In this book, Nick Bostrom delves into the implications of creating superintelligence, which could surpass human intelligence in all domains. He discusses the potential dangers, such as the loss of human control over such powerful entities, and presents various strategies to ensure that superintelligences align with human values. The book examines the 'AI control problem' and the need to endow future machine intelligence with positive values to prevent existential risks[3][5][4].
In this book, Stuart Russell explores the concept of intelligence in humans and machines, outlining the near-term benefits and potential risks of AI. He discusses the misuse of AI, from lethal autonomous weapons to viral sabotage, and proposes a novel solution by rebuilding AI on a new foundation where machines are inherently uncertain about human preferences. This approach aims to create machines that are humble, altruistic, and committed to pursuing human objectives, ensuring they remain provably deferential and beneficial to humans.
This book, written by Douglas Hofstadter and Emmanuel Sander, delves into the cognitive mechanisms that underpin human thought. It posits that analogy-making is the fundamental process by which our brains make sense of the world, constantly seeking strong analogical links to past experiences. The authors use a variety of colorful situations involving language, thought, and memory to illustrate how analogy is essential for thinking, from everyday experiences to the highest achievements of the human mind.
This book presents a collection of revised articles exploring the fundamental mechanisms of thought through computer models. It delves into the concepts of analogy and fluidity, crucial for understanding human problem-solving and creating intelligent computer programs. The book includes discussions on various projects, such as the Copycat program, which models mental fluidity and analogy-making.
Melanie Mitchell is a professor of computer science at Portland State University and an external professor at Santa Fe Institute. She has worked on and written about artificial intelligence from fascinating perspectives including adaptive complex systems, genetic algorithms, and the Copycat cognitive architecture which places the process of analogy making at the core of human cognition. From her doctoral work with her advisors Douglas Hofstadter and John Holland to today, she has contributed a lot of important ideas to the field of AI, including her recent book, simply called Artificial Intelligence: A Guide for Thinking Humans.
This conversation is part of the Artificial Intelligence podcast. If you would like to get more information about this podcast go to https://lexfridman.com/ai or connect with @lexfridman on Twitter, LinkedIn, Facebook, Medium, or YouTube where you can watch the video versions of these conversations. If you enjoy the podcast, please rate it 5 stars on Apple Podcasts, follow on Spotify, or support it on Patreon.
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Episode Links:
AI: A Guide for Thinking Humans (book)
Here’s the outline of the episode. On some podcast players you should be able to click the timestamp to jump to that time.
00:00 – Introduction
02:33 – The term “artificial intelligence”
06:30 – Line between weak and strong AI
12:46 – Why have people dreamed of creating AI?
15:24 – Complex systems and intelligence
18:38 – Why are we bad at predicting the future with regard to AI?
22:05 – Are fundamental breakthroughs in AI needed?
25:13 – Different AI communities
31:28 – Copycat cognitive architecture
36:51 – Concepts and analogies
55:33 – Deep learning and the formation of concepts
1:09:07 – Autonomous vehicles
1:20:21 – Embodied AI and emotion
1:25:01 – Fear of superintelligent AI
1:36:14 – Good test for intelligence
1:38:09 – What is complexity?
1:43:09 – Santa Fe Institute
1:47:34 – Douglas Hofstadter
1:49:42 – Proudest moment
Lex Fridman Podcast 
