ep6 - Norbert Wiener and Cybernetics

10/17/22 • 63 min

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In this episode, we delve into the extraordinary life of Norbert Wiener, the founding father of cybernetics - the science “control and communication in the animal and the machine”.
Outline
00:00 - Intro
02:06 - The early years of Norbert
09:00 - Europe and WWI
15:50 - MIT days
19:30 - Norbert’s marriage
22:39 - Generalised harmonic analysis
28:18 - The interactions with Hopf and Paley
31:14 - Bush and the analog computer program
35:55 - WWII, Bigelow and prediction theory
40:41 - Rosenbleuth and teleological machines
47:56 - Mexico and Norbert’s biological investigations
51:25 - Cybernetics
1:00:16 - The human behind Norbert Wiener
1:01:53 - Outro
Episode links
Things named after Wiener: https://tinyurl.com/mt37xn93
Autobiography: https://tinyurl.com/2umws9nd
Biography: https://tinyurl.com/nhawc9az
Wiener filter: https://tinyurl.com/n9u5ukxe
Paley-Wiener theorem: https://tinyurl.com/mr3z3f89
Wiener-Kinchin theorem: https://tinyurl.com/3mxm54ac
Vannevar Bush: https://tinyurl.com/y6s7kz6t
Julian Bigelow: https://tinyurl.com/28m4a6as
Behavior, Purpose and Teleology: https://tinyurl.com/3ut2afjz
Arturo Rosenblueth: https://tinyurl.com/57wp67vh
Cybernetics: https://tinyurl.com/5e3tnn6e
Out of control: https://tinyurl.com/3rnhn3xh
A scientist rebels: https://tinyurl.com/5f2d3urc
Moral and technical consequences of automation: https://tinyurl.com/72tvzuxy

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Podcast info
Podcast website: https://www.incontrolpodcast.com/
Apple Podcasts: https://tinyurl.com/5n84j85j
Spotify: https://tinyurl.com/4rwztj3c
RSS: https://tinyurl.com/yc2fcv4y
Youtube: https://tinyurl.com/bdbvhsj6
Facebook: https://tinyurl.com/3z24yr43
Twitter: https://twitter.com/IncontrolP
Instagram: https://tinyurl.com/35cu4kr4
Acknowledgments and sponsors
This episode was supported by the National Centre of Competence in Research on «Dependable, ubiquitous automation» and the IFAC Activity fund. The podcast benefits from the help of an incredibly talented and passionate team. Special thanks to L. Seward, E. Cahard, F. Banis, F. Dörfler, J. Lygeros, ETH studio and mirrorlake . Music was composed by A New Element.

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Previous Episode

ep5 - Sean Meyn: Markov chains, networks, reinforcement learning, beekeeping and jazz

In this episode, our guest is Sean Meyn, Professor and Robert C. Pittman Eminent Scholar Chair in the Department of Electrical and Computer Engineering at the University of Florida. The episode features Sean’s adventures in the areas of Markov chains, networks and Reinforcement Learning (RL) as well as anecdotes and trivia about beekeeping and jazz.
Outline
00:00 - Intro
00:22 - Sean’s early steps
03:53 - Markov chains
08:45 - Networks
18:26 - Stochastic approximation
25:00 - Reinforcement Learning
38:57 - The intersection of Reinforcement Learning and Control
42:37 - Favourite theorem
44:05 - Beekeeping and jazz
48:47 - Outro
Episode links
Sean’s website: https://meyn.ece.ufl.edu/
Sean’s books: shorturl.at/CFGRY (and T. Sargent's review: shorturl.at/hlGNR)
G. Zames: shorturl.at/JPRWX (see also: shorturl.at/chiw5)
State space model: shorturl.at/hST07
The life and work of A.A. Markov: shorturl.at/qsv35
Fluid model: shorturl.at/HKN56
M/M/1 queue: shorturl.at/dQW36
Borkar-Meyn theorem: shorturl.at/eSTV4
NCCR Automation Symposia: shorturl.at/csv03 (see also shorturl.at/ekpZ3)
V. Konda’s PhD Thesis: shorturl.at/bdrv7

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Next Episode

undefined - ep7 - Jean-Jacques Slotine: Sliding, nonlinear and adaptive control, contraction theory, complex networks, optimization, and machine learning

ep7 - Jean-Jacques Slotine: Sliding, nonlinear and adaptive control, contraction theory, complex networks, optimization, and machine learning

In this episode, our guest is Jean-Jacques Slotine, Professor of Mechanical Engineering and Information Sciences as well as Brain and Cognitive Sciences, Director of the Nonlinear Systems Laboratory at the Massachusetts Institute of Technology, and Distinguished Faculty at Google AI. We explore and connect a wide range of ideas from nonlinear and adaptive control to robotics, neuroscience, complex networks, optimization and machine learning.
Outline
00:00 - Intro
00:50 - Jean-Jacques' early life
06:17 - Why control?
09:45 - Sliding control and adaptive nonlinear control
18:47 - Neural networks
23:15 - First ventures in neuroscience
28:27 - Contraction theory and applications
48:26 - Synchronization
51:10 - Complex networks
57:59 - Optimization and machine learning
1:08:17 - Advice to future students and outro
Episode links
NCCR Symposium: https://tinyurl.com/bdz84p4c
Sliding mode control: https://tinyurl.com/2s45ra4m
Applied nonlinear control: https://tinyurl.com/4wmbt4bw
On the Adaptive Control of Robot Manipulators: https://tinyurl.com/b7jcpkzw
Gaussian Networks for Direct Adaptive Control: https://tinyurl.com/22zb7pkx
The intermediate cerebellum may function as a wave-variable processor: https://tinyurl.com/2c34ytep
On contraction analysis for nonlinear systems: https://tinyurl.com/5cw4z9j8
Kalman conjecture: https://tinyurl.com/2pfjsbke
I. Prigogine: https://tinyurl.com/5ct8yssb
RNNs of RNNs: https://tinyurl.com/3mpt7fec
How Synchronization Protects from Noise: https://tinyurl.com/2p82erwp
Controllability of complex networks: https://tinyurl.com/24w7hdae
B. Anderson: https://tinyurl.com/e9pkyxdx
Online lectures on nonlinear control: https://tinyurl.com/525cnru4