WSJ: “Dr. LaPointe calls our current era “learning-based AI.” What characterizes this time is that computers—rather than humans—are now building the models that machines use to accomplish a task. Even “generative” AI is a bit of a misnomer—ChatGPT is using many of the same prediction algorithms and related technologies AI scientists have been developing for years, but it uses them to predict which word to add next to a block of text, instead of, say, whether an image is of a cat. These new generative AI systems, which pull together almost every trick cooked up by AI researchers since the turn of the millennium, are doing things no AI has ever done before. And that’s why they’re being integrated into search and productivity tools from Microsoft, Google, and countless startups in every field imaginable, from healthcare and logistics to tax prep and videogames.”
Louis Hyman: “Computers have failed to produce a huge surge in productivity, but the problem isn’t the computers. It’s that we haven’t let workers tap into the computers’ true power — automation. We still use them like typewriters or calculators.The arrival of ChatGPT — most of all, its remarkable ability to write computer code to automate well-defined tasks — can change all that. Instead of eliminating many white-collar jobs altogether, as people are understandably worried it will do, it has the ability to do something much more powerful: to eliminate what’s boring about those jobs, freeing us up to be more stimulated, more creative and more human in our work. In the process it can drastically increase productivity.”
Magnus Carlsen: “I think there are — there are plenty of players in history who have been immensely talented, but they’re — they’re just too pessimistic. They see too many dangers that are not there and so on so they cannot perform at a very high level.”
Christopher Penn: “Suppose you were able to start two instances of ChatGPT. Suppose one instance could hear what the other instance was saying and respond appropriately to it. You’d sign into one instance and tell it to start writing a blog post. You’d sign into the other instance and tell it to correct the blog post for grammatical correctness and factual correctness. Both instances would start almost competing with each other, working with and against each other’s output to create an overall better outcome. That’s the essence of autonomous AI within the context of large language models. They’re multiple instances of a model working together, sometimes adversarially, sometimes collaboratively, in ways that a single instance of a model can’t do. If you consider a team of content creators within an organization, you might have writers, editors, producers, proofreaders, publishers, etc. Autonomous AI would start up an instance for each of the roles and have them perform their roles.”
