17 episodes
Causal Bandits Podcast Alex Molak
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- Technology
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5.0 • 4 Ratings
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Causal Bandits Podcast with Alex Molak is here to help you learn about causality, causal AI and causal machine learning through the genius of others. The podcast focuses on causality from a number of different perspectives, finding common grounds between academia and industry, philosophy, theory and practice, and between different schools of thought, and traditions. Your host, Alex Molak is an entrepreneur, independent researcher and a best-selling author, who decided to travel the world to record conversations with the most interesting minds in causality. Enjoy and stay causal!Keywords: Causal AI, Causal Machine Learning, Causality, Causal Inference, Causal Discovery, Machine Learning, AI, Artificial Intelligence
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Why Hinton Was Wrong, Causal AI & Science | Thanos Vlontzos Ep 15 | CausalBanditsPodcast.com
Recorded on Jan 17, 2024 in London, UK.
Video version available here
What makes so many predictions about the future of AI wrong?
And what's possible with the current paradigm?
From medical imaging to song recommendations, the association-based paradigm of learning can be helpful, but is not sufficient to answer our most interesting questions.
Meet Athanasios (Thanos) Vlontzos who looks for inspirations everywhere around him to build causal machine learning and causal inference systems at Spotify's Advanced Causal Inference Lab.
In the episode we discuss:
- Why is causal discovery a better riddle than causal inference?
- Will radiologists be replaced by AI in 2024 or 2025?
- What are causal AI skeptics missing?
- Can causality emerge in Euclidean latent space?
Ready to dive in?
About The Guest
Athanasios (Thanos) Vlontzos, PhD is a Research Scientist at Advanced Causal Inference Lab at Spotify. Previousl;y, he worked at Apple, at SETI Institute with NASA stakeholders and published in some of the best scientific journals, including Nature Machine Learning. He's specialized in causal modeling, causal inferernce, causal discovery and medical imaging.
Connect with Athanasios:
- Athanasios on Twitter/X
- Athanasios on LinkedIn
- Athanasios's web page
About The Host
Aleksander (Alex) Molak is an independent machine learning researcher, educator, entrepreneur and a best-selling author in the area of causality.
Connect with Alex:
- Alex on the Internet
Links
The full list of links can be found here.
Support the Show.
Causal Bandits Podcast
Causal AI || Causal Machine Learning || Causal Inference & Discovery
Web: https://causalbanditspodcast.com
Connect on LinkedIn: https://www.linkedin.com/in/aleksandermolak/
Join Causal Python Weekly: https://causalpython.io
The Causal Book: https://amzn.to/3QhsRz4 -
Causal Inference & Financial Modeling with Alexander Denev Ep 14 | CausalBanditsPodcast.com
Video version available here
Are markets efficient, and if not, can causal models help us leverage the inefficiencies?
Do we really need to understand what we're modeling?
What's the role of symmetry in modeling financial markets?
What are the main challenges in applying causal models in finance?
Ready to dive in?
About The Guest
Alexander Denev is the CEO of Turnleaf Analytics. He's an author of multiple books on financial modeling and a former Head of AI (Financial Services) at Deloitte. He lectures at the University of Oxford and has worked for organizations like IHS Markit, The Royal Bank of Scotland (RBS), and the European Investment Bank. He has over 20 years of experience in finance, data science, and modeling. His first book about causal models was published well ahead of its time.
Connect with Alexander:
- Alexander on LinkedIn
- Alexander's web page
About The Host
Aleksander (Alex) Molak is an independent machine learning researcher, educator, entrepreneur and a best-selling author in the area of causality.
Connect with Alex:
- Alex on the Internet
Full list of links can be found here.
#machinelearning #causalai #causalinference #causality #finance #CauslBanditsPodcast
Support the Show.
Causal Bandits Podcast
Causal AI || Causal Machine Learning || Causal Inference & Discovery
Web: https://causalbanditspodcast.com
Connect on LinkedIn: https://www.linkedin.com/in/aleksandermolak/
Join Causal Python Weekly: https://causalpython.io
The Causal Book: https://amzn.to/3QhsRz4 -
Causal Inference & Reinforcement Learning with Andrew Lampinen Ep 13 | CausalBanditsPodcast.com
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Video version of this episode is available here
Causal Inference with LLMs and Reinforcement Learning Agents?
Do LLMs have a world model?
Can they reason causally?
What's the connection between LLMs, reinforcement learning, and causality?
Andrew Lampinen, PhD (Google DeepMind) shares the insights from his research on LLMs, reinforcement learning, causal inference and generalizable agents.
We also discuss the nature of intelligence, rationality and how they play with evolutionary fitness.
Join us in the journey!
Recorded on Dec 1, 2023 in London, UK.
About The Guest
Andrew Lampinen, PhD is a Senior Research Scientist at Google DeepMind. He holds a PhD in PhD in Cognitive Psychology from Stanford University. He's interested in cognitive flexibility and generalization, and how these abilities are enabled by factors like language, memory, and embodiment.
Connect with Andrew:
- Andrew on Twitter/X
- Andrew's web page
About The Host
Aleksander (Alex) Molak is an independent machine learning researcher, educator, entrepreneur and a best-selling author in the area of causality (https://amzn.to/3QhsRz4).
Connect with Alex:
- Alex on the Internet
Links
Papers
- Lampinen et al. (2023) - "Passive learning of active causal strategies in agents and language models" (https://arxiv.org/pdf/2305.16183.pdf)
- Dasgupta, Lampinen, et al. (2022) Language models show human-like content effects on reasoning tasks" (https://arxiv.org/abs/2207.07051)
- Santoro, Lampinen, et al. (2021) - "Symbolic behaviour in artificial intelligence" (https://www.researchgate.net/publication/349125191_Symbolic_Behaviour_in_Artificial_Intelligence)
- Webb et al. (2022) - “Emergent Analogical Reasoning in Large Language Models” (https://arxiv.org/abs/2212.09196)
Books
- Tomasello (2019) - “Becoming Human” (https://amzn.to/43M3nAg)
- Joyce (1922) - “Ulysses” (https://amzn.to/3PKvb21)
#machinelearning #causalai
Support the Show.
Causal Bandits Podcast
Causal AI || Causal Machine Learning || Causal Inference & Discovery
Web: https://causalbanditspodcast.com
Connect on LinkedIn: https://www.linkedin.com/in/aleksandermolak/
Join Causal Python Weekly: https://causalpython.io
The Causal Book: https://amzn.to/3QhsRz4 -
Causal Inference, Clinical Trials & Randomization || Stephen Senn || Causal Bandits Ep. 012 (2024)
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Video version available on YouTube
Do We Need Probability?
Causal inference lies at the very heart of the scientific method.
Randomized controlled trials (RCTs; also known as randomized experiemnts or A/B tests) are often called "the golden standard for causal inference".
It's a less known fact that randomized trials have their limitations in answering causal questions.
What are the most common myths about randomization?
What causal questions can and cannot be answered with randomized experiments? Finally, why do we need probability?
Join me on a fascinating journey into clinical trials, randomization and generalization.
Ready to meet Stephen Senn?
About The Guest
Stephen Senn, PhD, is a statistician and consultant specializing in clinical trials for drug development. He is a former Group Head at Ciba-Geigy and has served as a professor at the University of Glasgow and University College London (UCL). He is the author of "Statistical Issues in Drug Development," "Crossover Trials in Clinical Research," and "Dicing with Death".
Connect with Stephen:
- Stephen on Twitter/X
- Stephen on LinkedIn
- Stephen's web page
About The Host
Aleksander (Alex) Molak is an independent machine learning researcher, educator, entrepreneur and a best-selling author in the area of causality.
Connect with Alex:
- Alex on the Internet
Links
Find the links here
Causal Bandits Team
Project Coordinator: Taiba Malik
Video and Audio Editing: Navneet Sharma, Aleksander Molak
#causalai #causalinference #causality #abtest #statistics #experiements
Support the Show.
Causal Bandits Podcast
Causal AI || Causal Machine Learning || Causal Inference & Discovery
Web: https://causalbanditspodcast.com
Connect on LinkedIn: https://www.linkedin.com/in/aleksandermolak/
Join Causal Python Weekly: https://causalpython.io
The Causal Book: https://amzn.to/3QhsRz4 -
Causal Models, Biology, Generative AI & RL || Robert Ness || Causal Bandits Ep. 011 (2024)
Support the show
Video version available on YouTube
Recorded on Nov 12, 2023 in Undisclosed location, Undisclosed location
From Systems Biology to Causality
Robert always loved statistics.
He went to study systems biology, driven by his desire to model natural systems.
His perspective on causal inference encompasses graphical models, Bayesian inference, reinforcement learning, generative AI and cognitive science.
It allows him to think broadly about the problems we encounter in modern AI research.
Is the reward enough and what's the next big thing in causal (generative) AI?
Let's see!
About The Guest
Robert Osazuwa Ness is a Senior Researcher at Microsoft Research. He explores how to combine causal discovery, causal inference, deep probabilistic modeling, and programming languages in search of new capabilities for AI systems.
Connect with Robert:
- Robert on Twitter/X
- Robert on LinkedIn
- Robert's web page
About The Host
Aleksander (Alex) Molak is an independent machine learning researcher, educator, entrepreneur and a best-selling author in the area of causality.
Connect with Alex:
- Alex on the Internet
Links
Find the links here
Causal Bandits Team
Project Coordinator: Taiba Malik
Video and Audio Editing: Navneet Sharma, Aleksander Molak
#causalai #causalinference #causality
Support the Show.
Causal Bandits Podcast
Causal AI || Causal Machine Learning || Causal Inference & Discovery
Web: https://causalbanditspodcast.com
Connect on LinkedIn: https://www.linkedin.com/in/aleksandermolak/
Join Causal Python Weekly: https://causalpython.io
The Causal Book: https://amzn.to/3QhsRz4 -
Causal AI & Supply Chain || Ishansh Gupta || Causal Bandits Ep. 010 (2024)
Support the show
Video version available on YouTube
Recorded on Sep 27, 2023 in München, Germany
From supply chain to large language models and back
Ishansh realized the potential of data when he was just 10 years old, during his time as a junior cricket player.
His journey led him to ask questions about the mechanisms behind the observed events.
Can large language models (LLMs) help in building an industrial causal graph?
What inspires stakeholders to share their knowledge and which causal discovery algorithms have been most effective for Ishansh's supply chain use case?
Hear the insights from one of the BMW Group's fastest-rising young data science talents.
Ready?
About The Guest
Ishansh Gupta is a Lead Data Scientist at BMW Group. Previously, he worked for several companies, including a legendary German sports club SV Werder Bremen. He studied Computer Science, and co-founded an educational startup during his study years. He has supervised or supported students in various universities, including the Munich-based TUM and MIT.
Connect with Ishansh:
- Ishansh on Twitter/X
- Ishansh on LinkedIn
About The Host
Aleksander (Alex) Molak is an independent machine learning researcher, educator, entrepreneur and a best-selling author in the area of causality
Connect with Alex:
- Alex on the Internet
Links
Papers
Full list of papers here
Books
- Molak (2023) - Causal Inference and Discovery in Python
- Pearl & Mackenzie (2019) - The Book of Why
Other
- causaLens
Causal Bandits Team
Project Coordinator: Taiba Malik
Video and Audio Editing: Navneet Sharma, Aleksander Molak
Support the Show.
Causal Bandits Podcast
Causal AI || Causal Machine Learning || Causal Inference & Discovery
Web: https://causalbanditspodcast.com
Connect on LinkedIn: https://www.linkedin.com/in/aleksandermolak/
Join Causal Python Weekly: https://causalpython.io
The Causal Book: https://amzn.to/3QhsRz4
Customer Reviews
Thoughtful and expert discussions of causality
I appreciate the thoughtful discussions. You can tell that the speakers have true experience and expertise in causal modeling.
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