In the rapidly evolving landscape of artificial intelligence (AI), the dialogue often veers between the extremes of stringent regulation, like the European Union’s AI Act, and laissez-faire approaches that risk unbridled technological advances without sufficient safeguards. Amidst this polarized debate, the Coalition for Health AI (CHAI) has emerged as a promising alternative approach that addresses the ethical, social, and economic complexities introduced by AI, while also supporting continued innovation.
It's difficult to understand some technologies because they're better experienced than described. I've found GenAI to be one example where it's difficult to grasp the full range of capabilities unless you see some of it in action. Over the last year, I've given a number of presentations that tried to contextualize GenAI for the audience by demonstrating relevant use cases. I compiled them in this long master deck, which I periodically update and am sharing in the hope that it may spark some ideas for you.
I had a great time joining James Pethokoukis on his podcast, Faster, Please! We touched on a number of areas including how AI can help improve teaching and learning.
Large Language Models (LLMs) have ushered in a new era of artificial intelligence (AI) demonstrating remarkable capabilities in language generation, translation, and reasoning. Yet, LLMs often stumble over basic math problems, posing a problem for their use in settings—including education—where math is essential. However, these limitations are being addressed through improvements in the models themselves along with better prompting strategies.
For the first time, the presidential transition process will receive technology-focused agency review briefs — written by expert technologists and policymakers, who themselves served in the agencies — to assess the current technological capacity, critical items for the first 200 days, and key technical leadership positions for 2021.
