Three Laws Blog: Law and Artificial Intelligence

Bilokon on Evolution-Boostrapped Simulation: Artificial or Human Intelligence: Which Came First? @bilokon @imperialcollege

Paul Bilokon, Imperial College Lndon, has published Evolution-Bootstrapped Simulation: Artificial or Human Intelligence: Which Came First? Here is the abstract.

Humans have created artificial intelligence (AI), not the other way around. This statement is deceptively obvious. In this note, we decided to challenge this statement as a small, lighthearted Gedankenexperiment. We ask a simple question: in a world driven by evolution by natural selection, would neural networks or humans be likely to evolve first? We compare the Solomonoff--Kolmogorov--Chaitin complexity of the two and find neural networks (even LLMs) to be significantly simpler than humans. Further, we claim that it is unnecessary for any complex human-made equipment to exist for there to be neural networks. Neural networks may have evolved as naturally occurring objects before humans did as a form of chemical reaction-based or enzyme-based DNA computation. Now that we know that neural networks can pass the Turing test and suspect that they may be capable of superintelligence, we ask whether the natural evolution of neural networks could lead from pure evolution by natural selection to what we call evolution-bootstrapped simulation. The evolution of neural networks does not involve irreducible complexity; would easily allow irreducible complexity to exist in the evolution-bootstrapped simulation; is a falsifiable scientific hypothesis; and is independent of / orthogonal to the issue of intelligent design.

Download the essay from SSRN at the link.

Bliss on Teaching Law in the Age of Generative AI

John Bliss, University of Denver Sturm College of Law, is publishing Teaching Law in the Age of Generative AI in Jurimetrics. Here is the abstract.

With the rise of large language models capable of passing law school exams and the Unified Bar Exam, how should legal educators prepare their students for an age of transformative AI advances? Text-generating AI is poised to become a standard tool of legal research and writing, as it is being integrated in legal research and word processing applications (such as LexisNexis and Microsoft Word) that automate the drafting of legal documents based on human prompts. This Article explores the implications of these developments for legal education, focusing on pedagogy, curriculum, and assessment. The Article draws from four key perspectives relevant to the use of generative AI in law teaching: a survey of law students who participated in an AI-integrated course; a national survey of law faculty; an overview of the current state and projected futures of AI in the legal profession; and a summary of findings from the remarkably extensive educational literature that has arisen around the globe exploring the use of ChatGPT in different teaching contexts. These perspectives tend to support the development of an AI-integrated legal education. Yet, most of the surveyed law faculty, even those who strongly agreed that students should be prepared to use and critically evaluate generative AI, emphasized that they were uninformed about this technology and unsure how to proceed. This Article provides guidance, recommending that legal educators begin teaching with emerging AI tools, while exploring how implementation might vary across the legal curriculum. These recommendations are based on a number of factors, including consideration of how AI-integrated teaching may affect emerging professional competencies, traditional learning goals, academic integrity, and equity among students. The Article concludes by offering practical suggestions for incorporating generative AI in law teaching, including examples of exercises where students collaborate with generative AI in their writing, evaluate AI outputs, create their own AI tutors and debate partners, role-play with chatbots in classroom simulations, and reflect on the responsible use of generative AI in the legal profession.

Download the article from SSRN at the link.