Business, Innovation, and Managing Life (July 26, 2023)

The Stephen Wolfram Podcast - En podcast af Wolfram Research

Kategorier:

Stephen Wolfram answers questions from his viewers about business, innovation, and managing life as part of an unscripted livestream series, also available on YouTube here: https://wolfr.am/youtube-sw-business-qa Questions include: Did you see the Oppenheimer movie? If so, what were your thoughts? - What are the things one should do to prepare oneself to become a scientist regarding education path, ideas, tools in the upcoming age of computation and AI? - Can "Kelly Criterion", aka calculating size of bets to place in markets, also be a good tool to manage life? Which is to say, you limit the size of your experiments by design? - ​Are you using any LLM Functions for managing your daily workflow? If so, which ones? - What's the "next big thing" in business? How will virtual spaces (like with Apple's new headset announcement) gaining popularity impact the workplace, if at all? - I'm a software engineer with about 8 years of professional experience. I'm interested in transitioning into the field of AI/machine learning. I found it quite difficult to find careers in the marketplace that don't require 5+ years of experience in AI/machine learning. Any advice on how best to make this transition? - What would you say to people who are scared to lose their jobs to AI? There are a lot of young professionals in the tech sector that are just getting started in becoming data analysts, project managers, and engineers. We are starting to hear a lot of bustle about these careers not being good investments in the long term. - A bit of a funny lifestyle question. What's your opinion on living off-grid (living in the rural quiet area) in a modern time? - Given the computational limitations of the human brain, are there drawbacks in thinking computationally? Do we risk losing track of high level patterns with too many parts to count? - When you were starting SMP, if someone else had already made significant progress in building a full-scale computational language, what would you have done? - Any cool projects you enjoyed working with during Summer School? - Science somewhat requires integration of many disciplines but in academia, almost only way to progress in your career is to publish stuff in your "area of expertise"

Visit the podcast's native language site