Dr. Christian Szegedy from Google Research is a deep learning heavyweight. He invented adversarial examples, one of the first object detection algorithms, the inceptionnet architecture, and co-invented batchnorm. He thinks that if you bet on computers and software in 1990 you would have been as right as if you bet on AI now. But he thinks that we have been programming computers the same way since the 1950s and there has been a huge stagnation ever since. Mathematics is the process of taking a fuzzy thought and formalising it. But could we automate that? Could we create a system which will act like a super human mathematician but you can talk to it in natural language? This is what Christian calls autoformalisation. Christian thinks that automating many of the things we do in mathematics is the first step towards software synthesis and building human-level AGI. Mathematics ability is the litmus test for general reasoning ability. Christian has a fascinating take on transformers too. 
With Yannic Lightspeed Kilcher and Dr. Mathew Salvaris
Whimsical Canvas with Tim's Notes:
https://whimsical.com/mar-26th-christian-szegedy-CpgGhnEYDBrDMFoATU6XYC
YouTube version (with detailed table of contents) https://youtu.be/ehNGGYFO6ms

#50 Christian Szegedy - Formal Reasoning, Program Synthesis

Welcome! We engage in fascinating discussions with pre-eminent figures in the AI field. Our flagship show covers current affairs in AI, cognitive science, neuroscience and philosophy of mind with in-depth analysis. Our approach is unrivalled in terms of scope and rigour – we believe in intellectual diversity in AI, and we touch on all of the main ideas in the field with the hype surgically removed. MLST is run by Tim Scarfe, Ph.D (https://www.linkedin.com/in/ecsquizor/) and features regular appearances from MIT Doctor of Philosophy Keith Duggar (https://www.linkedin.com/in/dr-keith-duggar/).

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Maria Santacaterina, with her background in the humanities, brings a critical perspective on the current state and future implications of AI technology, its impact on society, and the nature of human intelligence and creativity. She emphasizes that despite technological advancements, AI lacks fundamental human traits such as consciousness, empathy, intuition, and the ability to engage in genuine creative processes. Maria argues that AI, at its core, processes data but does not have the capability to understand or generate new, intrinsic meaning or ideas as humans do.


Throughout the conversation, Maria highlights her concern about the overreliance on AI in critical sectors such as healthcare, the justice system, and business. She stresses that while AI can serve as a tool, it should not replace human judgment and decision-making. Maria points out that AI systems often operate on past data, which may lead to outdated or incorrect decisions if not carefully managed.


The discussion also touches upon the concept of "adaptive resilience", which Maria describes in her book. She explains adaptive resilience as the capacity for individuals and enterprises to evolve and thrive amidst challenges by leveraging technology responsibly, without undermining human values and capabilities.


A significant portion of the conversation focussed on ethical considerations surrounding AI. Tim and Maria agree that there's a pressing need for strong governance and ethical frameworks to guide AI development and deployment. They discuss how AI, without proper ethical considerations, risks exacerbating issues like privacy invasion, misinformation, and unintended discrimination.


Maria is skeptical about claims of achieving Artificial General Intelligence (AGI) or a technological singularity where machines surpass human intelligence in all aspects. She argues that such scenarios neglect the complex, dynamic nature of human intelligence and consciousness, which cannot be fully replicated or replaced by machines.


Tim and Maria discuss the importance of keeping human agency and creativity at the forefront of technology development. Maria asserts that efforts to automate or standardize complex human actions and decisions are misguided and could lead to dehumanizing outcomes. They both advocate for using AI as an aid to enhance human capabilities rather than a substitute.


In closing, Maria encourages a balanced approach to AI adoption, urging stakeholders to prioritize human well-being, ethical standards, and societal benefit above mere technological advancement. The conversation ends with Maria pointing people to her book for more in-depth analysis and thoughts on the future interaction between humans and technology.


Buy Maria's book here: https://amzn.to/4avF6kq
https://www.linkedin.com/in/mariasantacaterina


TOC
00:00:00 - Intro to Book
00:03:23 - What Life Is
00:10:10 - Agency
00:18:04 - Tech and Society
00:21:51 - System 1 and 2
00:22:59 - We Are Being Pigeonholed
00:30:22 - Agency vs Autonomy
00:36:37 - Explanations
00:40:24 - AI Reductionism
00:49:50 - How Are Humans Intelligent
01:00:22 - Semantics
01:01:53 - Emotive AI and Pavlovian Dogs
01:04:05 - Technology, Social Media and Organisation
01:18:34 - Systems Are Not That Automated
01:19:33 - Hiring
01:22:34 - Subjectivity in Orgs
01:32:28 - The AGI Delusion
01:45:37 - GPT-laziness Syndrome
01:54:58 - Diversity Preservation
01:58:24 - Ethics
02:11:43 - Moral Realism
02:16:17 - Utopia
02:18:02 - Reciprocity
02:20:52 - Tyranny of Categorisation

CAN MACHINES REPLACE US? (AI vs Humanity) - Maria Santacaterina

Thomas Parr and his collaborators wrote a book titled "Active Inference: The Free Energy Principle in Mind, Brain and Behavior" which introduces Active Inference from both a high-level conceptual perspective and a low-level mechanistic, mathematical perspective.


Active inference, developed by the legendary neuroscientist Prof. Karl Friston - is a unifying mathematical framework which frames living systems as agents which minimize surprise and free energy in order to resist entropy and persist over time. It unifies various perspectives from physics, biology, statistics, and psychology - and allows us to explore deep questions about agency, biology, causality, modelling, and consciousness.


Buy Active Inference: The Free Energy Principle in Mind, Brain, and Behavior 
https://amzn.to/4dj0iMj


YT version: https://youtu.be/lbb-Si5wa_o


Please support us on Patreon to get access to the private Discord server, bi-weekly calls, early access and ad-free listening. 
https://patreon.com/mlst


Chapters should be embedded in the mp3, let me me know if issues

Dr. Thomas Parr - Active Inference Book

Connor is the CEO of Conjecture and one of the most famous names in the AI alignment movement. This is the "behind the scenes footage" and bonus Patreon interviews from the day of the Beff Jezos debate, including an interview with Daniel Clothiaux. It's a great insight into Connor's philosophy. At the end there is an unreleased additional interview with Beff.


Support MLST:
Please support us on Patreon. We are entirely funded from Patreon donations right now. Patreon supports get private discord access, biweekly calls, very early-access + exclusive content and lots more. 
https://patreon.com/mlst
Donate: https://www.paypal.com/donate/?hosted_button_id=K2TYRVPBGXVNA
If you would like to sponsor us, so we can tell your story - reach out on mlstreettalk at gmail


Topics:
Externalized cognition and the role of society and culture in human intelligence
The potential for AI systems to develop agency and autonomy
The future of AGI as a complex mixture of various components
The concept of agency and its relationship to power
The importance of coherence in AI systems
The balance between coherence and variance in exploring potential upsides
The role of dynamic, competent, and incorruptible institutions in handling risks and developing technology
Concerns about AI widening the gap between the haves and have-nots
The concept of equal access to opportunity and maintaining dynamism in the system
Leahy's perspective on life as a process that "rides entropy"
The importance of distinguishing between epistemological, decision-theoretic, and aesthetic aspects of morality (inc ref to Hume's Guillotine)
The concept of continuous agency and the idea that the first AGI will be a messy admixture of various components
The potential for AI systems to become more physically embedded in the future
The challenges of aligning AI systems and the societal impacts of AI technologies like ChatGPT and Bing
The importance of humility in the face of complexity when considering the future of AI and its societal implications


Disclaimer: this video is not an endorsement of e/acc or AGI agential existential risk from us - the hosts of MLST consider both of these views to be quite extreme. We seek diverse views on the channel.


00:00:00 Intro
00:00:56 Connor's Philosophy
00:03:53 Office Skit
00:05:08 Connor on e/acc and Beff
00:07:28 Intro to Daniel's Philosophy
00:08:35 Connor on Entropy, Life, and Morality
00:19:10 Connor on London
00:20:21 Connor Office Interview
00:20:46 Friston Patreon Preview
00:21:48 Why Are We So Dumb?
00:23:52 The Voice of the People, the Voice of God / Populism
00:26:35 Mimetics
00:30:03 Governance
00:33:19 Agency
00:40:25 Daniel Interview - Externalised Cognition, Bing GPT, AGI
00:56:29 Beff + Connor Bonus Patreons Interview

Connor Leahy - e/acc, AGI and the future.

Professor Chris Bishop is a Technical Fellow and Director at Microsoft Research AI4Science, in Cambridge. He is also Honorary Professor of Computer Science at the University of Edinburgh, and a Fellow of Darwin College, Cambridge. In 2004, he was elected Fellow of the Royal Academy of Engineering, in 2007 he was elected Fellow of the Royal Society of Edinburgh, and in 2017 he was elected Fellow of the Royal Society. Chris was a founding member of the UK AI Council, and in 2019 he was appointed to the Prime Minister’s Council for Science and Technology.


At Microsoft Research, Chris oversees a global portfolio of industrial research and development, with a strong focus on machine learning and the natural sciences.
Chris obtained a BA in Physics from Oxford, and a PhD in Theoretical Physics from the University of Edinburgh, with a thesis on quantum field theory. 


Chris's contributions to the field of machine learning have been truly remarkable. He has authored (what is arguably) the original textbook in the field - 'Pattern Recognition and Machine Learning' (PRML) which has served as an essential reference for countless students and researchers around the world, and that was his second textbook after his highly acclaimed first textbook Neural Networks for Pattern Recognition. 


Recently, Chris has co-authored a new book with his son, Hugh, titled 'Deep Learning: Foundations and Concepts.' This book aims to provide a comprehensive understanding of the key ideas and techniques underpinning the rapidly evolving field of deep learning. It covers both the foundational concepts and the latest advances, making it an invaluable resource for newcomers and experienced practitioners alike.


Buy Chris' textbook here:
https://amzn.to/3vvLcCh


More about Prof. Chris Bishop:
https://en.wikipedia.org/wiki/Christopher_Bishop
https://www.microsoft.com/en-us/research/people/cmbishop/


Support MLST:
Please support us on Patreon. We are entirely funded from Patreon donations right now. Patreon supports get private discord access, biweekly calls, early-access + exclusive content and lots more. 
https://patreon.com/mlst
Donate: https://www.paypal.com/donate/?hosted_button_id=K2TYRVPBGXVNA
If you would like to sponsor us, so we can tell your story - reach out on mlstreettalk at gmail


TOC:
00:00:00 - Intro to Chris
00:06:54 - Changing Landscape of AI
00:08:16 - Symbolism
00:09:32 - PRML
00:11:02 - Bayesian Approach
00:14:49 - Are NNs One Model or Many, Special vs General
00:20:04 - Can Language Models Be Creative
00:22:35 - Sparks of AGI
00:25:52 - Creativity Gap in LLMs
00:35:40 - New Deep Learning Book
00:39:01 - Favourite Chapters
00:44:11 - Probability Theory
00:45:42 - AI4Science
00:48:31 - Inductive Priors
00:58:52 - Drug Discovery
01:05:19 - Foundational Bias Models
01:07:46 - How Fundamental Is Our Physics Knowledge?
01:12:05 - Transformers
01:12:59 - Why Does Deep Learning Work?
01:16:59 - Inscrutability of NNs
01:18:01 - Example of Simulator
01:21:09 - Control

Prof. Chris Bishop's NEW Deep Learning Textbook!

Dr. Philip Ball is a freelance science writer. He just wrote a book called "How Life Works", discussing the how the science of Biology has advanced in the last 20 years. We focus on the concept of Agency in particular. 


He trained as a chemist at the University of Oxford, and as a physicist at the University of Bristol. He worked previously at Nature for over 20 years, first as an editor for physical sciences and then as a consultant editor. His writings on science for the popular press have covered topical issues ranging from cosmology to the future of molecular biology.


YT: https://www.youtube.com/watch?v=n6nxUiqiz9I


Transcript link on YT description


Philip is the author of many popular books on science, including H2O: A Biography of Water, Bright Earth: The Invention of Colour, The Music Instinct and Curiosity: How Science Became Interested in Everything. His book Critical Mass won the 2005 Aventis Prize for Science Books, while Serving the Reich was shortlisted for the Royal Society Winton Science Book Prize in 2014.


This is one of Tim's personal favourite MLST shows, so we have designated it a special edition. Enjoy!


Buy Philip's book "How Life Works" here: https://amzn.to/3vSmNqp


Support MLST:
Please support us on Patreon. We are entirely funded from Patreon donations right now. Patreon supports get private discord access, biweekly calls, early-access + exclusive content and lots more. 
https://patreon.com/mlst
Donate: https://www.paypal.com/donate/?hosted...
If you would like to sponsor us, so we can tell your story - reach out on mlstreettalk at gmail

#50 Christian Szegedy - Formal Reasoning, Program Synthesis

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