
73 segments available
Douglas Lenat is the founder of Cyc, a 37 year project aiming to solve common-sense knowledge and reasoning in AI. Please support this podcast by checking out our sponsors: - Squarespace: https://lexfridman.com/squarespace and use code LEX to get 10% off - BiOptimizers: http://www.magbreakthrough.com/lex to get 10% off - Stamps.com: https://stamps.com and use code LEX to get free postage & scale - LMNT: https://drinkLMNT.com/lex to get free sample pack - ExpressVPN: https://expressvpn.com/lexpod and use code LexPod to get 3 months free EPISODE LINKS: Douglas's Twitter: https://twitter.com/cycorpai Cyc's Website: https://cyc.com PODCAST INFO: Podcast website: https://lexfridman.com/podcast Apple Podcasts: https://apple.co/2lwqZIr Spotify: https://spoti.fi/2nEwCF8 RSS: https://lexfridman.com/feed/podcast/ Full episodes playlist: https://www.youtube.com/playlist?list=PLrAXtmErZgOdP_8GztsuKi9nrraNbKKp4 Clips playlist: https://www.youtube.com/playlist?list=PLrAXtmErZgOeciFP3CBCIEElOJeitOr41 OUTLINE: 0:00 - Introduction 1:11 - What is Cyc? 9:17 - How to form a knowledge base of the universe 19:43 - How to train an AI knowledge base 24:04 - Global consistency versus local consistency 48:25 - Automated reasoning 54:05 - Direct uses of AI and machine learning 1:06:43 - The semantic web 1:17:16 - Tools to help Cyc interpret data 1:26:26 - The most beautiful idea about Cyc 1:32:25 - Love and consciousness in AI 1:39:24 - The greatness of Marvin Minsky 1:44:18 - Is Cyc just a beautiful dream? 1:49:03 - What is OpenCyc and how was it born? 1:54:53 - The open source community and OpenCyc 2:05:20 - The inference problem 2:07:03 - Cyc's programming language 2:14:37 - Ontological engineering 2:22:02 - Do machines think? 2:30:47 - Death and consciousness 2:40:48 - What would you say to AI? 2:45:24 - Advice to young people 2:47:20 - Mortality SOCIAL: - 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 - Reddit: https://reddit.com/r/lexfridman - Support on Patreon: https://www.patreon.com/lexfridman
Douglas Lenat introduces Cyc, a groundbreaking project aimed at solving the challenge of common-sense knowledge in artificial intelligence. He discusses the importance of understanding human-like reasoning and the implications of achieving general superintelligence through common-sense reasoning. This segment sets the stage for the conversation about the complexities of AI and human cognition.
"the following is a conversation with Doug lennet creator of Psych A system that for close to 40 years and still today has sought to solve the core problem of artificial intelligence the acquisition of..."
Lenat elaborates on the mission of Cyc, which began in 1984, to create a comprehensive knowledge base that encapsulates common-sense knowledge. He reflects on the challenges faced in AI research, particularly the limitations of early systems that lacked true understanding and general world knowledge. This segment highlights the foundational goals of Cyc and the obstacles encountered in AI development.
"podcast and here is my conversation with Doug lennett Psych is a project launched by you in 1984 and still is active today whose goal is to assemble a knowledge base that spans the basic concepts and ..."
In this segment, Lenat discusses the difference between genuine understanding and mere trickery in AI systems. He uses the analogy of a dog performing tricks to illustrate how AI can mimic intelligent behavior without true comprehension. Lenat emphasizes the need for AI to possess a solid foundation of common-sense knowledge to make informed decisions in unexpected situations.
"so very much like a um um a clever dog performing tricks we could get them to do tricks but they never really understood what they were doing sort of like when you get a dog to fetch your morning news..."
Lenat shares a personal anecdote about a driving incident involving a trash truck to illustrate the necessity of common sense in decision-making. He explains how humans rely on their deep knowledge of the world to navigate unforeseen circumstances, emphasizing that AI must also leverage a vast repository of common-sense knowledge to function effectively in real-world scenarios.
"instance um my wife and I were driving recently um and there was a trash truck in front of us and um I guess they had packed it too full and the back exploded and trash bags went everywhere uh and we ..."
Lenat distinguishes between the theoretical and practical aspects of utilizing knowledge in AI. He discusses the philosophical underpinnings of representing common-sense knowledge in logical forms and the challenges of efficiently processing this information. This segment delves into the complexities of knowledge representation and the need for rapid decision-making in AI systems.
"the knowledge are not useful selecting only the useful parts and uh effectively making conclusive decisions so let's tease apart two different tasks really both of which are um incredibly important an..."
In this segment, Lenat addresses the question of how much common-sense knowledge is necessary for AI systems to function effectively. He recounts a meeting with experts who estimated that around a million pieces of knowledge would be required, only to later discover that the actual need is in the tens of millions. This discussion highlights the scale of knowledge required for AI to achieve a human-like understanding of the world.
"uh questions and and like a corollary addition to the first one is how many such things do you need to gather for it to be useful in certain contexts so like what in order you mentioned philosophers i..."
Lenat reflects on the evolution of Cyc since its inception, detailing the shift from basic common-sense knowledge to more specialized domain knowledge. He explains how the project has adapted over the years to incorporate vast amounts of information, ultimately aiming to create a robust AI system capable of handling complex real-world applications.
"one at a time so um in um the um 19 early um 1984 um I held a meeting at Stanford where I was a faculty member there um where we assembled um about half a dozen of the smartest people I know um people..."
Lenat discusses the funding and resources that have supported the development of Cyc, particularly in light of the competitive landscape of AI research. He describes how government initiatives and collaborative efforts have enabled the project to expand its knowledge base significantly, allowing for a more comprehensive approach to common-sense reasoning in AI.
"like maybe one every 30 seconds or something and other than that it's just shortterm memory and it flows away like water and so on so by the time you're say 10 years old or so um how many things could..."
In this segment, Lenat outlines various techniques used to acquire common-sense knowledge for Cyc. He explains how analyzing text and understanding the assumptions behind language can help identify gaps in knowledge. This discussion emphasizes the importance of context and inference in building a robust knowledge base for AI.
"this written down by hand and a marvelous um coincidence opportunity existed um right at that point in time the early 1980s there was something called the Japanese fifth generation Computing effort Ja..."
Lenat concludes by discussing how Cyc leverages its extensive knowledge base to make informed decisions in real-time. He illustrates the importance of having a solid foundation of common-sense knowledge to navigate unexpected situations, reinforcing the idea that understanding is not just about having information but also about effectively utilizing it.
"trouble to make each one as general as it possibly could be um but um the good news was that thanks to um uh initially the fifth generation effort and then later um US Government um agency funding and..."
Douglas Lenat discusses innovative techniques for extracting common-sense knowledge from text. He emphasizes the importance of understanding what assumptions writers make about their readers, such as the use of pronouns and ambiguous terms. By analyzing the gaps between sentences, Lenat illustrates how readers are expected to infer context and meaning, which is crucial for building a robust AI knowledge base.
"um a few um techniques which worked really well one is to take um any piece of text almost could be an advertisement it could be a transcri scpt it could be a novel it could be an article um and don't..."
Lenat shares insights on the challenges of maintaining global consistency in AI knowledge bases. He explains how Cyc has shifted towards a model of local consistency, allowing for contextual variations and acknowledging that inconsistencies are a natural part of knowledge representation. This approach enables the system to handle real-world complexities more effectively.
"you don't want to say things like um no mouse is also a moose uh because if you said things like that um then you'd have to add another one or two or three zeros onto the number of assertions you'd ac..."
In this segment, Lenat elaborates on the significance of context in Cyc's knowledge base. He explains how contexts are treated as first-class objects, allowing the system to reason about different contexts hierarchically. This structure helps avoid redundancy and ensures that knowledge is efficiently inherited across various levels of specificity.
"move to was a system of local consistency so a good analogy is you know that the surface of the Earth is more or less spherical globally um but you live your life every day as though the surface of th..."
Lenat discusses the limitations of tree structures in knowledge representation, advocating for graph structures instead. He highlights how graph-based representations allow for multiple parent nodes, enabling a more nuanced understanding of concepts. This flexibility is crucial for accurately modeling the complexities of real-world knowledge.
"specific um contexts to ask a slightly technical question is this inheritance inheritance a tree or a graph oh you definitely have to think of it as a graph um so we could talk about for instance why ..."
Lenat reflects on the evolution of AI and its potential impact on human intelligence. He posits that advancements in AI will not only enhance human capabilities but also redefine what it means to be intelligent. By collaborating with AI, humans will be able to tackle complex problems more creatively and efficiently, marking a significant leap in collective intelligence.
"be quite useful I think and in that same way conversation of just how many facts are required to capture the basic Common Sense knowledge of the world that's a fascinating question I want to distingui..."
Douglas Lenat discusses the transformative potential of general artificial intelligence (AI) in enhancing human intelligence. He argues that rather than a competition between humans and AI, the future lies in collaboration, where AI augments human creativity and problem-solving capabilities. Lenat reflects on historical milestones in human intelligence, suggesting that future generations may view today's humans as less capable compared to those enhanced by AI.
"artificial intelligence human level artificial intelligence then people will become smarter um it's not so much that it'll be us versus the AIS it's more like us and the AIS together will'll be able t..."
Lenat presents a nuanced view of the internet's impact on human knowledge and intelligence. He acknowledges the internet's role in democratizing access to information while also highlighting its potential to foster ignorance and dependency. He draws parallels between the internet and historical advancements like language, suggesting that while technology can enhance capabilities, it can also lead to a decline in critical thinking and understanding.
"Muses um and say you know those poor those poor people they weren't really human were they mhm exactly so you said a lot of really interesting things by the way I would maybe uh try to argue that the ..."
In this segment, Lenat explores how the internet creates echo chambers that reinforce existing beliefs and misinformation. He contrasts this with historical media landscapes, emphasizing how modern technology allows individuals to curate their information sources, which can lead to distorted perceptions of reality. He reflects on the implications of this phenomenon for society and the potential for AI to challenge entrenched narratives.
"um and so on they don't really understand the difference between trillions and billions and millions and so on very well um uh because calculators do that all for us um and um thanks to uh things like..."
Lenat argues that the advancement of AI is crucial for addressing pressing global issues such as poverty, starvation, and climate change. He posits that enhancing human intelligence through AI could be one of the few viable paths to overcoming these challenges. Lenat emphasizes the importance of developing general AI to augment human capabilities and tackle complex problems facing humanity.
"sorts of dependencies um on technology um and overall I think that the um having smarter individuals and having smarter AI augmented um human species um will be um one of the few ways that we'll actua..."
Lenat discusses the dual nature of technological advancements, particularly in AI and biology. He expresses optimism about AI's potential to empower individuals while cautioning against the unpredictable nature of biological systems. This segment highlights the need for careful consideration of how technology can be harnessed for good while being aware of its potential risks.
"through before it's too late yeah absolutely I agree with you and obviously as an engineer I have um I have a better sense and an optimism about the technology side of things because you can control t..."
Lenat emphasizes the role of AI in enhancing critical thinking skills among individuals. He envisions AI systems that not only provide information but also encourage users to engage in deeper reasoning and analysis. This segment discusses the potential for AI to serve as a personal assistant that fosters better decision-making and reduces cognitive biases.
"networks then propaganda could be much more effective and then the government can overpower its people by telling you the truth and then uh starving millions and uh torturing millions and putting Mill..."
In this segment, Lenat explores the integration of emotional intelligence into AI systems. He argues that AI should not only focus on intellectual growth but also on fostering emotional connections, companionship, and happiness. Lenat highlights the importance of developing AI that understands and enhances the human experience beyond mere logic.
"um they should take into consideration um if they really want to um um learn the truth about something and so on so I think um doing not just educating in the sense of um pouring facts into people's h..."
Lenat delves into the challenges of automated reasoning and knowledge acquisition in AI. He discusses the historical context of knowledge gathering and the need for AI systems to learn and adapt without extensive human intervention. This segment introduces concepts like abduction in reasoning and the development of tools to facilitate knowledge entry and editing.
"amazing is to to sort of you know the the old Pursuit of Happiness so it's not just the pursuit of reason it's the pursuit of happiness too yes the the full spectrum well let me um sort of because you..."
Lenat describes a method for improving AI systems through human expertise. He explains how AI can present plausible alternatives when it makes errors, allowing human users to guide the learning process. This collaborative approach aims to reduce the cognitive load on human teachers while enhancing the AI's understanding and capabilities.
"um you know if something causes you pain whenever you do it probably not a good idea to keep doing it uh so what ideas do you have given how difficult this step is what ideas are there for how to do i..."
In this segment, Lenat discusses the importance of causal reasoning in AI systems. He illustrates how AI can use common sense knowledge to construct causal chains that explain correlations in data. This approach not only aids in understanding complex relationships but also allows for predictive capabilities that can be tested against real-world data.
"system um in areas where for instance they want to build some have it do some application or something uh like that so I'll give you an example of one um which is something called um abduction so you'..."
Lenat emphasizes the synergy between machine learning and common sense reasoning in AI development. He argues that while machine learning excels at pattern recognition, common sense reasoning is essential for deeper understanding and interpretation. This segment highlights the need for a balanced approach that leverages both capabilities to achieve true general AI.
"of the wrong answer I came up with is one of these seven things true and then you the expert will look at those seven things and say oh yeah number five is actually true and so without actually having..."
Douglas Lenat discusses the importance of causal chains in understanding correlations within data. He explains how these chains can lead to predictions, such as the relationship between bioactive vitamin D levels and health outcomes. This segment emphasizes the need for researchers to focus on meaningful correlations that can be confirmed or disconfirmed through data analysis.
"you can't even think of one causal chain to explain this um then that correlation probably was just noise to begin with and secondly and even more powerfully along the way that caused puzzle chain wil..."
Lenat elaborates on the synergy between machine learning and AI in the context of knowledge acquisition. He highlights the potential of self-supervised learning methods to automate the growth of Cyc's knowledge base, emphasizing the importance of a solid foundational core for effective learning and knowledge expansion.
"expansion of the knowledge base you mentioned nlu this is very early days in the machine learning space of this but self-supervised learning methods you know you have these language models gpt3 and so..."
In this segment, Lenat reflects on the challenges of knowledge acquisition in AI systems. He discusses the necessity of a robust initial knowledge base to facilitate further learning and warns against the brittleness of current machine learning systems, using examples from GPT-3 and Siri to illustrate the limitations of understanding in AI.
"and that's what I meant by priming the pump that um you know if you you sort of learn things at The Fringe of what you know already you learn this new thing is similar to what you know already and her..."
Lenat shares a personal anecdote about his daughter's medical diagnosis to illustrate the critical need for AI systems to provide explanations for their recommendations. He argues that without a deep understanding of the world, AI can lead to dangerous outcomes, especially in high-stakes situations like medical care.
"for example in um gpt3 um where um it uh uh you know you'll tell it a story about um um you know someone uh putting a poison um you know plotting to poison someone and so on and then the um you know t..."
Lenat discusses the concept of the semantic web and its potential to transform how data on the internet is understood by machines. He critiques current implementations, suggesting that while they are steps in the right direction, they fall short of achieving true semantic understanding.
"really acceptable I like the model of what that learning happens at the edge and then you kind of think of knowledge as this sphere so uh if you want a large sphere because the uh the learning is happ..."
In this segment, Lenat explains the structure of knowledge graphs and their utility in representing binary relations. However, he emphasizes that they are insufficient for capturing complex human reasoning and understanding, which is necessary for advanced AI applications.
"mine Ted shortliffe was another um assistant professor in computer science um at Stanford at the time and he'd been building a program called M which was a medical diagnosis program that happened to s..."
Lenat argues for the need to represent more complex expressions and reasoning in AI systems. He discusses the limitations of simple graph structures and the necessity for higher-order logic to capture the nuances of human thought and communication.
"you should get this operation and you say why and it says you know 083 and you say no in more detail why and it says 0831 you know okay that's not really very compelling and that's not really very hel..."
Lenat reflects on the original dream of the semantic web, which aims for machines to truly understand the content they process. He critiques the current state of AI and search engines, emphasizing the need for deeper semantic understanding rather than surface-level keyword matching.
"want to represent um binary relations um um essentially relations between two things and if you so if you have um um the equivalent of like three-word sentences um you know like uh Fred's wife is Wilm..."
Lenat shares an anecdote about a now-defunct search engine, Northern Light, to illustrate the limitations of current search technologies. He emphasizes the importance of understanding user queries and the content of web pages to improve search results.
"represent and reason with these much much much more complicated Expressions um that go Way Way Beyond what simple um three as it were three-word or four-word English sentences are which is really what..."
In this segment, Lenat discusses the future of semantic understanding in AI and the potential for knowledge graphs to evolve. He advocates for a more meaningful representation of data that goes beyond simple statistical mappings.
"the word understand the contents of the Wikipedia Pages as part of the search process and the semantic web says let's try to get come up with a way for the computer to be able to truly understand the ..."
Lenat explores how content creators can make their information more accessible to AI systems. He suggests the development of tools that help AI understand and interpret human-written content, enhancing the interaction between humans and machines.
"users to where they' want to go and so on and of course the search engines are now using that kind of idea yes so um let's go back to what you said about the semantic web so the dream Tim berners Ley ..."
Lenat proposes a system where AI can provide feedback on human-written content, similar to footnotes in academic writing. This would help clarify ambiguous statements and improve the AI's understanding of the content, ultimately benefiting future users.
"reason with um full meaning of what's there for instance um about um Romeo and Juliet um with reasoning about what Juliet believes that Romeo will believe that Juliet believed you know and so on or if..."
Douglas Lenat discusses the potential for AI systems to provide semi-automated understanding of human language. He envisions tools that can analyze written content, highlight ambiguities, and suggest clarifications to enhance comprehension for both humans and AI. This segment explores how such systems could improve user experience and facilitate better communication between humans and machines.
"it I I'd love to see exactly that kind of semi-automated understanding of what people write and what people say I think of it as a kind of footnoting almost almost like the way that when you run somet..."
Lenat shares insights on the Mathcraft program, designed to help students learn math by mentoring peers. He emphasizes the importance of creating an educational environment where students can teach and learn from each other, enhancing their understanding through the act of teaching. This segment highlights the benefits of a mentor-student dynamic in educational software and its implications for AI learning.
"and so if it understands that there's a free day in Geneva Switzerland uh then if the person coming in happens to let's say um be a nurse or something like that then even though you didn't mention it ..."
In this segment, Lenat reflects on the emotional aspects of teaching AI systems, comparing it to parenting. He discusses the joy and significance of imparting knowledge to machines, emphasizing that each lesson taught could be a unique contribution to the AI's understanding. This exploration touches on the emotional connections humans have with teaching and the implications for AI development.
"kind of experience if you look at educational software today almost all of it has the computer playing the role of the teacher and the student plays the role of the student but as I just mentioned you..."
Lenat discusses the ongoing nature of knowledge evolution in AI systems, likening it to a living organism. He explains how knowledge grows and changes over time, necessitating continuous updates and modifications. This segment emphasizes the dynamic relationship between AI and human knowledge, highlighting the importance of adaptability in AI systems.
"education uh Institution as we know it today sorry for the Romantic question but what is the most beautiful idea you've learned about artificial intelligence knowledge reasoning from uh working on psy..."
Lenat elaborates on the significance of incorporating human emotions into AI knowledge bases. He explains how understanding emotions is crucial for AI to generate plausible scenarios and respond appropriately to human behavior. This segment underscores the complexity of human emotions and their role in enhancing AI's reasoning capabilities.
"clarifying questions which inadvertently turn out to be emotional to us so at one point it knew that these were the named entities who were authorized to make changes to the knowledge base and so on a..."
In this segment, Lenat discusses how Cyc represents various aspects of love within its knowledge base. He explains the importance of distinguishing between different types of love, such as romantic love and parental love, to enhance AI's understanding of human nature. This exploration highlights the complexities of language and the need for precise definitions in AI systems.
"scenarios so one of the applications that psych does one kind of application it does is to generate plausible scenarios of what might happen and what might happen based on that and what might happen b..."
Lenat reflects on the inherent ambiguity of human language and its implications for AI understanding. He discusses how language can be both a tool for communication and a source of confusion, emphasizing the need for AI to navigate these complexities. This segment explores the challenges AI faces in interpreting human language and the importance of context.
"don't have to deal with love to get to that level of complexity even something like in X being in y meaning physically in y uh we may have one English word in to represent that but it's useful to teas..."
Lenat shares anecdotes about Marvin Minsky and his unique approach to mentoring students. He highlights Minsky's ability to inspire creativity and critical thinking through cryptic statements, encouraging students to derive meaning from ambiguity. This segment celebrates Minsky's legacy and his impact on the field of artificial intelligence.
"there there's some dance of like uncertainty of wit of humor of push and pull and all that kind of stuff if everything is made precise then life is not worth living I think for in terms of the The Hum..."
Lenat reflects on the finite nature of research careers, emphasizing the importance of making impactful contributions in AI. He encourages researchers to tackle significant challenges and to be open to being wrong, as this mindset can lead to groundbreaking advancements in the field.
"seconds so the operator would give it another 20 seconds it would wait it says I'm almost done I need a little bit more time so at the end he'd get this print out and he'd be charg you know for like 1..."
In this segment, Lenat discusses the long-term commitment to the Cyc project, addressing the challenges faced over 37 years. He shares insights on funding difficulties and the temptation to sell the company, reinforcing the importance of maintaining control over their vision and mission in AI development.
"projects so you should make each one count don't be afraid of doing a project that's going to take years or even decades to and don't settle for bump on a log projects that could lead to some you know..."
Lenat talks about the need for Cyc to raise its profile and engage the community in its mission. He emphasizes the importance of collaboration and contributions from individuals interested in AI, particularly retirees and those with time to invest in meaningful projects, to help advance the knowledge base of Cyc.
"never really materialized that's how it's spoken about um I suppose you could say the same thing about new all networks and all all all ideas um until they are so what um why do you think people say t..."
Lenat discusses the potential of open-sourcing parts of Cyc to leverage the open-source community. He reflects on the balance between maintaining proprietary knowledge and sharing valuable resources to foster collaboration and innovation in AI research.
"begin to harness um this untapped amount of humanity I'm not really that concerned about professional colleagues opinions of our project I'm interested in getting as many people in the world as possib..."
In this segment, Lenat elaborates on the complexities of deciding what to open source from Cyc. He highlights the importance of sharing knowledge while protecting proprietary information, and the challenges of collaborating with researchers who may not fully appreciate the value of Cyc's comprehensive knowledge base.
"that almost all of our work is commercial applications of our technology so five years ago almost all of our money came from the government now uh virtually none of it comes from the government almost..."
Lenat explains the distinction between epistemological and heuristic approaches in AI. He discusses the development of a higher-order logic representation for knowledge in Cyc, emphasizing the need for efficient reasoning methods and the integration of various heuristic modules to enhance AI capabilities.
"and it will be um openly available to everyone who has access to psych so that's an important constraint that we never went back on even when we got push back from companies which we often did who wan..."
In this segment, Lenat explains the concept of meta reasoning, which allows Cyc to dynamically prune unnecessary knowledge and focus on relevant information. He discusses how this approach has led to significant speedups in the inference process, highlighting the balance between knowledge representation and efficient reasoning.
"that community of Agents gets an answer to your question gets a solution to your problem and if we ever come up in a domain application where Psych is getting the right answer but taking too long um t..."
Lenat addresses the complexity of inference problems in AI, noting that a significant portion of Cyc's efforts has been dedicated to inference programming. He contrasts the roles of inference programmers and ontological engineers, emphasizing the unique skills required for each role in the development of Cyc.
"on so that that's pretty much one of the main ways in which um psych has recouped this lost deficiency um a second important way is meta reasoning so um you can speed things up by focusing on removing..."
Douglas Lenat shares insights into the role of ontological engineers in Cyc, highlighting how their expertise often comes from diverse backgrounds rather than traditional programming. He draws an analogy to H.G. Wells' 'The Time Machine,' illustrating the division of labor between those who create knowledge and those who implement it.
"not as impressive so there's these kind of heuristic modules that really help improve the the inference uh how hard in general is this because you mentioned higher order logic you know the the in in t..."
Lenat discusses the programming languages used in Cyc, primarily Lisp, and the challenges of technical debt. He explains how the system has evolved to translate Lisp code into more modern languages like Java for efficiency, while maintaining the foundational logic that underpins Cyc's reasoning capabilities.
"content of what the system needs to know and yeah both are fascinating to some degree the entirety of the system as far as I understand is written in various variants of lisp so my favorite programm l..."
In this segment, Lenat reflects on the balance between the value of knowledge and the software engineering aspects of Cyc. He considers the implications of potentially starting from scratch with a new codebase and how much of Cyc's value lies in its extensive knowledge base versus its underlying software.
"compiled down to U by code yes okay so that that's sort of that's a that um that's a you know it's a process that probably has to do with the fact that when py was originally written and you build up ..."
Lenat explains the learning curve for new inference programmers at Cyc, emphasizing the importance of understanding Lisp and its dialect, Subel. He discusses how this knowledge is crucial for developing new HL modules and improving the efficiency of the inference process.
"much of the value is in the silly software engineering aspect and how much of the value is in the knowledge so development of of programs in lisp um precedes um I think somewhere between a th000 and 5..."
Lenat shares insights into the traits that make someone a successful ontological engineer. He highlights the importance of humor, introspection, and the ability to think deeply about the world, suggesting that these qualities are more indicative of potential success than formal education.
"other thing is remember that we're talking about hiring programmers to do inference who are programmers interested in effectively automatic theorem proving right and so those are people already predis..."
In this segment, Lenat discusses the concept of transitive relations and how redundant data structures can enhance efficiency in reasoning. He provides examples of how storing transitive closures can lead to faster answers in inference tasks, illustrating the practical applications of these ideas.
"um it's more that you have to be able to think in terms of for instance creating new helpful HL modules and how they'll work with each other and um looking at things that are taking a long time and co..."
Lenat introduces the idea of rule macro predicates, which simplify complex rules into more manageable forms. He explains how this transformation can significantly speed up the inference process, showcasing the innovative strategies employed within Cyc to enhance reasoning capabilities.
"efficiently let me give you one other um analogy analog of that uh which is something we call rule macro predicates which is we'll see this complicated Rule and we'll notice that things very much like..."
Lenat reflects on the disconnect between traditional education and the skills needed for ontological engineering. He shares his experiences with candidates from various educational backgrounds, emphasizing the need for practical skills over formal qualifications in this specialized field.
"useful yeah that's really interesting I mean this whole thing is just fascinating for a philosophical there's part of me I mean it makes me a little bit sad because your work is both um from a compute..."
Douglas Lenat discusses the complexities of teaching AI to reason about the world, emphasizing the importance of deep introspection over mere syntactical understanding. He reflects on how education can sometimes narrow thinking rather than expand it, particularly in the context of ontological engineering, which aims to equip AI with the ability to reason deeply about reality.
"head was still wet and so its head refers to the horse's head but how do you know that and so some people will say I just know it some people will say well the horse was the subject of the sentence an..."
In this segment, Lenat engages in a philosophical discussion about whether machines can think, referencing Alan Turing's seminal work. He argues that machines can think as well as humans, challenging the notion that intelligence is solely a human trait and critiquing the limitations of traditional tests like the Turing Test.
"education system but let me ask you let me go super philosophical as if we weren't already so in 1950 Alan touring wrote the paper that formulated the touring test yes and he opened the paper with the..."
Lenat proposes a new framework for evaluating AI intelligence that includes depth of reasoning and the ability to engage in recursive questioning. He critiques existing tests, such as the Turing Test, and emphasizes the need for a more comprehensive approach that assesses an AI's ability to understand and argue both sides of a proposition.
"created something special here the the test has to be something involving depth of reasoning and recursiveness of reasoning the ability to answer repeated why questions about the answer you just gave ..."
The conversation shifts to the concept of consciousness in AI, with Lenat asserting that while AI does not need a physical body to be intelligent, it must understand human experiences and emotions. He discusses the implications of consciousness for AI and whether it can be considered conscious if it exhibits human-like responses.
"understands what it's doing that it really is generally intelligent so to pass all of those tests you know we've talked a lot about psych and knowledge and reasoning do you think this AI system would ..."
Lenat and Fridman explore the idea of mortality and its impact on human creativity and behavior. They debate whether AI needs to understand the concept of death to function effectively, with Lenat suggesting that while it may not be necessary, understanding the value of human life is crucial for ethical decision-making in AI.
"Consciousness just as much so there's another burden that comes with this whole intellig thing that humans got is um the extinguishing of the light of Consciousness which is uh kind of realizing that ..."
In this segment, Lenat discusses the challenges of developing autonomous vehicles, emphasizing the intricate decision-making processes involved in driving. He highlights the societal implications of AI failures and the public's fascination with negative stories about technology, suggesting that these factors complicate the path to successful AI integration in everyday life.
"humans I don't know I think like I think AI really needs to understand death deeply in order to be able to drive a car for example I I think there's just some like there no I I really disagree I think..."
Lenat poses a thought-provoking question about the potential of AGI to identify and propose solutions to significant global issues. He emphasizes the importance of AI's ability to recognize human blind spots and contribute novel ideas that could lead to meaningful change, reflecting on the unique capabilities that AI might bring to problem-solving.
"stories where in fact it failed even if they're relatively few yeah it's so fascinating to watch us humans resisting The Cutting Edge of Science and Technology and almost like hoping for it to fail an..."
In this segment, Lenat reflects on historical scientific breakthroughs that challenged existing paradigms. He discusses how revolutionary ideas often face skepticism and how AI could play a role in questioning entrenched beliefs, potentially leading to significant advancements in understanding.
"that we have so the two-part question of can you help identify what are the big problems in the world and two what are some novel solutions to those problems that are not being talked about by anyone ..."
Lenat shares valuable advice for young people pursuing careers in innovation and science. He emphasizes the importance of perseverance, the courage to follow unconventional ideas, and the need to make impactful choices in a finite lifetime.
"paradigms what Thomas cun called paradigms and we can't get out of them very easily so a lot of AI is locked into the deep learning machine learning Paradigm right now um and um almost all of of us an..."
Douglas Lenat candidly discusses his thoughts on mortality as he ages. He reflects on the urgency to make a meaningful impact in the coming years and expresses his desire to be remembered as a pioneer in AI, contributing to a legacy that transcends short-term achievements.
"choice that's true in uh making discoveries and so on and if you follow the path of least resistance you'll find that you're optimizing for um short periods of time and before you know it you turn aro..."
In this concluding segment, Lenat articulates his vision for the future of AI and his hopes for being recognized as a foundational figure in the field. He emphasizes the importance of long-term dedication to innovation and the transformative potential of AI in society.
"and what it can do and the good news from your point of view is that that's that's why I'm sitting here you're going to be more productive uh I love it and if I can help in any way I would love to fro..."