#102 - Prof. MICHAEL LEVIN, Prof. IRINA RISH - Emergence, Intelligence, Transhumanism
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YT: https://youtu.be/Vbi288CKgis
Michael Levin is a Distinguished Professor in the Biology department at Tufts University, and the holder of the Vannevar Bush endowed Chair. He is the Director of the Allen Discovery Center at Tufts and the Tufts Center for Regenerative and Developmental Biology. His research focuses on understanding the biophysical mechanisms of pattern regulation and harnessing endogenous bioelectric dynamics for rational control of growth and form.
The capacity to generate a complex, behaving organism from the single cell of a fertilized egg is one of the most amazing aspects of biology. Levin' lab integrates approaches from developmental biology, computer science, and cognitive science to investigate the emergence of form and function. Using biophysical and computational modeling approaches, they seek to understand the collective intelligence of cells, as they navigate physiological, transcriptional, morphognetic, and behavioral spaces. They develop conceptual frameworks for basal cognition and diverse intelligence, including synthetic organisms and AI.
Also joining us this evening is Irina Rish. Irina is a Full Professor at the Université de Montréal's Computer Science and Operations Research department, a core member of Mila - Quebec AI Institute, as well as the holder of the Canada CIFAR AI Chair and the Canadian Excellence Research Chair in Autonomous AI. She has a PhD in AI from UC Irvine. Her research focuses on machine learning, neural data analysis, neuroscience-inspired AI, continual lifelong learning, optimization algorithms, sparse modelling, probabilistic inference, dialog generation, biologically plausible reinforcement learning, and dynamical systems approaches to brain imaging analysis.
Interviewer: Dr. Tim Scarfe
TOC:
[00:00:00] Introduction
[00:02:09] Emergence
[00:13:16] Scaling Laws
[00:23:12] Intelligence
[00:44:36] Transhumanism
Prof. Michael Levin
https://en.wikipedia.org/wiki/Michael_Levin_(biologist)
https://www.drmichaellevin.org/
https://twitter.com/drmichaellevin
Prof. Irina Rish
https://twitter.com/irinarish
https://irina-rish.com/
#95 - Prof. IRINA RISH - AGI, Complex Systems, Transhumanism
Canadian Excellence Research Chair in Autonomous AI. Irina holds an MSc and PhD in AI from the University of California, Irvine as well as an MSc in Applied Mathematics from the Moscow Gubkin Institute. Her research focuses on machine learning, neural data analysis, and neuroscience-inspired AI. In particular, she is exploring continual lifelong learning, optimization algorithms for deep neural networks, sparse modelling and probabilistic inference, dialog generation, biologically plausible reinforcement learning, and dynamical systems approaches to brain imaging analysis. Prof. Rish holds 64 patents, has published over 80 research papers, several book chapters, three edited books, and a monograph on Sparse Modelling. She has served as a Senior Area Chair for NeurIPS and ICML. Irina's research is focussed on taking us closer to the holy grail of Artificial General Intelligence. She continues to push the boundaries of machine learning, continually striving to make advancements in neuroscience-inspired AI.
In a conversation about artificial intelligence (AI), Irina and Tim discussed the idea of transhumanism and the potential for AI to improve human flourishing. Irina suggested that instead of looking at AI as something to be controlled and regulated, people should view it as a tool to augment human capabilities. She argued that attempting to create an AI that is smarter than humans is not the best approach, and that a hybrid of human and AI intelligence is much more beneficial. As an example, she mentioned how technology can be used as an extension of the human mind, to track mental states and improve self-understanding. Ultimately, Irina concluded that transhumanism is about having a symbiotic relationship with technology, which can have a positive effect on both parties.
Tim then discussed the contrasting types of intelligence and how this could lead to something interesting emerging from the combination. He brought up the Trolley Problem and how difficult moral quandaries could be programmed into an AI. Irina then referenced The Garden of Forking Paths, a story which explores the idea of how different paths in life can be taken and how decisions from the past can have an effect on the present.
To better understand AI and intelligence, Irina suggested looking at it from multiple perspectives and understanding the importance of complex systems science in programming and understanding dynamical systems. She discussed the work of Michael Levin, who is looking into reprogramming biological computers with chemical interventions, and Tim mentioned Alex Mordvinsev, who is looking into the self-healing and repair of these systems. Ultimately, Irina argued that the key to understanding AI and intelligence is to recognize the complexity of the systems and to create hybrid models of human and AI intelligence.
Find Irina;
https://mila.quebec/en/person/irina-rish/
https://twitter.com/irinarish
YT version: https://youtu.be/8-ilcF0R7mI
MLST Discord: https://discord.gg/aNPkGUQtc5
References;
The Garden of Forking Paths: Jorge Luis Borges [Jorge Luis Borges]
https://www.amazon.co.uk/Garden-Forking-Paths-Penguin-Modern/dp/0241339057
The Brain from Inside Out [György Buzsáki]
https://www.amazon.co.uk/Brain-Inside-Out-Gy%C3%B6rgy-Buzs%C3%A1ki/dp/0190905387
Growing Isotropic Neural Cellular Automata [Alexander Mordvintsev]
https://arxiv.org/abs/2205.01681
The Extended Mind [Andy Clark and David Chalmers]
https://www.jstor.org/stable/3328150
The Gentle Seduction [Marc Stiegler]
https://www.amazon.co.uk/Gentle-Seduction-Marc-Stiegler/dp/0671698877
#91 - HATTIE ZHOU - Teaching Algorithmic Reasoning via In-context Learning #NeurIPS
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Hattie Zhou, a PhD student at Université de Montréal and Mila, has set out to understand and explain the performance of modern neural networks, believing it a key factor in building better, more trusted models. Having previously worked as a data scientist at Uber, a private equity analyst at Radar Capital, and an economic consultant at Cornerstone Research, she has recently released a paper in collaboration with the Google Brain team, titled ‘Teaching Algorithmic Reasoning via In-context Learning’. In this work, Hattie identifies and examines four key stages for successfully teaching algorithmic reasoning to large language models (LLMs): formulating algorithms as skills, teaching multiple skills simultaneously, teaching how to combine skills, and teaching how to use skills as tools. Through the application of algorithmic prompting, Hattie has achieved remarkable results, with an order of magnitude error reduction on some tasks compared to the best available baselines. This breakthrough demonstrates algorithmic prompting’s viability as an approach for teaching algorithmic reasoning to LLMs, and may have implications for other tasks requiring similar reasoning capabilities.
TOC
[00:00:00] Hattie Zhou
[00:19:49] Markus Rabe [Google Brain]
Hattie's Twitter - https://twitter.com/oh_that_hat
Website - http://hattiezhou.com/
Teaching Algorithmic Reasoning via In-context Learning [Hattie Zhou, Azade Nova, Hugo Larochelle, Aaron Courville, Behnam Neyshabur, and Hanie Sedghi]
https://arxiv.org/pdf/2211.09066.pdf
Markus Rabe [Google Brain]:
https://twitter.com/markusnrabe
https://research.google/people/106335/
https://www.linkedin.com/in/markusnrabe
Autoformalization with Large Language Models [Albert Jiang Charles Edgar Staats Christian Szegedy Markus Rabe Mateja Jamnik Wenda Li Yuhuai Tony Wu]
https://research.google/pubs/pub51691/
Discord: https://discord.gg/aNPkGUQtc5
YT: https://youtu.be/80i6D2TJdQ4
Episode 35 - Irina Rish
COVID-19 has swept across the world was startling speed, but with equally startling speed, the machine learning community has responded. This week I speak with Irina Rish, a professor at the University of Montreal and a Mila academic member, who is helping head a task force to understand the virus. She talked about where the efforts currently stand and where they expect to go in the weeks and months ahead.
Let me know when it's live.
