Full show notes for this episode can be found here: https://www.96layers.ai/p/will-ai-ever-become-a-person

Have you ever considered what it truly means to be a person? I don't mean biologically, but from a philosophical standpoint, like what really defines personhood is a person. Someone that has common sense and can think and reason at a high level. Could a person be defined by having a distinct, consistent personality, or is it rooted in social interactions, like being accountable to others?

As ChatGPT and other large language models have continued to advance, some have asked whether these new AI systems might be considered persons. Earlier this year, the Los Angeles Times published an article titled is it time to start Considering personhood rights for AI chatbots? And even if the answer is no for current AI systems, might we reach a point where we're forced to recognize an AI as a person in its own right?

To help answer these questions, I spoke with Jake Browning, a visiting scientist at New York University's computer science department. Jake received his PhD in philosophy from The New School and has written extensively on the philosophy of artificial intelligence and large language models. I found Jake's ideas on AI personhood thought provoking, and I think you will too.

Let's say you're on the edge of developing an awesome new AI language model. But here's a critical question – how do you ensure that your use of training data aligns with its licensing terms? How do you even find out what the licensing terms of that data are? Here’s another question: how do you find out where the dataset came from and what's inside? And how do you prevent the dataset from introducing bias and toxicity into your model?

These are some of the key questions we're discussing in this week’s episode. I spoke with Robert Mahari and Shane Longpre from the Data Provenance Initiative, a research project and online tool that helps researchers, startups, legal scholars, and other interested parties track the lineage of AI fine-tuning datasets. Shane and Robert are both PhD candidates at MIT’s Media Lab, and Robert is also a J.D. candidate at Harvard Law School.

This week I talked to AI researcher Nina Beguš. Nina completed her PhD in Comparative Literature at Harvard University where she began creating a new practice called “Artificial Humanities,” the idea that history, literature, film, myth, and other humanities can help add depth to AI development, including in the design and engineering process. Nina is currently a postdoctoral researcher at Cal Berkeley’s Center for Science, Technology, Medicine, & Society.

We had a wide ranging conversation including Nina’s early experiences with art and literature while growing up in Slovenia, AI and chess, large language model’s impact on writing, AI and human interpretations of the pygmalion myth — an area Nina has researched in depth — and more about Nina’s goal of an Artificial Humanities research agenda. For those who enjoyed this conversation you may be interested to know that Nina has a book coming out in 2024 called, “Artificial Humanities: A Fictional Perspective on Language in AI,” so be on the lookout for that.

A transcript and show notes are available here. I have augmented the transcript with an extensive set of notes, links, videos, pictures, and maps.

This week I stopped by De Montfort University in Leicester, England to speak with Dr. Kutoma Wakunuma, an expert on Responsible AI in Africa. We discussed opportunities and challenges, the importance of gender equality, and Ubuntu and Ujamma philosophies.

Full show notes are available here.