In this week’s blog, I explore the fascinating question: What does a large language model (LLM) really know? Intriguing studies from MIT researchers contain a partial answer. I also delve […]

In this blog, I explore the notion of “fit for purpose” in the context of large language models (LLMs). When you have a big hammer, as they say, everything is […]

In this blog, I explore the state of affairs with how to label content generated by artificial intelligence (AI). This is an incredibly hard problem to solve.   Labeling AI […]

This week’s blog highlights content from my latest podcast with my friend Prem Devanbu, distinguished professor emeritus at UC Davis. He works in empirical software engineering and artificial intelligence (AI) […]

As I was browsing the blogsphere for what is new, I came across this snippet “Knowledge Graphs and LLMs are “a match made in Heaven.” and it piqued my interest. […]

This week I am focusing on a discipline that didn’t exist a few years ago: Prompt engineering. Yes, I use the term discipline as there are now courses being offered […]

In this week’s blog I look at the labor force impact of large language models (LLMs) and a blog post by Bill Gates heralding a new era of artificial intelligence […]

Big tech is at war so to speak, a war started by the popularity of ChatGPT, based on a large language model (LLM). Google, Microsoft, Meta and Amazon have all […]

In this blog, I want to highlight one theme that I found interesting from the 2021 Conference on Empirical Methods in Natural Language Processing (EMNLP 2021), one that explores the […]