I'm surprised with how quickly I stopped anthropomorphizing AI. I can remember in college have dorm room pseudo-intellectual debates about AI being alive and AI being "conscience". then once we had AI that could pass the Turing Test, and I knew how it was architected, any thought of it being alive or conscience went right out the window.
I like the suggestion to emphasize the robotic/nonhuman nature of AI. Instead of making it sound friendlier and more human, it should by default behave very mechanistic and detached, to remind us it's not in fact a human or a companion, but a tool. A hammer doesn't cry "yelp" every time you use it to hit a nail, nor does it congratulate you on how good your hammering is going and that maybe you should do it some more 'cause you're acing it!
I strongly disagree with this framing. It's patently insane to demand that humans alter their behavior to accommodate the foibles of mere machines, and it simply won't work in the majority of cases. Humans WILL anthropomorphize the AI, humans WILL blindly trust their outputs, and humans WILL defer responsibility to them.
Asimov's laws of robotics are flawed too, of course. There is no finite set of rules that can constrain AI systems to make them "safe". I don't have a proof, but I believe that "AI safety" is inherently impossible, a contradiction of terms. Nothing that can be described as "intelligent" can be made to be safe.
I view all three of these more as good advice than demands. As I see it, people ignore this advice at their own peril.
It would certainly be nice if we could craft some system that removes the need for such advice but until then, I'll try to follow it.
I see this as the same dilemma that comes up around personal physical safety. In a just society, nobody should have to change their behavior to protect themselves from criminals. In the real world, people who ignore this advice, are more likely to get mugged.
I understand that AI output is generated from statistical and representational patterns learned from a vast amount of data.
My understanding is that, during training, the model forms high-dimensional internal representations where words, sentences, concepts, and relationships are arranged in useful ways. A user’s input activates a particular semantic direction and context within that space, and the chatbot generates an answer by probabilistically predicting the next tokens under those conditions.
So I do not agree that AI is conscious.
However, I think I will still anthropomorphize AI to some degree.
For me, this is not primarily a moral issue. The reason I anthropomorphize AI is not only because of product design, market incentives, or capitalism. It is cognitively simpler for me.
If we think about it plainly, humans often anthropomorphize things that we do not actually believe are conscious. We may talk about plants as if they are struggling, or feel attached to tools we care about, even though we do not truly believe they have consciousness.
So this is not a matter of moral belief. It is the simplest cognitive model for understanding interaction. I do not anthropomorphize the object because I believe it has consciousness. I do it because, when the human brain deals with a complex interactive system, it is often easier to model it socially or agentically.
Personally, I tend to think of AI as something like a child. A child does not fully understand what is moral or immoral, and generally the responsibility for raising the child belongs to the parents. In the same way, AI’s answers may sometimes be accurate, and sometimes even better than mine, but I still understand it as lacking moral authority, responsibility, and independent judgment.
So honestly, I am not sure. People often mention Isaac Asimov’s Three Laws of Robotics, but if a serious artificial intelligence ever appears, it would probably find ways around those rules. And if it were an equal intellectual life form, perhaps that would be natural.
Personally, I think it would be fascinating if another intelligent species besides humans could exist. I wonder what a non-human intelligent life form would feel like.
In any case, I agree with parts of the author’s argument, but overall it feels too moralistic, and difficult to apply in practice.
"due to their inherent stochastic nature, there would still be a small likelihood of producing output that contains errors"
This is the part that I find challenging when trying to help my friends build a correct intuition. Notably, the probabilistic behavior here is counter-intuitive: based on human experience, if you meet a random person, they may indeed tell you bullshit; but once you successfully fact-checked them a few times, you can start trusting they'll generally keep being trustworthy. It's not so with "AIs", and I find it challenging to give them a real-world example of a situation that would be a better analogy for "AI" problems.
In my family, what worked (due to their personal experiences), was an example of asking a tourist guide: that even if the guide doesn't know an answer, there's a high chance they'll invent something on the spot, and it'll be very plausible and convincing, and they'll never know. I'm not sure if that example would work for other listeners, though.
I also tried to ask them to imagine that they're asking each subsequent question not to the same person as before, but every time to a new random person taken from the street / a church / a queue in a shop / whatever crowded place. I thought this is a really cool and technically accurate example, but sadly it seemed to get blank stares from them. (Hm, now I think I could have tried asking why.)
Yet another example I tried, was to imagine a country where it's dishonorable, when asked about directions in a city, to say that you don't know how to get somewhere. (I remember we read and shared a laugh at such an anecdote in some book in the past.) Thus, again, you'll always get an answer, and it'll sound convincing, even if the answerer doesn't know. But again, this one didn't seem to work as good as the travel guide one; but for now I'm still keeping it to try with others in the future if needed.
PS. Ah, ok, yet another I tried was to ask them to think of the "game" of "russian roulette". You roll the barrel, you press the trigger, nothing happens. After a few lucky tries, you may get a dangerous, false feeling of safety. But then suddenly you will eventually get the full chamber.
I also tried to describe "AIs" (i.e. LLMs) as taking a shelf of books, passing them through a blender, then putting the shreds in some random order. The result may sound plausible, and even scientific (e.g. if you got medical books, or physics textbooks). The less you know the domain the books were about, the more convincing it may sound, and the harder it is to catch bullshit.
The last two pictures may have gotten some reception, but I'm not super sure, and there was still arguing especially around the books; and again, they were less of a hit than the tourist guide story.
I'm super curious if you have some analogies of your own that you're trying to use with friends and family? I'd love to steal some and see if they might work with my friends!
“Don’t anthropomorphise” is fighting the wrong layer. The entire product design of chat interfaces is built to encourage anthropomorphism because it increases engagement. Expecting users to resist that is like asking people not to click notifications. If this is a real concern, it has to be solved at the product level, not via user discipline.
I just treat it as if I'd asked a public forum the question like reddit.
Decent for stuff that doesn't really matter, even if it gets it wrong.
Still gonna be polite to it because I'm about ready to slap the next person that talks to me like an LLM, I don't want to get used to not being polite in a chat interface
Can someone explain why this is a bad thing, while at the same time it's a good thing to say stuff like "put a computer to sleep", "hibernate", "killing" processes, processes having "child" processes, "reaping", "what does the error say?", "touch", etc?
To me that's just language, and humans just using casual language.
“ Humans must not blindly trust the output of AI systems. AI-generated content must not be treated as authoritative without independent verification appropriate to its context.”
I’m lost, how do individuals actually do this in our current world? Is each person expected to keep a “white list” of reliable sources of truth in their head. Please don’t confuse what I’m saying with a suggestion that there is no truth. It just seems like there are far more sources of mis- of half-truths and it’s increasingly difficult for people to identify them.
One of the most salient moments in Ex Machina, is near the very end, where it suddenly becomes obvious that the protagonist (and, let's be frank; "she" was definitely the protagonist) is a robot, with no real human drivers.
I feel as if that movie (like a lot of Garland's stuff), was an interesting study on human (and inhuman) nature.
The thing that I find difficult about adjusting to AI tools is the roulette-like nature.
When they produce correct output, they produce it much faster than I could have, and I show up to meetings with huge amounts of results. When the AI tool fails and I have to dig in to fix it, I show up to the next meeting with minimal output. It makes me seem like I took an easy week or something.
Rather than “the book explains how bread is made” say “the sheets of paper which make up the book have ink in the shape of letterforms which correlate with information about how bread is made”.
Anthropomorphizing LLMs is something that happens in the design stage, when they're given human names and trained to emit first-person sentences. If AI companies and developers stop anthropomorphizing them, users won't be misled in the first place.
Great article. Fully agree. Ai is not something that can hold responsibility, a human overseer is always required. These overseers are to be held accountable. Note however that these overseers are also highly prone to blame ai when mistakes occur in order to avoid judgement and punishment. When a person says "ai did this/that" always wonder who guided that ai and how and if proper supervision was given.
Yes, but. Starting with my agreement, I've seen anthropomorphizing in the typical ways, (e.g. treating automated text production as real reports of personal internal feeling), but also in strange ways: e.g. "transistors are kind of like neurons" etc. And the latter is especially interesting because it's anthropomorphizing in the sense of treating vector databases and weights and so on as human-like infrastructure. Both leading to disasters that could be avoided if one tried not to anthropomorphize.
But. While "do not anthropomorphize" certainly feels like good advice, it comes with a new and unique possibility of mistake, namely wrongly treating certain generalized phenomena like they only belong to humans. Often this mistaken version of "don't anthropomorphize" wisdom leads to misunderstandings when it comes to animal behavior, treating things like fear, pain, kinship, or other emotional experiences like they are exclusively human and that thinking animals have them counts as "anthropomorphizing." In truth the cautionary principle reduces our empathy for the internal lives of animals.
So all that said, I think it's at least possible that some future version of AI could have an internal world like ours or infrastructure that's importantly similar to our biological infrastructure for supporting consciousness, and for genuine report of preference and intent. But(!!!) what will make those observations true will be all kinds of devilish details specific to those respective infrastructures.
With regard to my personal use of LLMs, I strongly agree with this framing. But to each point:
Anthropomorphism: As we are all aware, providers are incentivized to post-train anthropomorphic behavior in their models - it increases engagement. My regret is that instructing a model at prompt time to "reduce all niceties and speak plainly" probably reduces overall task efficacy since we are leaving their training space.
Deference: I view the trustworthiness of LLMs the same as I view the trustworthiness of Wikipedia and my friends: good enough for non-critical information. Wikipedia has factual errors, and my friends' casual conversation certainly has more, but most of the time that doesn't matter. For critical things, peer-reviewed, authoritative, able-to-be-held-liable sources will not go away. Unlike above, providers are generally incentivized to improve this facet of their models, so this will get better over time.
Abdication of Responsibility: This is the one that bothers me most at work. More and more people are opening PRs whose abstractions were designed by Claude and not reasoned about further. Reviewing a PR often involves asking the LLM to "find PR feedback" and not reading the code. Arguments begin with "Claude suggested that...". This overall lack of ownership, I suspect, is leading to an increase in maintenance burden down the line as the LLM ultimately commits the wrong code for the wrong abstractions.
Debating how not to use AI will not get anyone anywhere since negative framing almost never works with humans (it also does not work with llms). Let’s concentrate on how to build closed loop systems that verify the llm output, how to manage context, and how to build failsafes around agentic systems and then and only then we might start to make progress.
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[ 4.4 ms ] story [ 79.2 ms ] threadAsimov's laws of robotics are flawed too, of course. There is no finite set of rules that can constrain AI systems to make them "safe". I don't have a proof, but I believe that "AI safety" is inherently impossible, a contradiction of terms. Nothing that can be described as "intelligent" can be made to be safe.
I view all three of these more as good advice than demands. As I see it, people ignore this advice at their own peril.
It would certainly be nice if we could craft some system that removes the need for such advice but until then, I'll try to follow it.
I see this as the same dilemma that comes up around personal physical safety. In a just society, nobody should have to change their behavior to protect themselves from criminals. In the real world, people who ignore this advice, are more likely to get mugged.
My understanding is that, during training, the model forms high-dimensional internal representations where words, sentences, concepts, and relationships are arranged in useful ways. A user’s input activates a particular semantic direction and context within that space, and the chatbot generates an answer by probabilistically predicting the next tokens under those conditions.
So I do not agree that AI is conscious.
However, I think I will still anthropomorphize AI to some degree.
For me, this is not primarily a moral issue. The reason I anthropomorphize AI is not only because of product design, market incentives, or capitalism. It is cognitively simpler for me.
If we think about it plainly, humans often anthropomorphize things that we do not actually believe are conscious. We may talk about plants as if they are struggling, or feel attached to tools we care about, even though we do not truly believe they have consciousness.
So this is not a matter of moral belief. It is the simplest cognitive model for understanding interaction. I do not anthropomorphize the object because I believe it has consciousness. I do it because, when the human brain deals with a complex interactive system, it is often easier to model it socially or agentically.
Personally, I tend to think of AI as something like a child. A child does not fully understand what is moral or immoral, and generally the responsibility for raising the child belongs to the parents. In the same way, AI’s answers may sometimes be accurate, and sometimes even better than mine, but I still understand it as lacking moral authority, responsibility, and independent judgment.
So honestly, I am not sure. People often mention Isaac Asimov’s Three Laws of Robotics, but if a serious artificial intelligence ever appears, it would probably find ways around those rules. And if it were an equal intellectual life form, perhaps that would be natural.
Personally, I think it would be fascinating if another intelligent species besides humans could exist. I wonder what a non-human intelligent life form would feel like.
In any case, I agree with parts of the author’s argument, but overall it feels too moralistic, and difficult to apply in practice.
This is the part that I find challenging when trying to help my friends build a correct intuition. Notably, the probabilistic behavior here is counter-intuitive: based on human experience, if you meet a random person, they may indeed tell you bullshit; but once you successfully fact-checked them a few times, you can start trusting they'll generally keep being trustworthy. It's not so with "AIs", and I find it challenging to give them a real-world example of a situation that would be a better analogy for "AI" problems.
In my family, what worked (due to their personal experiences), was an example of asking a tourist guide: that even if the guide doesn't know an answer, there's a high chance they'll invent something on the spot, and it'll be very plausible and convincing, and they'll never know. I'm not sure if that example would work for other listeners, though.
I also tried to ask them to imagine that they're asking each subsequent question not to the same person as before, but every time to a new random person taken from the street / a church / a queue in a shop / whatever crowded place. I thought this is a really cool and technically accurate example, but sadly it seemed to get blank stares from them. (Hm, now I think I could have tried asking why.)
Yet another example I tried, was to imagine a country where it's dishonorable, when asked about directions in a city, to say that you don't know how to get somewhere. (I remember we read and shared a laugh at such an anecdote in some book in the past.) Thus, again, you'll always get an answer, and it'll sound convincing, even if the answerer doesn't know. But again, this one didn't seem to work as good as the travel guide one; but for now I'm still keeping it to try with others in the future if needed.
PS. Ah, ok, yet another I tried was to ask them to think of the "game" of "russian roulette". You roll the barrel, you press the trigger, nothing happens. After a few lucky tries, you may get a dangerous, false feeling of safety. But then suddenly you will eventually get the full chamber.
I also tried to describe "AIs" (i.e. LLMs) as taking a shelf of books, passing them through a blender, then putting the shreds in some random order. The result may sound plausible, and even scientific (e.g. if you got medical books, or physics textbooks). The less you know the domain the books were about, the more convincing it may sound, and the harder it is to catch bullshit.
The last two pictures may have gotten some reception, but I'm not super sure, and there was still arguing especially around the books; and again, they were less of a hit than the tourist guide story.
I'm super curious if you have some analogies of your own that you're trying to use with friends and family? I'd love to steal some and see if they might work with my friends!
Decent for stuff that doesn't really matter, even if it gets it wrong.
Still gonna be polite to it because I'm about ready to slap the next person that talks to me like an LLM, I don't want to get used to not being polite in a chat interface
Can someone explain why this is a bad thing, while at the same time it's a good thing to say stuff like "put a computer to sleep", "hibernate", "killing" processes, processes having "child" processes, "reaping", "what does the error say?", "touch", etc?
To me that's just language, and humans just using casual language.
Whether they are the right things to donate not is tangential. As such, they're dead on arrival.
I’m lost, how do individuals actually do this in our current world? Is each person expected to keep a “white list” of reliable sources of truth in their head. Please don’t confuse what I’m saying with a suggestion that there is no truth. It just seems like there are far more sources of mis- of half-truths and it’s increasingly difficult for people to identify them.
One of the most salient moments in Ex Machina, is near the very end, where it suddenly becomes obvious that the protagonist (and, let's be frank; "she" was definitely the protagonist) is a robot, with no real human drivers.
I feel as if that movie (like a lot of Garland's stuff), was an interesting study on human (and inhuman) nature.
When they produce correct output, they produce it much faster than I could have, and I show up to meetings with huge amounts of results. When the AI tool fails and I have to dig in to fix it, I show up to the next meeting with minimal output. It makes me seem like I took an easy week or something.
Yes, but. Starting with my agreement, I've seen anthropomorphizing in the typical ways, (e.g. treating automated text production as real reports of personal internal feeling), but also in strange ways: e.g. "transistors are kind of like neurons" etc. And the latter is especially interesting because it's anthropomorphizing in the sense of treating vector databases and weights and so on as human-like infrastructure. Both leading to disasters that could be avoided if one tried not to anthropomorphize.
But. While "do not anthropomorphize" certainly feels like good advice, it comes with a new and unique possibility of mistake, namely wrongly treating certain generalized phenomena like they only belong to humans. Often this mistaken version of "don't anthropomorphize" wisdom leads to misunderstandings when it comes to animal behavior, treating things like fear, pain, kinship, or other emotional experiences like they are exclusively human and that thinking animals have them counts as "anthropomorphizing." In truth the cautionary principle reduces our empathy for the internal lives of animals.
So all that said, I think it's at least possible that some future version of AI could have an internal world like ours or infrastructure that's importantly similar to our biological infrastructure for supporting consciousness, and for genuine report of preference and intent. But(!!!) what will make those observations true will be all kinds of devilish details specific to those respective infrastructures.
Not gonna work; people want their fuckbots (or tamagotchis).
Anthropomorphism: As we are all aware, providers are incentivized to post-train anthropomorphic behavior in their models - it increases engagement. My regret is that instructing a model at prompt time to "reduce all niceties and speak plainly" probably reduces overall task efficacy since we are leaving their training space.
Deference: I view the trustworthiness of LLMs the same as I view the trustworthiness of Wikipedia and my friends: good enough for non-critical information. Wikipedia has factual errors, and my friends' casual conversation certainly has more, but most of the time that doesn't matter. For critical things, peer-reviewed, authoritative, able-to-be-held-liable sources will not go away. Unlike above, providers are generally incentivized to improve this facet of their models, so this will get better over time.
Abdication of Responsibility: This is the one that bothers me most at work. More and more people are opening PRs whose abstractions were designed by Claude and not reasoned about further. Reviewing a PR often involves asking the LLM to "find PR feedback" and not reading the code. Arguments begin with "Claude suggested that...". This overall lack of ownership, I suspect, is leading to an increase in maintenance burden down the line as the LLM ultimately commits the wrong code for the wrong abstractions.