
26 segments available
Full episode with Stephen Wolfram (Apr 2020): https://www.youtube.com/watch?v=ez773teNFYA Clips channel (Lex Clips): https://www.youtube.com/lexclips Main channel (Lex Fridman): https://www.youtube.com/lexfridman (more links below) Podcast full episodes playlist: https://www.youtube.com/playlist?list=PLrAXtmErZgOdP_8GztsuKi9nrraNbKKp4 Podcasts clips playlist: https://www.youtube.com/playlist?list=PLrAXtmErZgOeciFP3CBCIEElOJeitOr41 Podcast website: https://lexfridman.com/ai Podcast on Apple Podcasts (iTunes): https://apple.co/2lwqZIr Podcast on Spotify: https://spoti.fi/2nEwCF8 Podcast RSS: https://lexfridman.com/category/ai/feed/ Stephen Wolfram is a computer scientist, mathematician, and theoretical physicist who is the founder and CEO of Wolfram Research, a company behind Mathematica, Wolfram Alpha, Wolfram Language, and the new Wolfram Physics project. He is the author of several books including A New Kind of Science, which on a personal note was one of the most influential books in my journey in computer science and artificial intelligence. Subscribe to this YouTube channel or connect on: - Twitter: https://twitter.com/lexfridman - LinkedIn: https://www.linkedin.com/in/lexfridman - Facebook: https://www.facebook.com/lexfridman - Instagram: https://www.instagram.com/lexfridman - Medium: https://medium.com/@lexfridman - Support on Patreon: https://www.patreon.com/lexfridman
Stephen Wolfram introduces the Wolfram Language, discussing its foundational tools like Mathematica and Wolfram Alpha. He explains how it differs from traditional programming languages by focusing on symbolic computation, allowing users to work with abstract concepts rather than just raw data. This segment sets the stage for understanding the unique capabilities of Wolfram Language.
"what is Wolfram language in terms of sort of I mean I can answer the question for you but is it basically not the philosophical deep to profound the impact of it I'm talking about in terms of tools in..."
Wolfram elaborates on the concept of symbolic computation within the Wolfram Language. He highlights how users can represent real-world entities symbolically, enabling powerful computations that go beyond traditional programming. This segment emphasizes the language's design philosophy aimed at abstract representation and manipulation of knowledge.
"mean I don't know how to clean they express that but that makes a very distinct from what how we think about sort of I don't know programming in a ling like python or something right but so so the poi..."
In this segment, Wolfram discusses the historical context of the Wolfram Language's development, tracing its roots back to his earlier work on SMP. He describes the language's evolution towards being a high-level computational tool that encompasses a wide range of functions, showcasing its unique approach compared to other programming languages.
"that's what I mean you know when I started designing well I designed the predecessor of what's now often language the thing called SMP which was my first computer language I am I kind of wanted to hav..."
Wolfram provides a glimpse into the extensive library of functions available in the Wolfram Language, highlighting its versatility. He shares examples of various functions, including those related to machine learning and data manipulation, illustrating how users can leverage these tools for diverse applications.
"for fun I'll pick them let's take a random sample of them of all the things that we have here so let's just say random sample of 10 of them and let's see what we get Wow ok so these are really differe..."
This segment focuses on the image identification capabilities of the Wolfram Language, showcasing how it can classify objects in images. Wolfram discusses the integration of AI and machine learning within the language, emphasizing the potential for combining symbolic reasoning with statistical methods to enhance computational tasks.
"yeah yeah I mean you know so one of those functions is like image identify as a function here we just say image identify you know it's always good to let's do this let's say current image and let's pi..."
Wolfram reflects on the relationship between the Wolfram Language and the broader field of AI. He critiques past AI efforts and discusses the importance of a comprehensive knowledge base for effective natural language understanding. This segment highlights the vision for future AI developments leveraging the Wolfram Language's capabilities.
"of the that's like a part of the language yes I mean you know something like um I could say I don't know let's find the geo nearest what could we find let's find the nearest volcano um let's find the ..."
In this concluding segment, Wolfram discusses the significance of computable knowledge in the context of machine learning. He argues for the importance of encoding existing knowledge into computational forms, allowing machine learning tools to explore and utilize this knowledge effectively. The segment wraps up with insights into the future of Wolfram Language and its role in advancing AI.
"form that but like what was the context of history of AI if you could just comment on there is a you said computation X and there's just some sense where in the 80's and 90's sort of expert systems re..."
In this segment, Wolfram elaborates on the ongoing interaction between NLP using machine learning and precise computational methods in natural language understanding. He discusses how this synergy enhances both fields, leading to improved outcomes in processing and understanding language.
"added I mean right now with the version 12 oh yeah if you all seeing some demos it looks amazing right I mean I think you know this it's sort of interesting to see the this sort of the once it's compu..."
Wolfram reflects on the adoption of Wolfram Language among high-end researchers versus general users. He discusses the challenges of spreading innovative ideas and the dynamics of market penetration, emphasizing the need for a coherent vision and leadership in the development of the language.
"improving both of them yeah I think you have some of the state-of-the-art transforms okay have Bert in there I think oh you know so Josie of you're integrating all the models I mean this is the hybrid..."
Wolfram addresses the complexities of distributing knowledge and maintaining quality in the development of Wolfram Language. He shares insights on the importance of leadership and a unified vision in building a robust knowledge base, which is essential for the integrity of the language.
"because we really you know the high-end we've really been very successful in for a long time and it's it's some but was you know that's partly I think a consequence of my fault in a sense because it's..."
In this segment, Wolfram discusses the ambitious project of creating the Wolfram Knowledge Base. He shares his initial fears and the methodology behind ingesting vast amounts of information, emphasizing the finite nature of knowledge and the importance of systematic implementation.
"you can't distribute that effort yeah it's not the nature of how things work I mean you know what we're trying to do is a thing that for better or worse requires leadership and it requires kind of mai..."
Wolfram explains the development of Wolfram Alpha, a system designed to answer questions using natural language. He describes how it translates queries into computable language, leveraging the extensive knowledge base built over decades to provide accurate answers.
"company and so on that doesn't have you know a bunch of investors you know telling us we're gonna do this so that they have lots of freedom in what we can do and so for example we're able to oh I don'..."
Wolfram reflects on the journey of building the Wolfram Knowledge Base, discussing the initial challenges and the gradual process of acquiring knowledge. He emphasizes the importance of collaboration with experts in various fields to enhance the depth and breadth of the knowledge base.
"language what's the let's call it the Wolfram knowledge base knowledge base I mean it's it's been a that's been a big effort over the decades to collect all that stuff and you know more of it flows in..."
Stephen Wolfram shares his initial assumptions about building Wolfram Alpha and how he believed it required solving the general AI problem. He discusses his shift in focus towards exploring the computational universe and developing the principle of computational equivalence, which suggests a continuum between intelligence and computation.
"up such a such a difficult engineering notion at some point right right well the thing that happened with me which was kind of it's a it's a live your own paradigm kind of theory so basically what hap..."
Wolfram recounts the early days of his project, where he and his team aimed to ingest all knowledge from a reference library. He reflects on the daunting nature of this task while recognizing the finite nature of knowledge, emphasizing the importance of expert input in achieving their goal of expert-level knowledge representation.
"thing with computable knowledge forever and you know let me actually try and do it and so it was you know if my if my paradigm is right then this should be possible but the beginning was certainly you..."
Wolfram discusses the evolution of question-answering systems and his experience demonstrating their capabilities to AI pioneer Marvin Minsky. He highlights the significance of solving smaller problems within the larger AI framework, which ultimately led to the successful development of functional systems.
"what happened was sort of interesting there was from a methodology point of view was I didn't start off saying let me have a grand theory for how all this knowledge works it was like let's you know im..."
Wolfram reflects on the role of optimism and ego in undertaking large projects. He shares insights on the challenges of managing long-term projects and the importance of maintaining a balance between ambition and practicality in the pursuit of groundbreaking innovations.
"from the very beginning and it's now as you know like sir and so it's people are kind of getting more of the you know they get more of the sense of this is what should be possible to do I mean in a se..."
Wolfram discusses the foundational knowledge base behind Wolfram Language and Wolfram Alpha. He contrasts this with the limitations of existing information aggregators like Wikipedia, emphasizing the need for a deeper understanding of knowledge representation and the challenges involved in automating knowledge.
"I had the practical experience of building big systems I have the sort of intellectual confidence to not just sort of give up and doing something like this I think that the you know it is a it's alway..."
Wolfram delves into the ethical considerations of automated content selection on the internet. He discusses the necessity of encoding ethical principles into AI systems and the complexities of creating diverse ethical frameworks to guide content moderation and selection.
"doing the incredibly energetic things that I'm trying to do with all from language and so on I mean vision yeah and underlying that I mean I just talked for the second time with Elon Musk and that you..."
Wolfram explores the future of computable knowledge and its implications for various fields, including legal and ethical considerations. He emphasizes the importance of developing computational contracts that can express complex human scenarios and ethical dilemmas in a computable format.
"information searchable accessible but that's really as defined it's it's a it doesn't include the understanding of information right do you hope to make all of knowledge represented with the hope so t..."
Wolfram discusses the potential for encoding different ethical ideologies into AI systems. He reflects on the challenges of creating a diverse range of AI ethics modules that can represent various societal values and the implications for future AI development.
"and what I kind of well a bunch of different things I realized about that but one thing that's interesting is being able you know in effect you're building sort of an AI ethics you have to build an AI..."
In this segment, Wolfram discusses the idea of computational contracts, which could automate decision-making processes in various fields, including news credibility and content ranking. He highlights the challenges and opportunities of creating a system that can evaluate and rank information, emphasizing the importance of multiple perspectives in AI ethics.
"mean the the the you know that's we often language has sort of the best opportunity to kind of express those essentially computational contracts about what to do now there's a bunch more work to be do..."
Wolfram shares his vision for a symbolic discourse language that can represent complex human desires and actions in a computable form. He argues that achieving this would revolutionize how we interact with AI, allowing for more nuanced and meaningful exchanges that go beyond simple commands or queries.
"for anything is more important the the fundamental nature of reality or depends who you ask it's one of these things that's exactly like you know what's the ranking right it's the it's the ranking sys..."
Wolfram discusses the potential for AI to transform legal contracts into computational forms, making them more efficient and less prone to misinterpretation. He reflects on historical attempts to automate knowledge representation and how advancements in technology could finally make this vision a reality.
"probably the easiest of the AI ethics situations because it is each person gets to pick for themselves and there's not a huge interplay between what different people pick by the time you're dealing wi..."
In this segment, Wolfram critiques the Turing Test as a measure of intelligence, arguing that it oversimplifies the complexities of human-like interaction. He discusses the limitations of current AI systems and how they differ from human cognition, emphasizing the need for a deeper understanding of intelligence in the context of AI.
"have this autonomously executing smart contract the idea of a computational contract is just to say you know have something where all of the conditions of the contract are represented in computational..."
Wolfram reflects on the historical context of AI development, referencing Gottfried Leibniz's early ideas about computational knowledge. He contrasts past challenges with current advancements, suggesting that the layers of automation available today make previously impossible tasks achievable.
"more difficult is it than what we have now I'm often language to express I call it symbolic discourse language being able to express sort of everything in the world in kind of computational symbolic f..."