Business, Innovation, and Managing Life (April 5, 2023)
Dec 1, 2023
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Stephen Wolfram answers questions about programming, computational thinking, and managing life. They discuss the development of Wolf Language, grading answers in a class, understanding country borders, learning in MBA school, systematic learning, determining principles and terms of service, exploring democratic systems, and the impact of AI innovation on jobs and social interactions.
Programming should be seen as a routine part of achieving tasks rather than a standalone skill, making computation accessible for all.
Automation layers and computational languages simplify the programming process, reducing the need for low-level programming skills.
Learning computational thinking from a young age fosters problem-solving skills, and computational language like Wolfram Language can bridge the gap between computational thinking and specific domains.
Deep dives
The Future of Programming as an Enabling Capability
Programming should be seen as a routine part of achieving tasks rather than a standalone skill. The goal is to make computation accessible for those who want to accomplish goals, not just for programmers. The advancement of computational languages, like Wolfram Language, aims to automate programming and provide a notation that is useful for humans and computers alike.
Automation and the Changing Landscape of Programming
Many programming tasks are becoming more automated, making low-level programming less necessary. With tools like LLMs (large language models) combined with computational languages, the process of programming can be simplified. Instead of manually instructing computers step by step, automation layers and computational languages, like Wolfram Language, allow for more efficient and intuitive coding.
The Importance of Learning Computational Thinking
Computational thinking is becoming a crucial skill in the 21st century. Learning how to think about problems computationally is valuable, even if one doesn't pursue a career in programming. Starting to learn computational language around age 10 or 11 can help develop this kind of thinking. Computational language, like Wolfram Language, can bridge the gap between computational thinking and specific domains.
The Role of AI and Language Models in Learning and Computation
AI, particularly large language models like GPT, can enhance learning computational language and provide valuable explanations. AI can facilitate language learning processes and aid in removing confusion during the learning journey. It brings new possibilities for tutoring mechanisms and personalized learning experiences. The combination of AI and computational language opens up potential for superhuman capabilities in analogy making and translation tasks.
AI Ethics and Decision-Making
AI ethics and decision-making raise complex questions about defining principles and ground rules for AI. Should AI be given preferences or follow diverse paths, and who defines the ethical standards? These issues extend to content curation in social media, where decisions must be made about what type of content to show users. Understanding that ethical questions do not have technical answers is crucial, as human choice plays a significant role in setting these ground rules.
Personalized AI Tutoring
AI advancements enable personalized tutoring by AI systems tailored to individual needs. The potential for an AI to teach various subjects efficiently, adaptively, and within the preferences and engagement of learners is significant. By leveraging AI, educational interactions can be optimized to enhance learning outcomes and cater to individual interests and preferred learning styles. This integration of AI into education could revolutionize personalized learning and lead to profound improvements in education systems.
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: Should I become a programmer? At what age do you think kids should start learning computer-related skills? Should programming be a core class for students, like math and English? - What do you think are good ways to introduce computational thinking to kids? - But can you really get to a point to ask if there is something that you want to do that can be solved computationally without at least going about a trial-and-error-type process? - "Human-AI coauthorship" is what I call it now. - What would be some examples of the differences between programming, mathematical thinking and computational thinking? Or is there a difference? Is this just a colloquial thing? - Would you consider hiring someone without a technical background? - What is the minimum body of knowledge one should gather before being able to produce meaningful ideas in one research area? - What was the hardest part in starting Wolfram Research? - What are your thoughts on learning things outside of your domain of expertise? How should one balance their time between diving deep into their primary domain and exploring things outside of that? - What valuable new products will Wolfram Research build using AI in the next decade? What ideas do you have that you hope others build? - What do you think is going to happen in the next five years with AIs? What's the next big "surprise" thing like ChatGPT you think will come? - What's the worst thing that could happen with AI? - Are you concerned that we are building our murderer? Or that we have to simulate worlds empty of influence to determine the genuine intentions/alignments of an AI? - Which is better: ChatGPT calling a plugin, or a plugin/standalone calling ChatGPT? Depends on the application, probably. - I'd love for an AI to be able to, for instance, teach me chess in the most optimal way by figuring out my weaknesses and how to reinforce my learning. - One thing to consider: If the galaxy is incredibly vast, why wouldn't an AI just leave Earth so that it can gather resources elsewhere? Or it could even explore the universe. Staying on Earth seems like it'd be very limiting to an AI or superintelligence. - How can one NOT get left behind socially and economically in the wake of AI innovation? - One thing I was thinking earlier is that what we're going to be seeing now is "automation of AI," where we have lots of websites and APIs that do one machine learning task well, and then we're handing off data from one model to the next. - I like the idea of LLMs acting as the core interface module for a "soup" of APIs in a cognitive/hybrid AI
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