Professor Chakraborty explores the limitations of Large Language Models (LLMs) such as ChatGPT, which has billions of parameters learned from large volumes of data, and highlights that these models are not as smart as they appear.
They lack a robust theory of the world, which limits their ability to do tasks that require common-sense reasoning and basic logical inferencing. While LLMs excel at many jobs humans traditionally take pride in, they may not be suitable for tasks that require factor vaguely-specified high level business goals into components that need to be designed or prescribe how best to leverage competencies of the existing workforce.
The author argues that the post-LLM age will trigger the shift from a tool-centric approach to a problem-centric one, where those with a good mix of wisdom to make best use of the technological resources at hand and strong interpersonal skills will be well sought after. The article concludes that technological innovation initially displaces workers but creates fresh avenues for employment in the long run, and over time, we are likely to see a flurry of new jobs that do not exist today.
Key takeaways:
- LLMs lack a robust theory of the world and are not suitable for tasks that require common-sense reasoning and basic logical inferencing.
- LLMs excel at many jobs humans traditionally take pride in, but may not be suitable for tasks that require factor vaguely-specified high level business goals into components that need to be designed.
- The post-LLM age will trigger the shift from a tool-centric approach to a problem-centric one, where those with a good mix of wisdom to make best use of the technological resources at hand and strong interpersonal skills will be well sought after.
- Technological innovation initially displaces workers but creates fresh avenues for employment in the long run, and over time, we are likely to see a flurry of new jobs that do not exist today.