“On the other hand, one could make the argument that this wave of AI is going to boost economic growth and science”—One can make a much more direct argument than this. The rate of incremental progress is important because that determines the amount of money flowing into the field and the amount of programmers studying AI. Now that the scope of tasks solvable by AI has increased vastly, the size of the field has been permanently raised and this increases the chance that innovations in general will occur. Further, there has been an increase in optimism about the power of AI which encourages people to be more ambitious.
“AI” may be too broad of a category, though. As an analogy, consider that there is currently a huge demand for programmers who do all kind of website development, but as far as I know, this hasn’t translated into an increased number of academics studying—say—models of computation, even though both arguably fall under “computer science”.
Similarly, the current wave of AI may get us a lot of people into doing deep learning and building machine learning models for specific customer applications, without increasing the number of people working on AGI much.
It’s true that there is now more excitement for AI, including more excitement for AGI. On the other hand, more excitement followed by disillusionment has previously led to AI winters.
“On the other hand, one could make the argument that this wave of AI is going to boost economic growth and science”—One can make a much more direct argument than this. The rate of incremental progress is important because that determines the amount of money flowing into the field and the amount of programmers studying AI. Now that the scope of tasks solvable by AI has increased vastly, the size of the field has been permanently raised and this increases the chance that innovations in general will occur. Further, there has been an increase in optimism about the power of AI which encourages people to be more ambitious.
“AI” may be too broad of a category, though. As an analogy, consider that there is currently a huge demand for programmers who do all kind of website development, but as far as I know, this hasn’t translated into an increased number of academics studying—say—models of computation, even though both arguably fall under “computer science”.
Similarly, the current wave of AI may get us a lot of people into doing deep learning and building machine learning models for specific customer applications, without increasing the number of people working on AGI much.
It’s true that there is now more excitement for AI, including more excitement for AGI. On the other hand, more excitement followed by disillusionment has previously led to AI winters.