Business, Innovation, and Managing Life (July 26, 2023)
The Stephen Wolfram Podcast - En podcast af Wolfram Research
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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"