This seems like selective presentation of the evidence. You haven’t talked about AlphaZero or generative adversarial networks, for instance.
Not just in how any data-based algorithm engineering is 80% data cleaning while everyone pretends the power is in having clever algorithms
80% by what metric? Is your claim that Facebook could find your face in a photo using logistic regression if it had enough clean data? (If so, can you show me a peer-reviewed paper supporting this claim?)
Presumably you are saying something like: “80% of the human labor which goes into making these systems is data cleaning labor”. First, I don’t know if this is true. It seems like a hard claim to substantiate, because you’d have to get internal time usage data from a random sample of different organizations doing ML work. Anecdotes from social media are likely to lead us astray in this area, because “humans do most of the work that ‘AI’ is supposedly doing” is more of a “man bites dog” story and more likely to go viral.
But second… even if 80% of the hours spent are data cleaning hours, it’s not totally clear how this is relevant. This could just as easily be a story about how general-purpose and easy-to-use machine learning libraries are, because “once you plug them in and press go, most of the time is spent giving the system examples of what you want it to do. (A child could do it!)”
A friend of mine started a software startup which did not pretend to use any advanced AI whatsoever. However, he still did most email interactions with users by hand in the early days, because he wanted a deep understanding of how people were using his product. The existence of companies not using AI to power their products in no way refutes the existence of companies that do! And if you read the links in your post, their takes are significantly more nuanced than yours (Woebot does in fact use AI, ‘“Everything was perfect,” Mr. Park said in an interview after conversing with the Google bot. “It’s like a real person talking.”’)
I think a common opinion is that current deep learning tech will not get us to AGI, but we have recently acquired important new abilities we didn’t have before, we are using those abilities to do cool stuff we couldn’t previously do, and it’s possible we’ll have AGI after acquiring some number of additional key insights.
Even if deep learning is a local maximum which has just gotten us a few more puzzle pieces—my personal view—it’s possible that renewed interest in this area will end up producing AGI through some other means. I suspect that hype cycles in AI cause us to be overoptimistic about the ease of AGI during periods with lots of hype, and underoptimistic during periods of little hype. (From an EA perspective, the best outcome might be if the hype dies down but EAs keep working on it, to increase the probability that AGI is built by an EA team.) But at the end of the day, throwing research hours at problems tends to result in progress, and right now lots of research hours are being thrown at AI problems. I also think researchers tend to make more breakthroughs when they are feeling excited and audacious. That’s when I get my best ideas, at least.
This seems like selective presentation of the evidence. You haven’t talked about AlphaZero or generative adversarial networks, for instance.
80% by what metric? Is your claim that Facebook could find your face in a photo using logistic regression if it had enough clean data? (If so, can you show me a peer-reviewed paper supporting this claim?)
Presumably you are saying something like: “80% of the human labor which goes into making these systems is data cleaning labor”. First, I don’t know if this is true. It seems like a hard claim to substantiate, because you’d have to get internal time usage data from a random sample of different organizations doing ML work. Anecdotes from social media are likely to lead us astray in this area, because “humans do most of the work that ‘AI’ is supposedly doing” is more of a “man bites dog” story and more likely to go viral.
But second… even if 80% of the hours spent are data cleaning hours, it’s not totally clear how this is relevant. This could just as easily be a story about how general-purpose and easy-to-use machine learning libraries are, because “once you plug them in and press go, most of the time is spent giving the system examples of what you want it to do. (A child could do it!)”
A friend of mine started a software startup which did not pretend to use any advanced AI whatsoever. However, he still did most email interactions with users by hand in the early days, because he wanted a deep understanding of how people were using his product. The existence of companies not using AI to power their products in no way refutes the existence of companies that do! And if you read the links in your post, their takes are significantly more nuanced than yours (Woebot does in fact use AI, ‘“Everything was perfect,” Mr. Park said in an interview after conversing with the Google bot. “It’s like a real person talking.”’)
I think a common opinion is that current deep learning tech will not get us to AGI, but we have recently acquired important new abilities we didn’t have before, we are using those abilities to do cool stuff we couldn’t previously do, and it’s possible we’ll have AGI after acquiring some number of additional key insights.
Even if deep learning is a local maximum which has just gotten us a few more puzzle pieces—my personal view—it’s possible that renewed interest in this area will end up producing AGI through some other means. I suspect that hype cycles in AI cause us to be overoptimistic about the ease of AGI during periods with lots of hype, and underoptimistic during periods of little hype. (From an EA perspective, the best outcome might be if the hype dies down but EAs keep working on it, to increase the probability that AGI is built by an EA team.) But at the end of the day, throwing research hours at problems tends to result in progress, and right now lots of research hours are being thrown at AI problems. I also think researchers tend to make more breakthroughs when they are feeling excited and audacious. That’s when I get my best ideas, at least.