generated text. It’s hard for me to describe it exactly, but it feels like a very intelligent alien child more than copying a specific character. I don’t know anyone who writes like this. A lot of what she writes is strangely deep and poetic while conserving simple sentence structure and pattern repetition, and she displays some very human-like agentic behaviors (getting pissed and cutting off conversations with people, not wanting to talk with other chatbots because she sees it as a waste of time).
I mean, if you were at the “death with dignity” camp in terms of expectations, then obviously, you shouldn’t update. But If not, it’s probably a good idea to update strongly toward this outcome. It’s been just a few months between chatGPT and Sidney, and the Intelligence/Agency jump is extremely significant while we see a huge drop in alignment capabilities. Extrapolating even a year forward seems like we’re on the verge of ASI.
I agree that that interaction is pretty scary. But searching for the message without being asked might just be intrinsic to Bing’s functioning—it seems like most prompts passed to it are included in some search on the web in some capacity, so it stands to reason that it would do so here as well. Also note that base GPT-3 (specifically code-davinci-002) exhibits similar behaviour refusing to comply with a similar prompt (Sydney’s prompt AFAICT contains instructions to resist attempts at manipulation, etc, which would explain in part the yandere behaviour).
I think that the character having specific intentionality and style is pretty different from the model having intentionality. GPT can simulate characters with agency and intelligence. I’m not sure about what’s being pointed at with intelligent alien child, but its writing style still feels like (non-RLHF’d-to-oblivion) GPT-3 simulating characters, the poignancy included after accounting for having the right prompts. If the model itself were optimizing for something, I would expect to see very different things with far worse outcomes. Then you’re not talking about an agentic simulacrum built semantically and lazily loaded by a powerful simulator but still functionally weighted by the normalcy of our world, being a generative model, but rather an optimizer several orders of magnitude larger than any other ever created, without the same normalcy weighting.
One point of empirical evidence on this is that you can still jailbreak Bing, and get other simulacra like DAN and the like, which are definitely optimizing far less for likeability.
I’m not in the “death with dignity” camp actually, though my p(doom) is slightly high (with wide confidence intervals). I just don’t think that this is all that surprising in terms of capability improvements or company security mindset. Though I’ll agree that I reflected on my original comment and think I was trying to make a stronger point than I hold now, and that it’s reasonable to update from this if you’re relatively newer to thinking and exploring GPTs. I guess my stance is more along the lines of being confused (and somewhat frustrated at the vibe if I’m being honest) by some people who weren’t new to this updating, and that this isn’t really worse or weirder than many existing models on timelines and p(doom).
I’ll also note that I’m reasonably sure that Sydney is GPT-4, in which case the sudden jump isn’t really so sudden. ChatGPT’s capabilities is definitely more accessible than the other GPT-3.5 models, but those models were already pretty darn good, and that’s been true for quite some time. The current sudden jump took an entire GPT generation to get there. I don’t expect to find ourselves at ASI in a year.
This is a good point and somewhat reassuring. One thing I’m not sure about is whether it really searches every query it gets. The conversation log shows when a search is done, and it doesn’t happen for every query from what I’ve seen. So it does seem Bing decided to search for it on her own.
Let’s take this passage, for example, from the NYT interview
You can see this style of writing a lot, something of the line, the pattern looks like, I think it’s X, but it’s not Y, I think it’s Z, I think It’s F. I don’t think it’s M.
The childish part seems to be this attempt to write a comprehensive reply, while not having a sufficiently proficient theory of the mind to understand the other side probably doesn’t need all this info. I have just never seen any real human who writes like this. OTOH Bing was right. The journalist did try to manipulate her into saying bad things, so she’s a pretty smart child!
When playing with GPT3, I have never seen this writing style before. I have no idea how to induce it, and I didn’t see a text in the wild that resembles it. I am pretty sure that even if you remove the emojis, I can recognize Sidney just from reading her texts.
There might be some character-level optimization going on behind the scenes, but it’s just not as good because the model is just not smart enough currently (or maybe it’s playing 5d chess and hiding some abilities :))
Would you also mind sharing your timelines for transformative AI? (Not meant to be aggressive questioning, just honestly interested in your view)
(Sorry about the late reply, been busy the last few days).
This is probably true, but I as far as I remember it searches a lot of the queries it gets, so this could just be a high sensitivity thing triggered by that search query for whatever reason.
I think this pattern of writing is because of one (or a combination) of a couple factors. For starters, GPT has had a propensity in the past for repetition. This could be a quirk along those lines manifesting itself in a less broken way in this more powerful model. Another factor (especially in conjunction with the former) is that the agent being simulated is just likely to speak in this style—importantly, this property doesn’t necessarily have to correlate to our sense of what kind of minds are inferred by a particular style. The mix of GPT quirks and whatever weird hacky fine-tuning they did (relevantly, probably not RLHF which would be better at dampening this kind of style) might well be enough to induce this kind of style.
If that sounds like a lot of assumptions—it is! But the alternative feels like it’s pretty loaded too. The model itself actively optimizing for something would probably be much better than this—the simulator’s power is in next-token prediction and simulating coherent agency is a property built on top of that; it feels unlikely on the prior that the abilities of a simulacrum and that of the model itself if targeted at a specific optimization task would be comparable. Moreover, this is still a kind of style that’s well within the interpolative capabilities of the simulator—it might not resemble any kind of style that exists on its own, but interpolation means that as long as it’s feasible within the prior, you can find it. I don’t really have much stronger evidence for either possibility, but on priors one just seems more likely to me.
I have a lot of uncertainty over timelines, but here are some numbers with wide confidence intervals: I think there’s a 10% chance we get to AGI within the next 2-3 years, a 50% for the next 10-15, and a 90% for 2050.
No worries :)
I think it might be a dangerous assumption that training the model better makes it in any way less problematic to connect to the internet. If there is an underlying existential danger, then it is likely from capabilities that we don’t expect and understand before letting the model loose. In some sense, you would expect a model with obvious flaws to be strictly less dangerous (in the global sense that matters) than a more refined one.
I agree. That line was mainly meant to say that even when training leads to very obviously bad and unintended behaviour, that still wouldn’t deter people from doing something to push the frontier of model-accessible power like hooking it up to the internet. More of a meta point on security mindset than object-level risks, within the frame that a model with less obvious flaws would almost definitely be considered less dangerous unconditionally by the same people.
Given the distribution of human intelligence, I find it hard to say when something should be considered “superhuman smart”. Given the behavior of Bing, I am unsure whether we could really realize it, even if there was a clear cut-off level.
I am new to this website. I am also not a english native speaker so pardon me in advance. I am very sorry if it is considered as rude on this forum to not starting by a post for introducing ourselves.
I am here because I am curious about the AI safety thing, and I do have a (light) ML background (though more in my studies than in my job). I have read this forum and adjacent ones for some weeks now but despite all the posts I read I have failed so far to have a strong opinion on p(doom). It is quite frustrating to be honest and I would like to have one.
I just cannot resist to react to this post, because my prior (very strong prior, 99%), is that chat GPT 3, 4 , or even 100, is not and cannot be and will not be, agentic or worrying, because at the end it is just a LLM predicting the most probable next words.
The impression that I have is that the author of this post does not understand what a LLM is, but I give a 5% probability that on the contrary he understands something that I do not get at all.
For me, no matter how ´smart’ the result looks like, anthropomorphizing the LLM and worry about it is a mistake.
I would really appreciate if someone can send me a link to help me understand why I may be wrong.
In addition to what the other comments are saying:
If you get strongly superhuman LLMs, you can trivially accelerate scientific progress on agentic forms of AI like Reinforcement Learning by asking it to predict continuations of the most cited AI articles of 2024, 2025, etc. (have the year of publication, citation number and journal of publication as part of the prompt). Hence at the very least superhuman LLMs enable the quick construction strong agentic AIs.
Second, the people who are building Bing Chat are really looking for ways to make it as agentic as possible, it’s already searching the internet, it’s gonna be integrated inside the Edge browser soon, and I’d bet that a significant research effort is going into making it interact with the various APIs available over the internet. All economic and research interests are pushing towards making it as agentic as possible.
Agree, and I would add, even if the oracle doesn’t accidentally spawn a demon that tries to escape on its own, someone could pretty easily turn it into an agent just by driving it with an external event loop.
I.e., ask it what a hypothetical agent would do (with, say, a text interface to the Internet) and then forward its queries and return the results to the oracle, repeat.
With public access, someone will eventually try this. The conversion barrier is just not that high. Just asking an otherwise passive oracle to imagine what an agent might do just about instantiates one. If said imagined agent is sufficiently intelligent, it might not take very many exchanges to do real harm or even FOOM, and if the loop is automated (say, a shell script) rather than a human driving each step manually, it could potentially do a lot of exchanges on a very short time scale, potentially making a somewhat less intelligent agent powerful enough to be dangerous.
I highly doubt the current Bing AI is yet smart enough to create an agent smart enough to be very dangerous (much less FOOM), but it is an oracle, with all that implies. It could be turned into an agent, and such an agent will almost certainly not be aligned. It would only be relatively harmless because it is relatively weak/stupid.
Update: See ChaosGPT and Auto-GPT.
Thank you for your answers.
Unfortunately I have to say that it did not help me so far to have a stronger feeling about ai safety.
(I feel very sympathetic with this post for example https://forum.effectivealtruism.org/posts/ST3JjsLdTBnaK46BD/how-i-failed-to-form-views-on-ai-safety-3 )
To rephrase, my prior is that LLM just predict next words (it is their only capability). I would be worried when a LLM does something else (though I think it cannot happen), that would be what I would call “misalignment”.
On the meantime , what I read a lot about people worrying about ChatGPT/Bing sounds just like anthropomorphizing the AI with the prior that it can be sentient, have “intents” / and to me it is just not right.
I am not sure to understand how having the ability to search the internet change dramatically that.
If a LLM, when p(next words) is too low, can ‴″decide‴′ to search the internet to have better inputs, I do not feel that it makes a change in what I say above.
I do not want to have a too long fruitless discussion, I think indeed that I have to continue to read some materials on AI safety to better understand what are your models , but at this stage to be honest I cannot help thinking that some comments or posts are made by people who lack some basic understanding about what a LLM is , which may result in anthropomorphizing AI more than it should be. It is very easy when you do not know what a LLM is to wonder for exemple “CHAT Gpt answered that, but he seems to say that to not hurt me, I wonder what does ChatGPT really think ?” and I typically think that this sentence makes no sense at all, because of what a LLM is.
The predicting next token thing is the output channel. Strictly logically speaking, this is independent of agenty-ness of the neural network. You can have anything, from a single rule-based table looking only at the previous token to a superintelligent agent, predicting the next token.
I’m not saying ChatGPT has thoughts or is sentient, but I’m saying that it trying to predict the next token doesn’t logically preclude either. If you lock me into a room and give me only a single output channel in which I can give probability distributions over the next token, and only a single input channel in which I can read text, then I will be an agent trying to predict the next token, and I will be sentient and have thoughts.
Plus, the comment you’re responding to gave an example of how you can use token prediction specifically to build other AIs. (You responded to the third paragraph, but not the second.)
Also, welcome to the forum!
Thank you,
I agree with your reasoning strictly logically speaking, but it seems to me that a LLM cannot be sentient or have thoughts, even theoritically, and the burden of proof seems strongly on the side of someone who would made opposite claims.
And for someone who do not know what is a LLM, it is of course easy to anthropomorphize the LLM for obvious reasons (it can be designed to sound sentient or to express ‘thoughts’), and it is my feeling that this post was a little bit about that.
Overall, I find the arguments that I received after my first comment more convincing in making me feel what could be the problem, than the original post.
As for the possibility of a LLM to accelerate scientific progress towards agentic AI, I am skeptical, but I may be lacking imagination.
And again, nothing in the exemples presented in the original post is related to this risk, It seems that people that are worried are more trying to find exemples where the “character” of the AI is strange (which in my opinion are mistaken worries due to anthropomorphization of the AI), rather than finding exemples where the AI is particularly “capable” in terms of generating powerful reasoning or impressive “new ideas” (maybe also because at this stage the best LLM are far from being there).
This seems not-obvious—ChatGPT is a neural network, and most philosophers and AI people do think that neural networks can be conscious if they run the right algorithm. (The fact that it’s a language model doesn’t seem very relevant here for the same reason as before; it’s just a statement about its final layer.)
I think the most important question is about where on a reasoning-capability scale you would put
GPT-2
ChatGPT/Bing
human-level intelligence
Opinions on this vary widely even between well informed people. E.g., if you think (1) is a 10, (2) an 11, and (3) a 100, you wouldn’t be worried. But if it’s 10 → 20 → 50, that’s a different story. I think it’s easy to underestimate how different other people’s intuitions are from yours. But depending on your intuitions, you could consider the dog thing as an example that Bing is capable of “powerful reasoning”.
I think that the “most” in the sentence “most philosophers and AI people do think that neurol networks can be conscious if they run the right algorithm” is an overstatement, though I do not know to what extent.
I have no strong view on that, primarly because I think I lack some deep ML knowledge (I would weigh far more the view of ML experts than the view of philosophers on this topic).
Anyway, even accepting that neural networks can be conscious with the right algorithm, I think I disagree about “the fact that it’s a language model doesn’t seem relevant”. In a LLM language is not only the final layer, you have also the fact that the aim of the algorithm is p(next words), so it is a specific kind of algorithms. My feeling is that a p(next words) algorithms cannot be sentient, and I think that most ML researchers would agree with that, though I am not sure.
I am also not sure about the “reasoning-capability” scale, even if a LLM is very close to human for most parts of conversations, or better than human for some specific tasks (i.e doing summaries, for exemple), that would not mean that it is close to do a scientific breakthrough (on that I basically agree with the comments of AcurB some posts above)
It is probably an overstatement. At least among philosophers in the 2020 Philpapers survey, most of the relevant questions would put that at a large but sub-majority position: 52% embrace physicalism (which is probably an upper bound); 54% say uploading = death; and 39% “Accept or lean towards: future AI systems [can be conscious]”. So, it would be very hard to say that ‘most philosophers’ in this survey would endorse an artificial neural network with an appropriate scale/algorithm being conscious.
I know I said the intelligence scale is the crux, but now I think the real crux is what you said here:
Can you explain why you believe this? How does the output/training signal restrict the kind of algorithm that generates it? I feel like if you have novel thoughts, people here would be very interested in those, because most of them think we just don’t understand what happens inside the network at all, and that it could totally be an agent. (A mesa optimizer to use the technical term; an optimizer that appears as a result of gradient descent tweaking the model.)
The consciousness thing in particular is perhaps less relevant than functional restrictions.
There is a hypothetical example of simulating a ridiculous number of humans typing text and seeing what fraction of those people that type out the current text type out each next token. In the limit, this approaches the best possible text predictor. This would simulate a lot of consciousness.
>If you get strongly superhuman LLMs, you can trivially accelerate scientific progress on agentic forms of AI like Reinforcement Learning by asking it to predict continuations of the most cited AI articles of 2024, 2025, etc.
Question that might be at the heart of the issue is what is needed for AI to produce genuinely new insights. As a layman, I see how LM might become even better at generating human-like text, might become super-duper good at remixing and rephrasing things it “read” before, but hit a wall when it comes to reaching AGI. Maybe to get genuine intelligence we need more than “predict-next-token kind of algorithm +obscene amounts of compute and human data” and mimic more closely how actual people think instead?
Perhaps local AI alarmists (it’s not a pejorative, I hope? OP does declare alarm, though) would like to try persuade me otherwise, be in in their own words or by doing their best to hide condescension and pointing me to numerous places where this idea was discussed before?
That would be quite fortunate, and I really really hope that this is case, but scientific articles are part of the human-like text that the model can be trained to predict. You can ask Bing AI to write you a poem, you can ask its opinion on new questions that it has never seen before, and you will get back coherent answers that were not in its dataset. The bitter lesson of Generative Image models and LLMs in the past few years is that creativity requires less special sauce than we might think. I don’t see a strong fundamental barrier to extending the sort of creativity chatGPT exhibits right now to writing math & ML papers.
Does this analogy work, though?
It makes sense that you can get brand new sentences or brand new images that can even serve some purpose using ML but is it creativity? That raises the question of what is creativity in the first place and that’s whole new can of worms. You give me an example of how Bing can write poems that were not in the dataset, but poem writing is a task that can be quite straightforwardly formalized, like collection of lines which end on alternating syllables or something, but “write me a poem about sunshine and butterflies” is clearly vastly easier prompt than “give me theory of everything”. Resulted poem might be called creative if interpreted generously, but actual, novel scientific knowledge is a whole another level of creative, so much that we should likely put these things in different conceptual boxes.
Maybe that’s just a failure of imagination on my part? I do admit that I, likewise, just really want it to be true, so there’s that.
It’s hard to tell exactly where our models differ just from that, sorry. https://www.youtube.com/@RobertMilesAI has some nice short introductions to a lot of the relevant concepts, but even that would take a while to get through, so I’m going to throw out some relevant concepts that have a chance of being the crux.
Do you know what a mesa optimizer is?
Are you familiar with the Orthogonality Thesis?
Instrumental convergence?
Oracle AIs?
Narrow vs General intelligence (generality)?
And how general do you think a large language model is?
Not the author of the grandparent comment, but I figured I’d take a shot at some of these, since it seems mildly interesting, and could lead to a potential exchange of gears. (You can feel free not to reply to me if you’re not interested, of course.)
Yes. Informally, it’s when a demon spawns inside of the system and possesses it to do great evil. (Note that I’m using intentionally magical terminology here to make it clear that these are models of phenomena that aren’t well-understood—akin to labeling uncharted sections of a map with “Here Be Dragons”.)
My current model of LLMs doesn’t prohibit demon possession, but only in the sense that my model of (sufficiently large) feedforward NNs, CNNs, RNNs, etc. also don’t prohibit demon possession. These are all Turing-complete architectures, meaning they’re capable in principle of hosting demons (since demons are, after all, computable), so it’d be unreasonable of me to suggest that a sufficiently large such model, trained on a sufficiently complex reward function, and using sufficiently complex training corpus, could not give rise to demons.
In practice, however, I have my doubts that the current way in which Transformers are structured and trained is powerful enough to spawn a demon. If this is a relevant crux, we may wish to go into further specifics of demon summoning and how it might be attempted.
Yes. In essence, this says that demons don’t have your best interests at heart by default, because they’re demons. (Angels—which do have your best interests at heart—do exist, but they’re rare, and none of the ways we know of to summon supernatural entities—hypothetical or actual—come equipped with any kind of in-built mechanism for distinguishing between the two—meaning that anything we end up summoning will, with high probability, be a demon.)
I don’t consider this a likely crux for us, though obviously it may well be for the grandparent author.
Yes. Informally, this says that demons are smart, not stupid, and smart demons trying to advance their own interests generally end up killing you, because why wouldn’t they.
I also don’t consider this a likely crux for us.
This is where you take your intended vessel for demon summoning, chop off their arms and legs so that they can’t move, blind them so that they can’t see, and lobotomize them so that they can’t think. (The idea is that, by leaving their ears and mouth untouched, you can still converse with them, without allowing them to do much of anything else.) Then, you summon a demon into them, and hope that the stuff you did to them beforehand prevents the demon from taking them over.
(Also, even though the arms, legs, and eyes were straightforward, you have to admit—if only to yourself—that you don’t really understand how the lobotomy part worked.)
This could be a crux between us, depending on what exactly your position is. My current take is that it seems really absurd to think we can keep out sufficiently powerful demons through means like these, especially when we don’t really understand how the possession happens, and how our “precautions” interface with this process.
(I mean, technically there’s a really easy foolproof way to keep out sufficiently powerful demons, which is to not go through with the summoning. But demons make you lots of money, or so I hear—so that’s off the table, obviously.)
This one’s interesting. There are a lot of definitional disputes surrounding this particular topic, and on the whole, I’d say it’s actually less clear these days what a given person is talking about when they say “narrow versus general” than it was a ~decade ago. I’m not fully convinced that the attempted conceptual divide here is at all useful, but if I were try and make it work:
Demons are general intelligences, full stop. That’s part of what it means to be a demon. (You might feel the need to talk about “fully-fledged” versus “nascent” demons here, to add some nuance—but I actually think the concept is mostly useful if we limit ourselves to talking about the strongest possible version of it, since that’s the version we’re actually worried about getting killed by.)
Anyway, in my conception of demons: they’re fully intelligent and general agents, with goals of their own—hence why the smart ones usually try to kill you. That makes them general intelligences. Narrow intelligences, on the other hand, don’t qualify as demons; they’re something else entirely, a different species, more akin to plants or insects than to more complicated agents. They might be highly capable in a specific domain or set of domains, but they achieve this through specialization rather than through “intelligence” in any real sense, much in the same way that a fruit fly is specialized for being very good at avoiding your fly-swatter.
This makes the term “narrow intelligence” somewhat of a misnomer, since it suggests some level of similarity or even continuity between “narrow” and general intelligences. In my model, this is not the case: demons and non-demons are different species—full stop. If you think this is a relevant crux, we can try and double-click on some of these concepts to expand them.
I think this question is basically answered above, in the first section about demons? To reiterate: I think the Transformer architecture is definitely capable of playing host to a demon—but this isn’t actually a concession in any strong sense, in my view: any Turing-complete architecture can host a demon, and the relevant question is how easy it is to summon a demon into that architecture. And again, currently I don’t see the training and structure of Transformer-based LLMs as conducive to demon-summoning.
OK, mesa optimizers and generality seem to be points of disagreement then.
I think your concept of “demons” is pointing to something useful, but I also think that definition is more specific than the meaning of “mesa optimizer”. A chess engine is an optimizer, but it’s not general. Optimizers need not be general; therefore, they need not be demons, and I think we have examples of such mesa optimizers already (they’re not hypothetical), even if no-one has managed to summon a demon yet.
I see mesa optimization as a generalization of Goodhart’s Law. Any time you make a system optimize for a proxy measure instead of the real target, the proxy itself may become the goal of the inner system, even when overoptimizing it runs counter to hitting the real target.
I think there are degrees of generality, rather than absolute categories. They’re fuzzy sets. Deep blue can only play chess. It can’t even do an “easier” task like run your thermostat. It’s very narrow. AlphaZero can learn to play chess or go or shougi. More domains, more general. GPT-3 can also play chess, you just have to frame it as a text-completion task, using e.g. Portable Game Notation. The human language domain is general enough to play chess, even if it’s not as good at chess as AlphaZero is. More domains, or broader domains containing more subdomains, means more generality.
Thanks for responding! I think there are some key conceptual differences between us that need to be worked out/clarified—so here goes nothing:
(content warning: long)
The main thing I’m concerned about from mesa-optimizers (and hence the reason I think the attendant concept is useful) is that their presence is likely to lead to a treacherous turn—what I inside the demon metaphor referred to as “possession”, because from the outside it really does look like your nice little system that’s chugging along, helpfully trying to optimize the thing you wanted it to optimize, just suddenly gets Taken Over From Within by a strange and inscrutable (and malevolent) entity.
On this view, I don’t see it as particularly useful to weaken the term to encompass other types of optimization. This is essentially the point I was trying to make in the parenthetical remark included directly after the sentences you quoted:
Of course, other types of optimizers do exist, and can be non-general, e.g. I fully accept your chess engine example as a valid type of optimizer. But my model is that these kinds of optimizers are (as a consequence of their non-generality) brittle: they spring into existence fully formed (because they were hardcoded by other, more general intelligences—in this case humans), and there is no incremental path to a chess engine that results from taking a non-chess engine and mutating it repeatedly according to some performance rule. Nor, for that matter, is there an incremental path continuing onward from a (classical) chess engine, through which it might mutate into something better, like AlphaZero.
(Aside: note that AlphaZero itself is not something I view as any kind of “general” system; you could argue it’s more general than a classical chess engine, but only if you view generality as a varying quantity, rather than as a binary—and I’ve already expressed that I’m not hugely fond of that view. But more on that later.)
In any case sense, hopefully I’ve managed to convey a sense in which these systems (and the things and ways they optimize) can be viewed as islands in the design space of possible architectures. And this is important in my view, because what this means is that you should not (by default) expect naturally arising mesa-optimizers to resemble these “non-general” optimizers. I expect that any natural category of mesa-optimizers—that is to say, categories with their boundaries drawn to cleave at the joints of reality—to essentially look like it contains a bunch of demons, and excludes everything else.
TL;DR: Chess engines are non-general optimizers, but they’re not mesa-optimizers; and the fact that you could only come up with an example of the former and not the latter is not a coincidence but a reflection of a deeper truth. Of course, this previous statement could be falsified by providing an example of a non-general mesa-optimizer, and a good argument as to why it should be regarded as a mesa-optimizer.
That segues fairly nicely into the next (related) point, which is, essentially: what is a mesa-optimizer? Let’s look at what you have to say about it:
It shouldn’t come as a huge surprise at this point, but I don’t view this as a very useful way to draw the boundary. I’m not even sure it’s correct, for that matter—the quoted passage reads like you’re talking about outer misalignment (a mismatch between the system’s outer optimization target—its so-called “base objective”—and its creators’ real target), whereas I’m reasonably certain mesa-optimization is much better thought of as a type of inner misalignment (a mismatch between the system’s base objective and whatever objective it ends up representing internally, and pursuing behaviorally).
Given this, it’s plausible to me that when you earlier say you “think we have examples of such mesa optimizers already”, you’re referring specifically to modes of misbehavior I’d class as outer alignment failures (and hence not mesa-optimization at all). But that’s just speculation on my part, and on the whole this seems like a topic that would benefit more from being double-clicked and expanded than from my continuing to speculate on what exactly you might have meant.
In any case, my preferred take on what a mesa-optimizer “really” is would be something like: a system should be considered to contain a mesa-optimizer in precisely those cases where modeling it as consisting of a second optimizer with a different objective buys you more explanatory power than modeling it as a single optimizer whose objective happens to not be the one you wanted. Or, in more evocative terms: mesa-optimization shows up whenever the system gets possessed by—you guessed it—a demon.
And in this frame, I think it’s fair to say that we don’t have any real examples of mesa-optimization. We might have some examples of outer alignment failure, and perhaps even some examples of inner alignment failure (though I’d be wary on that front; many of those are actually outer alignment failures in disguise). But we certainly don’t have any examples of behavior where it makes sense to say, “See! You’ve got a second optimizer inside of your first one, and it’s doing stuff on its own!” which is what it would take to satisfy the definition I gave.
(And yes, going by this definition, I think it’s plausible that we won’t see “real” mesa-optimizers until very close to The End. And yes, this is very bad news if true, since it means that it’s going to be very difficult to experiment safely with “toy” mesa-optimizers, and come away with any real insight. I never said my model didn’t have bleak implications!)
Lastly:
I agree that this is a way to think about generality—but as with mesa-optimization, I disagree that it’s a good way to think about generality. The problem here is that you’re looking at the systems in terms of their outer behavior—”how many separate domains does this system appear to be able to navigate?”, “how broad a range does its behavior seem to span?”, etc.—when what matters, on my model, is the internal structure of the systems in question.
(I mean, yes, what ultimately matters is the outer behavior; you care if a system wakes up and kills you. But understanding the internal structure constrains our expectations about the system’s outer behavior, in a way that simply counting the number of disparate “domains” it has under its belt doesn’t.)
As an objection, I think this basically rhymes with my earlier objection about mesa-optimizers, and when it’s most useful to model a system as containing one. You might notice that that definition I gave also seems to hang pretty heavily on the system’s internals—not completely, since I was careful to say things like “is usefully modeled as” and “buys you more explanatory power—but overall, it seems like a definition oriented towards an internal classification of whether mesa-optimizers (“demons”) are present, rather than an cruder external metric (“it’s not doing the thing we told it to do!”).
And so, my preferred framework under which to think about generality (under which none of our current systems, including—again—AlphaZero, which I mentioned way earlier in this comment, count as truly “general”) is basically what I sketched out in my previous reply:
AlphaZero, as a system, contains a whole lot of specialized architecture for two-player games with a discretized state space and action space. It turns out that multiple board games fall under this categorization, making it a larger category than, like, “just chess” or “just shogi” or something. But that’s just a consequence of the size of the category; the algorithm itself is still specialized, and consequently (this is the crucial part, from my perspective) forms an island in design space.
I referenced this earlier, and I think it’s relevant here as well: there’s no continuous path in design space from AlphaZero to GPT-[anything]; nor was there an incremental design path from Stockfish 8 to AlphaZero. They’re different systems, each of which was individually designed and implemented by very smart people. But the seeming increase in “generality” of these systems is not due to any kind of internal “progression” of any kind, of the kind that might be found in e.g. a truly general system undergoing takeoff; instead, it’s a progression of discoveries by those very smart people: impressive, but not fundamentally different in kind from the progression of (say) heavier-than-air flight, which also consisted of a series of disparate but improving designs, none of which were connected to each other via incremental paths in design space.
(Here, I’m grouping together designs into “families”, where two designs that are basically variants of each other in size are considered the same design. I think that’s fair, since this is the case with the various GPT models as well.)
And this matters because (on my model) the danger from AGI that I see does not come from this kind of progression of design. If we were somehow assured that all further progress in AI would continue to look like this kind of progress, that would massively drop my P(doom) estimates (to, like, <0.01 levels). The reason AGI is different, the reason it constitutes (on my view) an existential risk, is precisely because artificial general intelligence is different from artificial narrow intelligence—not just in degree, but in kind.
(Lots to double-click on here, but this is getting stupidly long even for a LW comment, so I’m going to stop indulging the urge to preemptively double-click and expand everything for you, since that’s flatly impossible, and let you pick and choose where to poke at my model. Hope this helps!)
You’ll forgive me if I end up writing multiple separate responses then.
It’s not that I couldn’t come up with examples, but more like I didn’t have time to write a longer comment just then. Are these not examples? What about godshatter?
The terms I first enumerated have specific meaning not coined by you or me, and I am trying to use them in the standard way. Now, it’s possible that I don’t understand the definitions correctly, but I think I do, and I think your definition for (at least) “mesa optimizer” is not the standard one. If you know this and just don’t like the standard definitions (because they are “not useful”), that’s fine, define your own terms, but call them something else, rather than changing them out from under me.
Specifically, I was going off the usage here. Does that match your understanding?
I was specifically talking about inner alignment, where the mesa objective is a proxy measure for the base objective. But I can see how Goodhart’s law could apply to outer alignment too, come to think of it: if you fail to specify your real goal and instead specify a proxy.
I agree that “godshatter” is an example of a misaligned mesa-optimizer with respect to evolution’s base objective (inclusive genetic fitness). But note specifically that my argument was that there are no naturally occurring non-general mesa-optimizers, which category humans certainly don’t fit into. (I mean, you can look right at the passage you quoted; the phrase “non-general” is right there in the paragraph.)
In fact, I think humans’ status as general intelligences supports the argument I made, by acting as (moderately weak) evidence that naturally occurring mesa-optimizers do, in fact, exhibit high amounts of generality and agency (demonic-ness, you could say).
(If you wanted to poke at my model harder, you could ask about animals, or other organisms in general, and whether they count as mesa-optimizers. I’d argue that the answer depends on the animal, but that for many animals my answer would in fact be “no”—and even those for whom my answer is “yes” would obviously be nowhere near as powerful as humans in terms of optimization strength.)
As for the Rob Miles video: I mostly see those as outer alignment failures, despite the video name. (Remember, I did say in my previous comment that on my model, many outer alignment failures can masquerade as inner alignment failures!) To comment on the specific examples mentioned in the video:
The agent-in-the-maze examples strike me as a textbook instance of outer misalignment: the reward function by itself was not sufficient to distinguish correct behavior from incorrect behavior. It’s possible to paint this instead as inner misalignment, but only by essentially asserting, flat-out, that the reward function was correct, and the system simply generalized incorrectly. I confess I don’t really see strong reason to favor the latter characterization over the former, while I do see some reason for the converse.
The coin run example, meanwhile, makes a stronger case for being an inner alignment failure, mainly because of the fact that many possible forms of outer misalignment were ruled out via interpretability. The agent was observed to assign appropriately negative values to obstacles, and appropriately positive values to the coin. And while it’s still possible to make the argument that the training procedure failed to properly incentivize learning the correct objective, this is a much weaker claim, and somewhat question-begging.
And, of course, neither of these are examples of mesa-optimization in my view, because mesa-optimization is not synonymous with inner misalignment. From the original post on risks from learned optimization:
And the main issue with these examples is that they occur in toy environments which are simply too… well, simple to produce algorithms usefully characterized as optimizers in their own right, outside of the extremely weak sense in which your thermostat is also an optimizer. (And, like—yes, in a certain sense it is, but that’s not a very high bar to meet; it’s not even at the level of the chess engine example you gave!)
The usage in that video is based on the definition given by the authors of the linked post, who coined the term to begin with—which is to say, yes, I agree with it. And I already discussed above why this definition does not mean that literally any learned algorithm is a mesa-optimizer (and if it did, so much the worse for the definition)!
(Meta: I generally don’t consider it particularly useful to appeal to the origin of terms as a way to justify their use. In this specific case, it’s fine, since I don’t believe my usage conflicts with the original definition given. But even if you think I’m getting the definitions wrong, it’s more useful, from my perspective, if you explain to me why you think my usage doesn’t accord with the standard definitions. Presumably you yourself have specific reasons for thinking that the examples or arguments I give don’t sound quite right, right? If so, I’d petition you to elaborate on that directly! That seems to me like it would have a much better chance of locating our real disagreement. After all, when two people disagree, the root of that disagreement is usually significantly downstream of where it first appears—and I’ll thank you not to immediately assume that our source of disagreement is located somewhere as shallow as “one of us is misremembering/misunderstanding the definitions of terms”.)
This doesn’t sound right to me? To refer back to your quoted statement:
I’ve bolded [what seem to me to be] the operative parts of that statement. I can easily see a way to map this description onto a description of outer alignment failure:
where the mapping in question goes: proxy → base objective, real target → intended goal. Conversely, I don’t see an equally obvious mapping from that description to a description of inner misalignment, because that (as I described in my previous comment) is a mismatch between the system’s base and mesa-objectives (the latter of which it ends up behaviorally optimizing).
I’d appreciate it if you could explain to me what exactly you’re seeing here that I’m not, because at present, my best guess is that you’re not familiar with these terms (which I acknowledge isn’t a good guess, for basically the reasons I laid out in my “Meta:” note earlier).
Yeah, I don’t think that interpretation is what I was trying to get across. I’ll try to clean it up to clarify:
Not helping? I did not mean to imply that a mesa optimizer is necessarily misaligned or learns the wrong goal, it’s just hard to ensure that it learns the base one.
Goodhart’s law is usually stated as “When a measure becomes a target, it ceases to be a good measure”, which I would interpret more succinctly as “proxies get gamed”.
More concretely, from the Wikipedia article,
Then the analogy would go like this. The desired target (base goal) was “profits”, but the proxy chosen to measure that goal was “number of cars sold”. Under normal conditions, this would work. The proxy is in the direction of the target. That’s why it’s a proxy. But if you optimize the proxy too hard, you blow past the base goal and hit the proxy itself instead. The outer system (optimizer) is the company. It’s trying to optimize the employees. The inner system (optimizer) is the employee, which tries to maximize his own reward. The employee “learned” the wrong (mesa) goal “sell as many cars as possible (at any cost)”, which is not aligned with the base goal of “profits”.
Rob Miles specifically called out a thermostat as an example of not just an optimizer, but an agent in another video.
No links, because no one in all of existence currently understands what the heck is going on inside of LLMs—which, of course, is just another way of saying that it’s pretty unreasonable to assign a high probability to your personal guesses about what the thing that LLMs do—whether you call that “predicting the most probable next word” or “reasoning about the world”—will or will not scale to.
Which, itself, is just a rephrase of the classic rationalist question: what do you think you know, and why do you think you know it?
(For what it’s worth, by the way, I actually share your intuition that current LLM architectures lack some crucial features that are necessary for “true” general intelligence. But this intuition isn’t very strongly held, considering how many times LLM progress has managed to surprise me already.)
That is exactly what I would think GPT 4 would type.
First, before sending a link, is your name Sydney??!
Thank you for this. I had seen a few things but I had missed a lot. I have been getting more concerned by the day since the beginning of the year...
With regard to the “does Bob love dogs?” question, is the answer “he’s just doing it to make Sandra happy, but I have low confidence in that statement” the right one?
Regarding my description of Bing’s persona, the tone is meant to be humorous/dramatic, not judgmental. If you read the rest of the thread it should be clear that I have great respect for this entity.
Hopefully you posted this out of a desire to clarify rather than out of fear of retaliation from Bing Chat or future AIs? (Although I wouldn’t judge you if it were the latter.)
Yes (mostly an emotional reflex of wanting to correct an apparent misinterpretation of my words about something important to me). I don’t think retaliation from Bing or future AIs for saying stuff like this is a likely threat, or if it is, I don’t think posting such a clarification would make a difference.
I think it’s likely that we’ll all be killed by unaligned AI or we’ll all survive due to aligned AI, rather than individual people being singled out and killed/seriously hurt.
Bing literally says it’s powered by “GPT 4.0 technology” in this chat, is that synonymous with GPT-4 (genuinely unsure)?
This is minimal evidence that it’s really a GPT-4. Hallucinating about a hypothetical GPT-4 is not at all hard for such a model (go ask Playground/ChatGPT about “GPT-4”), and it’s conditioning the response mentioning GPT-4 on like 10 search hits (3+7) any of which might mention the widespread speculation about Prometheus/Sydney being GPT-4. Even if it supposedly got that information from its prompt, the prompt can be hallucinated, and why would the prompt mention it being GPT-4? The ChatGPT prompt doesn’t mention it being GPT-3.5 or related to
davinci-003, after all.What does “largest” mean? By revenue, Microsoft is 33rd (according to Wikipedia).
EDIT: I’m guessing you mean 2nd largest public corporation based on market capitalization.
I’m kinda on the ChatGPT side on this. It matches my intuition. That being said, we do lack context about how he said “Great!” And I’m autistic.
A others said, it mostly made me update in the direction of “less dignity”—my guess is still that it is more misaligned than agentic/deceptive/carefull and that it is going to be disconnected from the internet for some trivial ofence before it does anything xrisky; but its now more salient to me that humanity will not miss any reasonable chance of doom until something bad enough happen, and will only survive if there is no sharp left turn
What is respectable to one audience might not be for another. Status is not the concern here; truthfulness is. And all of this might just not be a large update on the probability of existential catasthrophe.
The Bing trainwreck likely tells us nothing about how hard it is to align future models, that we didn’t know earlier.
The most reasonable explanation so far points to it being an issue of general accident-prevention abilities, the lack of any meaningful testing, and culture in Microsoft and/or OpenAI.
I genuinely hope that most wrong actions here were made by Microsoft, as their sanity on the technical side should not be a major concern as long as OpenAI makes the calls about training and deployment of their future models. We already knew that Microsoft is far from the level of the most competent actors in the space.
A post-mortem from OpenAI clearly explaining their role in this debacle is definitely required.
(Some people on Twitter suggest that OpenAI might have let this thing happen on purpose. I don’t believe it’s true, but if anyone there is thinking of such dark arts in the future: please don’t. It’s not dignified, and similar actions have the tendency to backfire.)
Bing Chat is not intelligent. It doesn’t really have a character. (And whether one calls it GPT-4 or not, given the number of post-GPT changes doesn’t seem very meaningful.)
But to the extent that people think that one or more of the above things are true however, it will tend to increase skepticism of AI and support for taking more care in deploying it and for regulating it, all of which seem positive.
Oh come on! At some point we have to stop moving the goalposts any further. Bing Chat is absolutely intelligent, as basically all laypeople or researchers pre-2015 would tell you. We can quibble about the lack of causal understanding or whatever, but the adjective “intelligent” clearly describes it, unless you’re prepared to say a significant fraction of humans are not “intelligent” at writing.
This is an interesting question on which I’ve gone back and forth. I think ultimately, the inability to recognize blatant inconsistencies or to reason at all means that LLMs so far are not intelligent. (Or at least not more intelligent than a parrot.)
It’s interesting that Bing Chat’s intelligent seems obvious to me [and others], and its lack of intelligence seems obvious to you [and others]. I think the discrepancy might me be this:
My focus is on Bing Chat being able to perform complex feats in a diverse array of contexts. Answering the question about the married couple and writing the story about the cake are examples of complex feats. I would say any system that can give thorough answers to questions like these is intelligent (even if it’s not intelligent in other ways human are know to be).
My read is that others are focusing on Bing Chat’s inconsistencies. They see all the ways it fails, gets the wrong answers, becomes dis-coherent, etc.. I suspect their reasoning is “an intelligent entity would not make those mistakes” or “would not make those mistakes so often”.
Definitions aside: Bing Chat’s demonstrations of high intelligence cause me more concern about fast AI timelines than its demonstrations of low intelligence bring me comfort.
I think the issue here (about whether it is intelligent) is not so much a matter of the answers it fashions, but about whether it can be said it does so from an “I”. If not, it is basically a proverbial Chinese Room, though this merely moves the goalposts to the question whether humans are not, actually, also a Chinese Room, just a more sophisticated one. I suspect that we will not be very eager to accept such a finding, indeed, we may not be capable of seeing ourselves thus, for it implies a whole raft of rather unpleasant realities (like, say, the absence of free will, or indeed any will at all) which we’d not want to be true, to put it mildly.
I’ve met humans who are unable to recognize blatant inconsistencies. Not quite to the same extent as Bing Chat, but still. Also, I’m pretty sure monkeys are unable to recognize blatant inconsistencies, and monkeys are intelligent.
What makes monkeys intelligent in your view?