Artificial intelligence researcher and author Kenneth Stanley has argued that “as soon as you create an objective, you ruin your ability to reach it.” So what should you consider when thinking about your objectives, and what will set you up for success? On this episode Stanley discusses how to set the right objectives for your life, why we’re too tied to accomplishments, what role accountability plays in our education system, the value of peer review,  why transformative innovations are always counter intuitive, and so much more. Stanley is the co–author of Why Greatness Cannot Be Planned: The Myth of the Objective,  as well as the former Head of Core AI Research at Uber AI and the Open-Endedness Team Leader at OpenAI. He has also served as the Charles Millican Professor in Computer Science at University of Central Florida. -- Want even more? Members get early access, hand-edited transcripts, member-only episodes, and so much more. Learn more here: https://fs.blog/membership/ Every Sunday our Brain Food newsletter shares timeless insights and ideas that you can use at work and home. Add it to your inbox: https://fs.blog/newsletter/ Follow Shane on Twitter at: https://twitter.com/ShaneAParrish

My guest today is Ken Stanley. Ken is a Professor in Computer Science and a pioneer in the field of neuroevolution. He is also the co-author of a book called, Why Greatness Cannot Be Planned, which details a provocative idea that setting big, audacious goals can reduce the odds of achieving something great. We discuss that revelation in detail and how to apply it in our day-to-day lives. Please enjoy this great discussion with Ken Stanley.   For the full show notes, transcript, and links to mentioned content, check out the episode page here.   -----   This episode is brought to you by Tegus. Tegus streamlines the investment research process so you can get up to speed and find answers to critical questions on companies faster and more efficiently. The Tegus platform surfaces the hard-to-get qualitative insights, gives instant access to critical public financial data through BamSEC, and helps you set up customized expert calls. It’s all done on a single, modern SaaS platform that offers 360-degree insight into any public or private company. As a listener, you can take Tegus for a free test drive by visiting tegus.co/patrick. And until 2023 every Tegus license comes with complimentary access to BamSec by Tegus.   -----   Today's episode is brought to you by Brex, the integrated financial platform trusted by the world's most innovative entrepreneurs and fastest-growing companies. With Brex, you can move money fast for instant impact with high-limit corporate cards, payments, venture debt, and spend management software all in one place. Ready to accelerate your business? Learn more at brex.com/best.   -----   Invest Like the Best is a property of Colossus, LLC. For more episodes of Invest Like the Best, visit joincolossus.com/episodes.    Past guests include Tobi Lutke, Kevin Systrom, Mike Krieger, John Collison, Kat Cole, Marc Andreessen, Matthew Ball, Bill Gurley, Anu Hariharan, Ben Thompson, and many more.   Stay up to date on all our podcasts by signing up to Colossus Weekly, our quick dive every Sunday highlighting the top business and investing concepts from our podcasts and the best of what we read that week. Sign up here.   Follow us on Twitter: @patrick_oshag | @JoinColossus   Show Notes [00:02:36] - [First question] - The best way to change the world is to stop trying to change it [00:06:26] - The kinds of goals his work addresses and the ones it doesn’t [00:08:46] - Almost no prerequisite to any major invention was invented with that major invention in mind [00:14:04] - Picbreeder [00:17:21] - How looking for specific results often makes arriving at them a longer process [00:24:00] - The importance of the individual in a web of invention and disruption [00:28:30] - How generations progressed in Picbreeder when consensus mechanisms were inserted into the process  [00:31:24] - Examples of stepping stones that were invented that became something even greater [00:36:02] - What his research means for how we should conduct ourselves writ large [00:44:17] - Thoughts on necessity being the mother of all invention [00:50:08] - The ways that society is arranged is psychologically toxic [00:55:14] - The role that constraints play in creative output and outcomes in general; Brett Victor - Inventing on Principle [01:01:10] - What the constraints are that he sets for himself in AI development [01:04:44] - To know what’s new you need to know what’s not new [01:06:47] - The kindest thing anyone has ever done for him [01:08:28] - How he would allocate resources to create more innovation in the world

Discover how Professor Kenneth Stanley created a unique social network focused on interests rather than popularity, inspired by his book 'Why greatness cannot be planned'. Explore the philosophy and technology behind the Serendipity network, challenging traditional optimization systems and promoting creativity through dynamic interest discovery. Dive into the paradox of setting objectives in AI research and the complexity of human intelligence contrasted with AI models. Learn about cultivating serendipity, fostering genuine interactions, and balancing user engagement in social networking design.

Professor Kenneth Stanley is currently a research science manager at OpenAI in San Fransisco. We've Been dreaming about getting Kenneth on the show since the very begininning of Machine Learning Street Talk. Some of you might recall that our first ever show was on the enhanced POET paper, of course Kenneth had his hands all over it. He's been cited over 16000 times, his most popular paper with over 3K citations was the NEAT algorithm. His interests are neuroevolution, open-endedness, NNs, artificial life, and AI. He invented the concept of novelty search with no clearly defined objective. His key idea is that there is a tyranny of objectives prevailing in every aspect of our lives, society and indeed our algorithms. Crucially, these objectives produce convergent behaviour and thinking and distract us from discovering stepping stones which will lead to greatness. He thinks that this monotonic objective obsession, this idea that we need to continue to improve benchmarks every year is dangerous. He wrote about this in detail in his recent book "greatness can not be planned" which will be the main topic of discussion in the show. We also cover his ideas on open endedness in machine learning.  00:00:00 Intro to Kenneth  00:01:16 Show structure disclaimer  00:04:16 Passionate discussion  00:06:26 WHy greatness cant be planned and the tyranny of objectives  00:14:40 Chinese Finger Trap   00:16:28 Perverse Incentives and feedback loops  00:18:17 Deception  00:23:29 Maze example  00:24:44 How can we define curiosity or interestingness  00:26:59 Open endedness  00:33:01 ICML 2019 and Yannic, POET, first MSLST  00:36:17 evolutionary algorithms++  00:43:18 POET, the first MLST   00:45:39 A lesson to GOFAI people  00:48:46 Machine Learning -- the great stagnation  00:54:34 Actual scientific successes are usually luck, and against the odds -- Biontech  00:56:21 Picbreeder and NEAT  01:10:47 How Tim applies these ideas to his life and why he runs MLST  01:14:58 Keith Skit about UCF  01:15:13 Main show kick off  01:18:02 Why does Kenneth value serindipitous exploration so much  01:24:10 Scientific support for Keneths ideas in normal life  01:27:12 We should drop objectives to achieve them. An oxymoron?  01:33:13 Isnt this just resource allocation between exploration and exploitation?  01:39:06 Are objectives merely a matter of degree?  01:42:38 How do we allocate funds for treasure hunting in society  01:47:34 A keen nose for what is interesting, and voting can be dangerous  01:53:00 Committees are the antithesis of innovation  01:56:21 Does Kenneth apply these ideas to his real life?  01:59:48 Divergence vs interestingness vs novelty vs complexity  02:08:13 Picbreeder  02:12:39 Isnt everything novel in some sense?  02:16:35 Imagine if there was no selection pressure?  02:18:31 Is innovation == environment exploitation?  02:20:37 Is it possible to take shortcuts if you already knew what the innovations were?  02:21:11 Go Explore -- does the algorithm encode the stepping stones?  02:24:41 What does it mean for things to be interestingly different?  02:26:11 behavioral characterization / diversity measure to your broad interests  02:30:54 Shaping objectives  02:32:49 Why do all ambitious objectives have deception? Picbreeder analogy  02:35:59 Exploration vs Exploitation, Science vs Engineering  02:43:18 Schools of thought in ML and could search lead to AGI  02:45:49 Official ending 

Support us! https://www.patreon.com/mlst   MLST Discord: https://discord.gg/aNPkGUQtc5 We are honoured to welcome Dr. Joel Lehman, an eminent machine learning research scientist, whose work in AI safety, reinforcement learning, creative open-ended search algorithms, and indeed the philosophy of open-endedness and abandoning objectives has paved the way for innovative ideas that challenge our preconceptions and inspire new visions for the future. Dr. Lehman's thought-provoking book, "Why Greatness Cannot Be Planned" penned with with our MLST favourite Professor Kenneth Stanley has left an indelible mark on the field and profoundly impacted the way we view innovation and the serendipitous nature of discovery. Those of you who haven't watched our special edition show on that, should do so at your earliest convenience! Building upon this foundation, Dr. Lehman has ventured into the domain of AI systems that embody principles of love, care, responsibility, respect, and knowledge, drawing from the works of Maslow, Erich Fromm, and positive psychology. YT version: https://youtu.be/23-TXgJEv-Q http://joellehman.com/ https://twitter.com/joelbot3000 Interviewer: Dr. Tim Scarfe TOC: Intro [00:00:00] Model [00:04:26] Intro and Paper Intro [00:08:52] Subjectivity [00:16:07] Reflections on Greatness Book [00:19:30] Representing Subjectivity [00:29:24] Nagal's Bat [00:31:49] Abstraction [00:38:58] Love as Action Rather Than Feeling [00:42:58] Reontologisation [00:57:38] Self Help [01:04:15] Meditation [01:09:02] The Human Reward Function / Effective... [01:16:52] Machine Hate [01:28:32] Societal Harms [01:31:41] Lenses We Use Obscuring Reality [01:56:36] Meta Optimisation and Evolution [02:03:14] Conclusion [02:07:06] References: What Is It Like to Be a Bat? (Thomas Nagel) https://warwick.ac.uk/fac/cross_fac/iatl/study/ugmodules/humananimalstudies/lectures/32/nagel_bat.pdf Why Greatness Cannot Be Planned: The Myth of the Objective (Kenneth O. Stanley and Joel Lehman) https://link.springer.com/book/10.1007/978-3-319-15524-1  Machine Love (Joel Lehman) https://arxiv.org/abs/2302.09248  How effective altruists ignored risk (Carla Cremer) https://www.vox.com/future-perfect/23569519/effective-altrusim-sam-bankman-fried-will-macaskill-ea-risk-decentralization-philanthropy Philosophy tube - The Rich Have Their Own Ethics: Effective Altruism https://www.youtube.com/watch?v=Lm0vHQYKI-Y Abandoning Objectives: Evolution through the Search for Novelty Alone (Joel Lehman and Kenneth O. Stanley) https://www.cs.swarthmore.edu/~meeden/DevelopmentalRobotics/lehman_ecj11.pdf