I think a big obstacle to meaningfully using AI is going to be public perception. Understanding the difference between CHAT-GPT and open source models means that people like us will probably continue to find ways of using AI as it continues to improve, but what I keep seeing is botched applications, where neither the consumers nor the investors who are pushing AI really understand what it is or what it’s useful for. It’s like trying to dig a grave with a fork - people are going to throw away the fork and say it’s useless, not realising that that’s not how it’s meant to be used.
I’m concerned about the way the hype behaves because I wouldn’t be surprised if people got so sick of hearing about AI at all, let alone broken AI nonsense, that it hastens the next AI winter. I worry that legitimate development may be held back by all the nonsense.
I actually think public perception is not going to be that big a deal one way or the other. A lot of decisions about AI applications will be made by businessmen in boardrooms, and people will be presented with the results without necessarily even knowing that it’s AI.
I’ve seen a weird aspect of it from the science side, where people writing grant applications or writing papers feel compelled to incorporate AI into it, because even if they know that their sub-field has no reliable use-cases for AI yet, they’re feeling the pressure of the hype.
Specifically, when I say the pressure of the hype, I mean that some of the best scientists I have known were pretty bad at the academic schmoozing that facilitates better funding and more prestige. In practice, businessmen in boardrooms are often the ones holding the purse strings and sometimes it’s easier to try to speak their language than to “translate” one’s research to something they’ll understand.
I think a big obstacle to meaningfully using AI is going to be public perception. Understanding the difference between CHAT-GPT and open source models means that people like us will probably continue to find ways of using AI as it continues to improve, but what I keep seeing is botched applications, where neither the consumers nor the investors who are pushing AI really understand what it is or what it’s useful for. It’s like trying to dig a grave with a fork - people are going to throw away the fork and say it’s useless, not realising that that’s not how it’s meant to be used.
I’m concerned about the way the hype behaves because I wouldn’t be surprised if people got so sick of hearing about AI at all, let alone broken AI nonsense, that it hastens the next AI winter. I worry that legitimate development may be held back by all the nonsense.
I actually think public perception is not going to be that big a deal one way or the other. A lot of decisions about AI applications will be made by businessmen in boardrooms, and people will be presented with the results without necessarily even knowing that it’s AI.
I’ve seen a weird aspect of it from the science side, where people writing grant applications or writing papers feel compelled to incorporate AI into it, because even if they know that their sub-field has no reliable use-cases for AI yet, they’re feeling the pressure of the hype.
Specifically, when I say the pressure of the hype, I mean that some of the best scientists I have known were pretty bad at the academic schmoozing that facilitates better funding and more prestige. In practice, businessmen in boardrooms are often the ones holding the purse strings and sometimes it’s easier to try to speak their language than to “translate” one’s research to something they’ll understand.
Businessmen are just the public but with money.