The Shifting Dynamics Of Large Language Models

We are clearly in the early days of a transition and we are watching it unfold before us. Hype aside, it is fascinating to watch artificial intelligence rapidly evolve. A recent article provides a small example. As we view this evolution through the lens of accelerants and obstacles, much has been said about regulations and the limitations of data and compute power (potential obstacles). The article identifies two possible accelerants: intensifying competition and new sources of data. Here is a brief summary.

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The AI Revolution In Medicine

I finished reading a very timely book on the exploding topic of artificial intelligence. Two very important reasons to read this book: 1) the author’s early work with GPT-4 and 2) the focus on medicine. A dominant conversation occurring in board rooms across the world involves the critical question of generative AI and its impact on a business and/or an industry. One of the authors is Peter Lee, Corporate VP for Research and Incubations at Microsoft. He leads the company’s worldwide research labs. For the past six years, his primary focus has been on AI’s uses in healthcare and the life sciences.

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The Age Of AI

The other day I reflected on a future of transportation post via Bill Gates. Today, I am writing about his recent post on the age of AI. Driven by the fascination of ChatGPT, I’ve heard the phrase “The Age of AI” multiple times recently. The launch of ChatGPT made what was lurking beneath the surface visible – the same effect that the pandemic had in making the word “resilience” a critical part of our vocabulary. Artificial intelligence was already on this path, but this recent exposure is making it real for many. I asked this question initially in February 2020: Will Artificial Intelligence be more impactful than fire, electricity, or the Internet? I followed it up with a second poll late in 2022. The community answered in the following way.

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