Risto Miikkulainen is a computer scientist at UT Austin. Please support this podcast by checking out our sponsors: - The Jordan Harbinger Show: https://jordanharbinger.com/lex/ - Grammarly: https://grammarly.com/lex to get 20% off premium - Belcampo: https://belcampo.com/lex and use code LEX to get 20% off first order - Indeed: https://indeed.com/lex to get $75 credit EPISODE LINKS: Risto's Website: https://www.cs.utexas.edu/users/risto/ 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 (06:51) - If we re-ran Earth over 1 million times (10:08) - Would aliens detect humans? (12:46) - Evolution of intelligent life (16:31) - Fear of death (22:47) - Hyenas (26:12) - Language (29:43) - The magic of programming (35:43) - Neuralink (43:15) - Surprising discoveries by AI (46:49) - How evolutionary computation works (58:12) - Learning to walk (1:01:25) - Robots and a theory of mind (1:10:29) - Neuroevolution (1:20:47) - Tesla Autopilot (1:24:11) - Language and vision (1:29:53) - Aliens communicating with humans (1:35:29) - Would AI learn to lie to humans? (1:42:03) - Artificial life (1:46:56) - Cellular automata (1:52:32) - Advice for young people (1:57:09) - Meaning of life
The speaker is inspired by Jordan to have challenging conversations and to accept responsibility for mistakes. They also express a fascination with nature-inspired algorithms. Noises of ad for meat can be heard in the end.
The complexity of human construction and cities would stand out to an external observer in a simulated world, and although it may take some time, there will be regularities and it is possible to detect interesting developments.
The podcast discusses the possibilities of exploring the question of "What is Intelligence?" through computational experiments, particularly in the context of the evolution of multicellular organisms. Through such experiments, it is possible to gain valuable insights into the nature of intelligence and its role in the evolution of life.
This episode explores the idea of building emotional agents that can mimic human consciousness and emotion, while also discussing the fear of mortality and how it can affect our thought processes.
The speaker compares the joy of creating a robot to finding God and being a father, and notes how it is difficult to think of humans as just algorithms.
The brain can adapt to take advantage of prosthetic devices through brain-computer interfaces, much like it has adapted to use electricity. This gives hope for the effectiveness of such devices.
This podcast episode discusses anecdotes from the evolutionary computation and artificial life research communities, including surprising solutions and unique perspectives on game theory.
This podcast discusses the basic mechanisms of evolutionary computation algorithms, which involve creating variations and then selecting the best ones through the use of an evolutionary approach. The focus is on identifying major transitions in biology and trying to apply those processes to computational algorithms.
Michael Littman discusses the potential of evolutionary computation and the need for incorporating dating mechanisms like marriage into the process to encourage weak selection, more crossover, larger populations and a new type of creativity.
The use of population-based methods in evolutionary computation can be more effective than deep learning in certain contexts when trying to explore and discover new solutions. However, deep learning is still valuable in individual learning during their lifetime.
The speaker discusses the importance of simple theory of mind in building trust and cooperation between humans and robots. Despite the complex nature of human-robot communication, the theory of mind may prove helpful in improving interactions.
The combination of neural networks and evolutionary computation can produce a powerful mechanism for constructing brains and behaviors, allowing the evolution of parameters and weight values in neural networks for specific purposes.
The goal of building general AI systems is not just to perform better in one or multiple tasks, but also to prepare for future challenges. Building a multitask network, with a core trained on multiple tasks and different heads for each, can lead to better performance overall.
The AI community has started to analyze the idea of language models utilizing images and vision. The visualization is a fundamental element for cognition and consciousness alike.
The possibility of humans communicating with aliens and the challenge of finding a common language and methodology is discussed; the idea of karma and how kindness can lead to success in society is explored, with the suggestion of running a simulation to evolve communication between agents.
Artificial life can be seen as a simulation of life that helps us understand how life evolved on earth. It can aid in developing better societies and even shed light on molecular designs and the start of life.
Discussing the limitations of the human mind, the podcast explores the potential of creating AI systems that could understand what it means to live in a multi-dimensional world outside of the four dimensions. It also discusses the power of simplicity and complexity, and how cellular automata could be efficient for studying life systems.
The podcast delves into how diversity exploration is fundamental to having a successful career and how it is necessary to concentrate and invest effort where it matters for evolutionary computation to be effective.
The speaker highlights the need to leave an impact even after the agent is gone and emphasizes that younger people are more inclined towards this thought process. The field of artificial intelligence is said to be the future of technology.