It has become impossible to tell managers mesmerised by artificial intelligence that the tools are not, in fact, helpful. So employees just play along with the fiction to keep their jobs, writes our tech columnist
First of all, thank you for your extensive response. I think discussions like this are important. Here’s my 2 cents, sorry if I’m not that eloquent - I’m not a native English speaker so some of this is a bit hard to put into words for me.
Because your comment seemed to add an anecdotal “it also helped two people” to a long list of evils as if it were equivalent.
Well, surely my two friends can’t be the only people AI has helped, that would be highly unlikely. So I do think it is a valid point along the other ones - AI does create value. Otherwise there would be no hype. I just think it’s dishonest to claim that AI is completely useless. If we want to turn this into something positive, we have to be honest about all the aspects.
Aside from the nebulous question of just how it’s supposedly making lives better, and whether that actually is an improvement, the question repeats: Is a fee people’s lives getting better really equivalent to the people losing their homes, water supply or job over it? It’s not that we’re unaware that it might have benefits, it’s that those benefits are massively dwarfed by the evils.
Of course it’s not. We’re totally on the same page here. I just think it’s not a matter of the technology itself, it’s how those companies (are allowed to) act. That doesn’t seem like an AI problem to me, it’s a capitalism problem. We might have different perspectives on this btw because I live in the EU. People don’t lose their home or water supply here because of AI companies.
People treat them as virtual humans, use terms such as singularity and trust them with cognitive work a linguistic model plainly isn’t capable of actually doing.
Yes. And I do think AI companies are at fault here for marketing their products dishonestly to people who don’t understand how the technology works. They exploit stupid people, and mostly stupid people in positions with too much power.
They’re still being hailed as the future, despite evidence backing up what theorists have been warning for a while: A text generator cannot generate meaning and should not be used for any purpose where the content actually matters. That your friends’ word salad happened to resemble useful advice is nice, but it wasn’t actually advice, just the product of a high-tech advice imitator that got lucky.
I think we might fundamentally disagree on this. Advice from a human is nothing but a multivariate combination of things that person has learned in the past. It’s way more complex - brains are orders of magnitude more complex than an artificial neural network - but I don’t see a fundamentally and qualitative difference here unless you believe in something like a soul that somehow magically gives more value to a human advice. If you understand that an LLM is a mathematical tool and not a human, you can use it in very efficient and helpful ways. Some questions actually better answered by something that has gathered a wide range of information from thousands or even millions of sources. Some questions that require fast answers are also better answered by AI (just yesterday I asked ChatGPT for a python script for a certain task - it took about 20 seconds and provided a perfectly working ~500 lines python script that I could use; no human could ever do that). Lots of other questions - questions that require emotional connection, personal experience, specialized knowledge - should be left to humans, of course. The amount of shitty and wrong advice I’ve gotten from people, even doctors and other experts, is immense btw. The crucial thing is that you need to learn how to test and evaluate advice that you get - from people AND from LLMs. Many people would profit from learning that skill - maybe using LLMs might even teach them that, even if it will probably usually be the hard way.
Language models are useful for linguistic correlation tasks, but the actual utility of that is hard to gauge, because it heavily depends on the mode of the output and on the recipient.
Yes, that’s exactly my point. We need to educate the recipients. That is actually one of the most important things imo. AI and LLMs aren’t going to go away, they’re here to stay. Just look at how it already penetrated all kinds of scientific disciplines. We urgently need to regulate what companies can do with it and we need to educate people on how they work and how to use them, and especially how NOT to use them. And by the way, I wish we had never made the jump from calling it deep learning to calling it AI - I think that might have prevented a lot of problems und misunderstandings.
Look, I think ultimately our views aren’t even that different. That’s why discourse is important. I just don’t think screaming “AI is bad” is going to get us anywhere (and that’s essentially what the original comment that I replied to was doing imo). We need to be specific about what the problem is: the problem is not AI, the problem is that some people are getting super rich on the expense of almost everyone else. THAT is what we need to stop. You can’t fight technological and scientific progress, what you need to fight is malicious use of technology and the exploitation of the masses for the profit of a few. I’d say we might actually in an “industrial revolution” kind of situation. Back then, workers rights and communism became a thing. We might need something as fundamental as that to deal with today’s problems. We’ll need all the social sciences now that we’ve cut funding for over the last decades because MINT was just more important to us (I say that as a MINT scientist btw).
I find that writing out and defending my position helps me refine it. Sometimes, my perspective on things shifts just by trying to explain it. Discussions are the whetstone by which arguments are sharpened.
I’m not a native English speaker so some of this is a bit hard to put into words for me.
That makes two of us :D
Sometimes, these discussions help expand my Engish skills too.
So I do think it is a valid point along the other ones - AI does create value.
Yesn’t. It’s a text generator that predicts a likely series of words, based on the language patterns it learned from its training material. It doesn’t so much create value as aggregate the value of other people’s work into a weighted reproduction.
However, those weights are biased by quantity, not quality. It has no way to assess which responses are good, only which ones are likely. The average value it (re-)produces is an average of the value of the training material. Models trained on a wide variety of content (such as ChatGPT) will inevitably include a lot of material of little value to specialised topics.
Hence my argument: It can get lucky and predict a high-value response, but the problem is that it isn’t guaranteed to do so. A layperson doesn’t have the expertise to tell the difference. That not only dilutes the value, it invites false confidence in the results. In cases where accuracy is critical, this may actually produce negative value if people consult an unreliable model rather than a specialist.
And that’s the critical difference to human advice: human experience is shaped by a number of factors beyond just language. We create semantic connections to abstract concepts and attach specific meaning to certain words and patterns.
LLMs don’t have that abstraction. They can predict sequences of text that sound plausible and might coincide with something describing reality, but they can’t tell whether it’s accurate.
Advice has to be grounded in reality to be useful, and that’s what LLMs are missing.
They’re perfectly suitable for tasks where correlation is enough, but if the task requires actual understanding, LLMs aren’t equipped for it.
Otherwise there would be no hype.
Hype doesn’t always need a solid reason. In this case, a lot of hype stems from the hope that we may one day have the type of Artificial General Intelligence that SciFi has long dreamed of, the illusion that they may be able to do our work for us and the promise to company managers that they may be able to save money by replacing human employees with AI.
The various executives of the big AI vendors are obviously capitalising on that, stoking the hype with grand and utopic visions because they want to sell their product.
It’s not that it’s useless. It’s that the utility is far less than the lofty promises made by people milking the hype for all it’s worth. And that little utility comes at a terrible social, economic and ecologic price.
We might have different perspectives on this btw because I live in the EU. People don’t lose their home or water supply here because of AI companies.
I’m in the EU too, I just read a lot of US news. I don’t think we should dismiss the consequences our use of US infrastructure has. Worse yet, I don’t think we should dismiss the political dependencies that creates.
We need to be specific about what the problem is: the problem is not AI, the problem is that some people are getting super rich on the expense of almost everyone else.
I think that’s only a part of the problem. The second part is the lack of understanding you mentioned, and the resulting mis- and overuse. I’ve seen people trying to argue with experts because “ChatGPT said” because they genuinely do not understand that ChatGPT is a parrot, not an expert.
And the third part, again, is the disastrous effect on our world.
You can’t fight technological and scientific progress […]
I’d say we might actually in an “industrial revolution” kind of situation.
I’m not fighting it. I’m trying to pull it out of the pit that the current obsession with imitation has dug. When college students, the next generation or scientists, trades their scientific understanding for the convenience of high-tech parrots, that is the opposite of progress. It’s stagnation, fostered by those few people that are happily trading our future for their present profits.
That’s the mirage of this “industrial revolution” analogy: They’ve built something in the shape of a steam engine, promised the functionality of Spinning Jenny, sold fabric factory owners on the idea who fired their workers to buy these machines. The people they fired were promised that they’d have to work less, but weren’t told that they would be paid less too.
Now these machines turn out to not actually provide the smooth, spinning motion required for spinning thread.
Factories are facing expensive production outages, compounding the expenses of getting these machines installed and can’t afford to hire all the workers back.
The machine shops built to produce these Sloppy Jennies or their parts will eventually find it harder to sell their iventory. Once they go under, their workers will also become economic casualties.
We’re at the point where we need to reinforce worker protections. We need to push for a system where our livelihood isn’t contingent on the amount of work we do. Only then can that utopia even manifest. And to actually make progress:
We’ll need all the social sciences now that we’ve cut funding for over the last decades
Yes.
We’ll need to understand the social dynamics of such technology to better prevent the disastrous side-effects. We’ll need to compare historical developments with present circumstances and plans to account for future developments. We’ll need to study the psychological effects of interacting with human-like machines to be able to correct course where needed.
Whatever control mechanism is supposed to prevent machines from producing harmful output will need heuristics based on those disciplines to assess the dangers of that output.
I’d include philosophy too. We don’t need neural networks that are a lesser version of human ones, nor just scaled-up variants that also replicate all the human inefficiencies, errors and biases. Those are the pure MINT approach to the problem, and it’s clearly coming up short.
We need to find a rigorous mechanism for representing semantic knowledge in digital systems that don’t just imitate, but surpass human cognition. We need a workable philosophical grounding for logical and mathematical approaches to modelling knowledge of concepts rather than just language.
We need to get over the error that LLMs are “thinking” or “just like humans”. They’re not, but as long as we’re stuck on the idea, we can’t fix it.
Also, we really, really should spend less time thinking about whether we could and more about whether we should.
First of all, thank you for your extensive response. I think discussions like this are important. Here’s my 2 cents, sorry if I’m not that eloquent - I’m not a native English speaker so some of this is a bit hard to put into words for me.
Well, surely my two friends can’t be the only people AI has helped, that would be highly unlikely. So I do think it is a valid point along the other ones - AI does create value. Otherwise there would be no hype. I just think it’s dishonest to claim that AI is completely useless. If we want to turn this into something positive, we have to be honest about all the aspects.
Of course it’s not. We’re totally on the same page here. I just think it’s not a matter of the technology itself, it’s how those companies (are allowed to) act. That doesn’t seem like an AI problem to me, it’s a capitalism problem. We might have different perspectives on this btw because I live in the EU. People don’t lose their home or water supply here because of AI companies.
Yes. And I do think AI companies are at fault here for marketing their products dishonestly to people who don’t understand how the technology works. They exploit stupid people, and mostly stupid people in positions with too much power.
I think we might fundamentally disagree on this. Advice from a human is nothing but a multivariate combination of things that person has learned in the past. It’s way more complex - brains are orders of magnitude more complex than an artificial neural network - but I don’t see a fundamentally and qualitative difference here unless you believe in something like a soul that somehow magically gives more value to a human advice. If you understand that an LLM is a mathematical tool and not a human, you can use it in very efficient and helpful ways. Some questions actually better answered by something that has gathered a wide range of information from thousands or even millions of sources. Some questions that require fast answers are also better answered by AI (just yesterday I asked ChatGPT for a python script for a certain task - it took about 20 seconds and provided a perfectly working ~500 lines python script that I could use; no human could ever do that). Lots of other questions - questions that require emotional connection, personal experience, specialized knowledge - should be left to humans, of course. The amount of shitty and wrong advice I’ve gotten from people, even doctors and other experts, is immense btw. The crucial thing is that you need to learn how to test and evaluate advice that you get - from people AND from LLMs. Many people would profit from learning that skill - maybe using LLMs might even teach them that, even if it will probably usually be the hard way.
Yes, that’s exactly my point. We need to educate the recipients. That is actually one of the most important things imo. AI and LLMs aren’t going to go away, they’re here to stay. Just look at how it already penetrated all kinds of scientific disciplines. We urgently need to regulate what companies can do with it and we need to educate people on how they work and how to use them, and especially how NOT to use them. And by the way, I wish we had never made the jump from calling it deep learning to calling it AI - I think that might have prevented a lot of problems und misunderstandings.
Look, I think ultimately our views aren’t even that different. That’s why discourse is important. I just don’t think screaming “AI is bad” is going to get us anywhere (and that’s essentially what the original comment that I replied to was doing imo). We need to be specific about what the problem is: the problem is not AI, the problem is that some people are getting super rich on the expense of almost everyone else. THAT is what we need to stop. You can’t fight technological and scientific progress, what you need to fight is malicious use of technology and the exploitation of the masses for the profit of a few. I’d say we might actually in an “industrial revolution” kind of situation. Back then, workers rights and communism became a thing. We might need something as fundamental as that to deal with today’s problems. We’ll need all the social sciences now that we’ve cut funding for over the last decades because MINT was just more important to us (I say that as a MINT scientist btw).
I find that writing out and defending my position helps me refine it. Sometimes, my perspective on things shifts just by trying to explain it. Discussions are the whetstone by which arguments are sharpened.
That makes two of us :D
Sometimes, these discussions help expand my Engish skills too.
Yesn’t. It’s a text generator that predicts a likely series of words, based on the language patterns it learned from its training material. It doesn’t so much create value as aggregate the value of other people’s work into a weighted reproduction.
However, those weights are biased by quantity, not quality. It has no way to assess which responses are good, only which ones are likely. The average value it (re-)produces is an average of the value of the training material. Models trained on a wide variety of content (such as ChatGPT) will inevitably include a lot of material of little value to specialised topics.
Hence my argument: It can get lucky and predict a high-value response, but the problem is that it isn’t guaranteed to do so. A layperson doesn’t have the expertise to tell the difference. That not only dilutes the value, it invites false confidence in the results. In cases where accuracy is critical, this may actually produce negative value if people consult an unreliable model rather than a specialist.
And that’s the critical difference to human advice: human experience is shaped by a number of factors beyond just language. We create semantic connections to abstract concepts and attach specific meaning to certain words and patterns.
LLMs don’t have that abstraction. They can predict sequences of text that sound plausible and might coincide with something describing reality, but they can’t tell whether it’s accurate.
Advice has to be grounded in reality to be useful, and that’s what LLMs are missing.
They’re perfectly suitable for tasks where correlation is enough, but if the task requires actual understanding, LLMs aren’t equipped for it.
Hype doesn’t always need a solid reason. In this case, a lot of hype stems from the hope that we may one day have the type of Artificial General Intelligence that SciFi has long dreamed of, the illusion that they may be able to do our work for us and the promise to company managers that they may be able to save money by replacing human employees with AI.
The various executives of the big AI vendors are obviously capitalising on that, stoking the hype with grand and utopic visions because they want to sell their product.
It’s not that it’s useless. It’s that the utility is far less than the lofty promises made by people milking the hype for all it’s worth. And that little utility comes at a terrible social, economic and ecologic price.
I’m in the EU too, I just read a lot of US news. I don’t think we should dismiss the consequences our use of US infrastructure has. Worse yet, I don’t think we should dismiss the political dependencies that creates.
I think that’s only a part of the problem. The second part is the lack of understanding you mentioned, and the resulting mis- and overuse. I’ve seen people trying to argue with experts because “ChatGPT said” because they genuinely do not understand that ChatGPT is a parrot, not an expert.
And the third part, again, is the disastrous effect on our world.
I’m not fighting it. I’m trying to pull it out of the pit that the current obsession with imitation has dug. When college students, the next generation or scientists, trades their scientific understanding for the convenience of high-tech parrots, that is the opposite of progress. It’s stagnation, fostered by those few people that are happily trading our future for their present profits.
That’s the mirage of this “industrial revolution” analogy: They’ve built something in the shape of a steam engine, promised the functionality of Spinning Jenny, sold fabric factory owners on the idea who fired their workers to buy these machines. The people they fired were promised that they’d have to work less, but weren’t told that they would be paid less too.
Now these machines turn out to not actually provide the smooth, spinning motion required for spinning thread. Factories are facing expensive production outages, compounding the expenses of getting these machines installed and can’t afford to hire all the workers back.
The machine shops built to produce these Sloppy Jennies or their parts will eventually find it harder to sell their iventory. Once they go under, their workers will also become economic casualties.
We’re at the point where we need to reinforce worker protections. We need to push for a system where our livelihood isn’t contingent on the amount of work we do. Only then can that utopia even manifest. And to actually make progress:
Yes.
We’ll need to understand the social dynamics of such technology to better prevent the disastrous side-effects. We’ll need to compare historical developments with present circumstances and plans to account for future developments. We’ll need to study the psychological effects of interacting with human-like machines to be able to correct course where needed.
Whatever control mechanism is supposed to prevent machines from producing harmful output will need heuristics based on those disciplines to assess the dangers of that output.
I’d include philosophy too. We don’t need neural networks that are a lesser version of human ones, nor just scaled-up variants that also replicate all the human inefficiencies, errors and biases. Those are the pure MINT approach to the problem, and it’s clearly coming up short.
We need to find a rigorous mechanism for representing semantic knowledge in digital systems that don’t just imitate, but surpass human cognition. We need a workable philosophical grounding for logical and mathematical approaches to modelling knowledge of concepts rather than just language.
We need to get over the error that LLMs are “thinking” or “just like humans”. They’re not, but as long as we’re stuck on the idea, we can’t fix it.
Also, we really, really should spend less time thinking about whether we could and more about whether we should.