Researchers: “Look at our child crawl! This is a big milestone. We can’t wait to see what he’ll do in the future.
CEOs: Give that baby a job!
AI stuff was so cool to learn about in school, but it was also really clear how much further we had to go. I’m kind of worried. We already had one period of AI overhype lead to a crash in research funding for decades. I really hope this bubble doesn’t do the same thing.
I’m… honestly kinda okay with it crashing. It’d suck because AI has a lot of potential outside of generative tasks; like science and medicine. However, we don’t really have the corporate ethics or morals for it, nor do we have the economic structure for it.
AI at our current stage is guaranteed to cause problems even when used responsibly, because its entire goal is to do human tasks better than a human can. No matter how hard you try to avoid it, even if you do your best to think carefully and hire humans whenever possible, AI will end up replacing human jobs. What’s the point in hiring a bunch of people with a hyper-specialized understanding of a specific scientific field if an AI can do their work faster and better? If I’m not mistaken, normally having some form of hyper-specialization would be advantageous for the scientist because it means they can demand more for their expertise (so long as it’s paired with a general understanding of other fields).
However, if you have to choose between 5 hyper-specialized and potentially expensive human scientists, or an AI designed to do the hyper-specialized task with 2~3 human generalists to design the input and interpret the output, which do you go with?
So long as the output is the same or similar, the no-brainer would be to go with the 2~3 generalists and AI; it would require less funding and possibly less equipment - and that’s ignoring that, from what I’ve seen, AI tends to be better than human scientists in hyper-specialized tasks (though you still need scientists to design the input and parse the output). As such, you’re basically guaranteed to replace humans with AI.
We just don’t have the society for that. We should be moving in that direction, but we’re not even close to being there yet. So, again, as much potential as AI has, I’m kinda okay if it crashes. There aren’t enough people who possess a brain capable of handling an AI-dominated world yet. There are too many people who see things like money, government, economics, etc as some kind of magical force of nature and not as human-made systems which only exist because we let them.
The sheer waste of energy and mass production of garbage clogging up search results alone is enough to make me hope the bubble will pop reeeeal soon. Sucks for research but honestly the bad far outweighs the good right now, it has to die.
Yeah search is pretty useless now. I’m so over it. Trying to fix problems always has the top 15 results be like:
“You might ask yourself, how is Error-13 on a Maytag Washer? Well first, let’s start with What Is a Maytag Washer. You would be right to assume washing clothes has been a task for thousands of years. The first washing machine was invented…” (Yes I wrote that by hand, how’d I do? Lol)
It’s the same as how I really stopped caring if crypto was gonna “revolutionize money” once it became a gold rush to horde GPUs and subsequently any other component you could store a hash on.
R&D and open source for the advancement of humanity is cool.
Building enormous farms and burning out powerful components that could’ve been used for art and science, to instead prove-that-you-own-a-receipt-for-an-ugly-monkey-jpeg hoping it explodes in value, is apalling.
I’m sure there was an ethical application way back there somewhere, but it just becomes a pump-and-dump scheme and ruins things for a lot of good people.
AI as a field initially started getting big in the 1960s with machine translation and perceptrons (super-basic neural networks), which started promising but hit a wall basically immediately. Around 1974 the US military cut most of their funding to their AI projects because they weren’t working out, but by 1980 they started funding AI projects again because people had invented new AI approaches. Around 1984 people coined the term “AI winter” for the time when funding had dried up, which incidentally was right before funding dried up again in the 90s until around the 2010s.
Yeah. It’s more like:
Researchers: “Look at our child crawl! This is a big milestone. We can’t wait to see what he’ll do in the future.
CEOs: Give that baby a job!
AI stuff was so cool to learn about in school, but it was also really clear how much further we had to go. I’m kind of worried. We already had one period of AI overhype lead to a crash in research funding for decades. I really hope this bubble doesn’t do the same thing.
I’m… honestly kinda okay with it crashing. It’d suck because AI has a lot of potential outside of generative tasks; like science and medicine. However, we don’t really have the corporate ethics or morals for it, nor do we have the economic structure for it.
AI at our current stage is guaranteed to cause problems even when used responsibly, because its entire goal is to do human tasks better than a human can. No matter how hard you try to avoid it, even if you do your best to think carefully and hire humans whenever possible, AI will end up replacing human jobs. What’s the point in hiring a bunch of people with a hyper-specialized understanding of a specific scientific field if an AI can do their work faster and better? If I’m not mistaken, normally having some form of hyper-specialization would be advantageous for the scientist because it means they can demand more for their expertise (so long as it’s paired with a general understanding of other fields).
However, if you have to choose between 5 hyper-specialized and potentially expensive human scientists, or an AI designed to do the hyper-specialized task with 2~3 human generalists to design the input and interpret the output, which do you go with?
So long as the output is the same or similar, the no-brainer would be to go with the 2~3 generalists and AI; it would require less funding and possibly less equipment - and that’s ignoring that, from what I’ve seen, AI tends to be better than human scientists in hyper-specialized tasks (though you still need scientists to design the input and parse the output). As such, you’re basically guaranteed to replace humans with AI.
We just don’t have the society for that. We should be moving in that direction, but we’re not even close to being there yet. So, again, as much potential as AI has, I’m kinda okay if it crashes. There aren’t enough people who possess a brain capable of handling an AI-dominated world yet. There are too many people who see things like money, government, economics, etc as some kind of magical force of nature and not as human-made systems which only exist because we let them.
The sheer waste of energy and mass production of garbage clogging up search results alone is enough to make me hope the bubble will pop reeeeal soon. Sucks for research but honestly the bad far outweighs the good right now, it has to die.
Yeah search is pretty useless now. I’m so over it. Trying to fix problems always has the top 15 results be like:
“You might ask yourself, how is Error-13 on a Maytag Washer? Well first, let’s start with What Is a Maytag Washer. You would be right to assume washing clothes has been a task for thousands of years. The first washing machine was invented…” (Yes I wrote that by hand, how’d I do? Lol)
It’s the same as how I really stopped caring if crypto was gonna “revolutionize money” once it became a gold rush to horde GPUs and subsequently any other component you could store a hash on.
R&D and open source for the advancement of humanity is cool.
Building enormous farms and burning out powerful components that could’ve been used for art and science, to instead prove-that-you-own-a-receipt-for-an-ugly-monkey-jpeg hoping it explodes in value, is apalling.
I’m sure there was an ethical application way back there somewhere, but it just becomes a pump-and-dump scheme and ruins things for a lot of good people.
Actually we’re already two “AI winters” in, so we should be hitting another pretty soon
Can you explain? I’ve never heard of them before.
AI as a field initially started getting big in the 1960s with machine translation and perceptrons (super-basic neural networks), which started promising but hit a wall basically immediately. Around 1974 the US military cut most of their funding to their AI projects because they weren’t working out, but by 1980 they started funding AI projects again because people had invented new AI approaches. Around 1984 people coined the term “AI winter” for the time when funding had dried up, which incidentally was right before funding dried up again in the 90s until around the 2010s.
Make that baby a CEO!
Oh no. Boss baby.