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The Importance of Objective Optimization in AI Research
Kenneth Stanley is a professor of computer science at the University of California, San Francisco. His research focuses on neuroevolution and AI generating algorithms. The two men had an interesting back-and-forth about how to use artificial intelligence in society.
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
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