How chatgpt is changing the economy

Power struggle

Anton Korinek, an economist at the University of Virginia and fellow at the Brookings Institution, did what many of us did when we discovered the new generation of big language models like ChatGPT: He started playing with them to see. How do you understand the job? He meticulously documented their performance in a paper in February, noting how well they handled 25 “use cases,” from brainstorming and text editing (very helpful) to coding (with some help) to math (not great).

Chhattisgarh has misinterpreted one of the most fundamental principles in economics, Corinc says, “so badly messed up.” But the mistake, which is easily seen, is quickly forgiven in light of the benefits. “I can tell you that it makes me more productive as a cognitive worker,” he says. “There’s no question to me that I’m more effective when I use my hands-on, language model.”

When GPT-4 came out, I tested its performance on the 25 questions I ran in February, and it performed much better. There were few opportunities to do things; He was also much better at math assignments, says Korinek.

Because ChatGPT and other AI bots automate cognitive work, as opposed to physical work that requires investment in equipment and infrastructure, increases in economic productivity can happen faster than previous technological revolutions, Korinek says. “I think we’ll see more growth in productivity at the end of the year – certainly in 2024,” he says.

What’s more, he says, in the long run, the way AI models can make researchers like him more productive has the potential to drive technological progress.

That enormous potential of language models is growing in research in the physical sciences. Berend Smit, who runs the Chemical Engineering Laboratory at EPFL in Lausanne, Switzerland, is an expert in using machine learning to discover new materials. Last year, after one of his graduate students, Kevin Mike Jabonka, showed some interesting results using GPT-3, Smit asked him to demonstrate that GPT-3 was useless for the advanced machine learning studies the group was doing. To predict the properties of compounds.

“It’s completely gone,” Smith joked.

After a few minutes of fine-tuning with a few relevant examples, the model answers basic questions about things like the solubility of a compound or a reaction, making advanced machine learning tools specifically designed for chemistry. Simply give it the name of the compound, and it can predict different properties based on its structure.