Andrej Karpathy is a legendary AI researcher, engineer, and educator. He's the former director of AI at Tesla, a founding member of OpenAI, and an educator at Stanford. Please support this podcast by checking out our sponsors: - Eight Sleep: https://www.eightsleep.com/lex to get special savings - BetterHelp: https://betterhelp.com/lex to get 10% off - Fundrise: https://fundrise.com/lex - Athletic Greens: https://athleticgreens.com/lex to get 1 month of fish oil EPISODE LINKS: Andrej's Twitter: http://twitter.com/karpathy Andrej's YouTube: http://youtube.com/c/AndrejKarpathy Andrej's Website: http://karpathy.ai Andrej's Google Scholar: http://scholar.google.com/citations?user=l8WuQJgAAAAJ Books mentioned: The Vital Question: https://amzn.to/3q0vN6q Life Ascending: https://amzn.to/3wKIsOE The Selfish Gene: https://amzn.to/3TCo63s Contact: https://amzn.to/3W3y5Au The Cell: https://amzn.to/3W5f6pa 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/ YouTube Full Episodes: https://youtube.com/lexfridman YouTube Clips: https://youtube.com/lexclips SUPPORT & CONNECT: - Check out the sponsors above, it's the best way to support this podcast - Support on Patreon: https://www.patreon.com/lexfridman - Twitter: https://twitter.com/lexfridman - Instagram: https://www.instagram.com/lexfridman - LinkedIn: https://www.linkedin.com/in/lexfridman - Facebook: https://www.facebook.com/lexfridman - Medium: https://medium.com/@lexfridman OUTLINE: Here's the timestamps for the episode. On some podcast players you should be able to click the timestamp to jump to that time. (00:00) - Introduction (05:41) - Neural networks (10:45) - Biology (16:15) - Aliens (26:27) - Universe (38:18) - Transformers (46:34) - Language models (56:45) - Bots (1:03:05) - Google's LaMDA (1:10:28) - Software 2.0 (1:21:28) - Human annotation (1:23:25) - Camera vision (1:28:30) - Tesla's Data Engine (1:32:39) - Tesla Vision (1:39:09) - Elon Musk (1:44:17) - Autonomous driving (1:49:11) - Leaving Tesla (1:54:39) - Tesla's Optimus (2:03:45) - ImageNet (2:06:23) - Data (2:16:15) - Day in the life (2:29:31) - Best IDE (2:36:37) - arXiv (2:41:06) - Advice for beginners (2:50:24) - Artificial general intelligence (3:03:44) - Movies (3:09:37) - Future of human civilization (3:13:56) - Book recommendations (3:20:05) - Advice for young people (3:21:56) - Future of machine learning (3:28:44) - Meaning of life
This podcast discusses the benefits of talk therapy and encourages listeners to prioritize it in their lives. However, an advertisement for several products, including Better Help for mental health, is included towards the end.
Demis Hassabis talks about drawing wisdom from biological neural networks in the creation of neural nets and the importance of finding the right settings to achieve desired results.
The origin of life is not a magical rare event, but rather a special and punctuated equilibrium where leaps are sparse between explorations. Despite the difficulty of the process, it is uncertain whether the origin of life is rare or not.
The fate of Earth and life on it, including the chemical process and the end of the world, is discussed by the speaker in this excerpt from a podcast episode.
The transcript provided is too unclear and does not correspond to any specific podcast episode or topic.
The speaker believes that the universe is mostly deterministic, and even seemingly random events like the collapse of a wave function are actually predetermined. The meta-game for artificial general intelligence (AGI) could involve manipulating quantum mechanical systems to solve complex problems and extract infinite energy.
This episode talks about optimization with neural networks and how a powerful enough transformer can optimize each line of code to create better and more accurate predictions.
The digital world is designed for the human form of seeing the screen and using keyboard and mouse, while the physical world is designed for the human form. There is a need to give neural networks access to a keyboard and a mouse to train them to take actions on the internet.
The need for boundaries between digital and human entities is becoming more apparent, with a focus on proof of personhood through solutions like digital signatures, to trace every human program involved with internet programs.
The conversation delves into the process of AI prompting as a mode of teaching and learning, the difficulties and the implications of training an AI model using prompts, as well as GPTs' (Generative Pre-trained Transformer) capabilities as a language model.
The podcast discusses the remarkable transition of traditional programming to neural net programming, with developer environments for software 1.0, IDEs, and GitHub, and the need to embrace the shift.
The success of a neural net depends on the quality of the data sets and the architecture of the net. For an autopilot system, the data sets include object detection, lane line markings and traffic lights.
The use of pixels in computer vision for driving tasks has been the focus of the field for a long time, but it still requires significant engineering efforts in developing the pipeline to effectively train and evaluate neural network-based systems. While vision is the main tool for perceiving the external world, it requires additional reasoning to infer human behavior and common sense physics.
This podcast episode discusses the data engine, a natural process to perfect training sets for neural networks that helps in creating large, diverse and clean data sets. The insights on the philosophical process behind this engine are also discussed.
The integration of sensors into data collection and analysis systems is crucial to the success of building a large autonomous vehicle fleet. However, while vision and radar sensors are important, they do not offer unique information beyond what a pre-mapped environment can provide.
Improving a system by 10 times doesn't need 100 times the work, and having a scalable system is crucial for success. A strong leader like Elon Musk is necessary to pursue ambitious goals and inspire people to work towards them.
The speaker shared his experience on building a competent machine learning team, which involves studying, looking at sample data, driving, and growing a deep learning team with a massive compute cluster and a good data annotation organization.
The host admits to not being a fan of movies before 1995 but is preparing for a change of pace and wanting to do more technical work.
The development of a humanoid robot is a daunting task that will take a significant amount of time and effort. Despite Tesla's interest in the project, it may take a while before they can produce robots with the smoothness of movement of Boston Dynamics' robots.
The AI community needs to move beyond the use of ImageNet and aim to develop new benchmarks for research, according to a recent podcast. In addition, researchers should avoid getting trapped in negativity towards the success of others, as this can harm both individual and community productivity.
As neural networks continue to develop and become more similar to humans, the use of simulation for training set creation and learning new tasks will become increasingly valuable. Although there may still be a domain gap, neural nets are becoming more efficient at learning from structured data and can understand the high level structures in simulations.
The debate around whether learning occurs in infants revolves around brain maturation and the need for a knowledge base to learn facts about the world. Some researchers believe that the reason why we can't remember our first few years of life is because it's a painful process.
The speaker discusses the importance of uninterrupted, focused time to effectively approach and solve problems, building momentum to maximize productivity.
The speaker discusses their productivity habits and plant-based diet, explaining their focus sessions and intermittent fasting. They mention working for only a few hours a day and being on a plant-based or plant-forward diet.
The speaker shares his personal experience on creating a routine to find a balance between work and life.
A new tool called Copilot generates a compiler, a linter, and a type checker to improve code efficiency. It can be customized to match personal preferences, and used through a remote folder via SSH on VS code.
The possibility of software programming evolving to software 2.0 and a generation of systems programming similar to that of Copilot is discussed, raising the question of the role of traditional software 1.0. The challenge of UI/UX in this changing field is also acknowledged.
When it comes to code in the computer science space, the actual code that developers write is the source of truth that matters most, not just scientific publications or theory. Citing and building on each other's work, similar to blockchains, helps create security and build knowledge.
The guest believes in the 10,000 hours concept to become an expert and suggests forming a daily habit to maximize the chances of reaching that goal.
This episode explores the evolution of GANs from generating tiny digits to stable diffusion, and discusses the efficiency of flash attention kernel in running the attention operation inside the transformer.
The speaker discusses the limitations of text-based AI systems and the need for embodiment and interaction with the world to build a full understanding.
This episode discusses the potential for AI to become self-aware, and the ethical considerations that arise from this. The guest predicts that with this development, there will be debates around turning off a conscious AI and what constitutes as "life".
The human imperfections are what form connections with each other, and similarly, flaws in AI can lead to more power-efficient and deployable models according to AI expert Lex Fridman.
A discussion about the dangers of technology and the responsibility that comes with it, as well as the potential catastrophic outcomes that come with just small perturbations to the system.
In this podcast, the speaker talks about his happy place, which includes lush nature and high-tech solutions. He also discusses how the future of human experience will likely see a spread of human experiences and a disappearing into virtual realities.
This podcast discusses how genetics and ideas have shaped humans and their evolution over time, with a recommendation for the book "Life Ascending" by Nic Lane.
The key to finding your passion is to dedicate yourself to something and consistently build upon it. By focusing on the amount of work you put into a task, and low pass filtering for consistency, you can determine what truly interests you.
The deployment and user interface of an independent transcription tool is much easier compared to large integrated systems like YouTube or Zoom. However, a deeper meaning of life is to question the quality and accuracy of such tools compared to personal expectations.
The speaker believes that people often view death as inevitable, but he thinks that people who wish to do so might be able to avoid it, which is a great idea.