Causal Bandits @ CLeaR 2024 | Part 1 | CausalBanditsPodcast.com

Causal Bandits Podcast

#Tech #Alex Molak #Science #Business #Causal AI #Causal #Causal Machine Learning #Machine Learning #Causal Inference #Causal Discovery #Artificial Intelligence

44:09

When a young Eva Kollisch arrives as a refugee in New York in 1940, she finds a community among socialists who share her values and idealism. She soon discovers ‘the cause’ isn’t as idyllic as it seems. Little does she know this is the beginning of a lifelong commitment to activism and her determination to create radical change in ways that include belonging, love and one's full self. In addition to Eva Kollisch’s memoirs Girl in Movement (2000) and The Ground Under My Feet (2014), LBI’s collections include an oral history interview with Eva conducted in 2014 and the papers of Eva’s mother, poet Margarete Kolllisch, which document Eva’s childhood experience on the Kindertransport. Learn more at www.lbi.org/kollisch . Exile is a production of the Leo Baeck Institute , New York | Berlin and Antica Productions . It’s narrated by Mandy Patinkin. Executive Producers include Katrina Onstad, Stuart Coxe, and Bernie Blum. Senior Producer is Debbie Pacheco. Associate Producers are Hailey Choi and Emily Morantz. Research and translation by Isabella Kempf. Sound design and audio mix by Philip Wilson, with help from Cameron McIver. Theme music by Oliver Wickham. Voice acting by Natalia Bushnik. Special thanks to the Kollisch family for the use of Eva’s two memoirs, “Girl in Movement” and “The Ground Under My Feet”, the Sophia Smith Collection at Smith College and their “Voices of Feminism Oral History Project”, and Soundtrack New York.…

год назад 22:11

MP3•Главная эпизода

Send us a text

Root cause analysis, model explanations, causal discovery.
Are we facing a missing benchmark problem?
Or not anymore?
In this special episode, we travel to Los Angeles to talk with researchers at the forefront of causal research, exploring their projects, key insights, and the challenges they face in their work.
Time codes:
0:15 - 02:40 Kevin Debeire
2:41 - 06:37 Yuchen Zhu
06:37 - 10:09 Konstantin Göbler
10:09 - 17:05 Urja Pawar
17:05 - 23:16 William Orchard
Enjoy!

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

30 эпизодов

Causal Bandits Podcast

Causal Bandits @ CLeaR 2024 | Part 1 | CausalBanditsPodcast.com

Causal Bandits Podcast

published год назад

MP3•Главная эпизода

Send us a text

Support the show

30 эпизодов

#Tech #Alex Molak #Science #Business #Causal AI #Causal #Causal Machine Learning #Machine Learning #Causal Inference #Causal Discovery #Artificial Intelligence

كل الحلقات

Causal Bandits Podcast

1
From Quantum Physics to Causal AI at Spotify | Ciarán Gilligan-Lee S2E2 | CausalBanditsPodcast.com 52:10

5 weeks назад52:10

52:10

Send us a text From Quantum Causal Models to Causal AI at Spotify Ciarán loved Lego. Fascinated by the endless possibilities offered by the blocks, he once asked his parents what he could do as an adult to keep building with them. The answer: engineering. As he delved deeper into engineering, Ciarán noticed that its rules relied on a deeper structure. This realization inspired him to pursue quantum physics, which eventually brought him face-to-face with fundamental questions about causality. Today, Ciarán blends his deep understanding of physics and quantum causal models with applied work at Spotify, solving complex problems in innovative ways. Recently, while collaborating with one of his students, he stumbled upon a new interesting question: could we learn something about the early history of the universe by applying causal inference methods in astrophysics? Could we? Hear it from Ciarán himself. Join us for this one-of-a-kind conversation! ------------------------------------------------------------------------------------------------------ Video version and episode links available on YouTube Recorded on Nov 6, 2024 in Dublin, Ireland. ------------------------------------------------------------------------------------------------------ About The Guest Ciarán Gilligan-Lee is Head of the Causal Inference Research Lab at Spotify and Honorary Associate Professor at University College London. He got interested in causality during his studies in quantum physics. This interest led him to study quantum causal models. He published in Nature Machine Intelligence, Nature Quantum Information, Physical Review Letters, New Journal of Physics and more. In his free time, he writes for New Scientist and helps his students apply causal methods in new fields (e.g., astrophysics). Connect with Ciarán: - Ciarán on LinkedIn: https://www.linkedin.com/in/ciaran-gilligan-lee/ - Ciarán's web page: https://www.ciarangilliganlee.com/ About The Host Aleksander (Alex) Molak is an independent machine learning researcher, educator, entrepreneur 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 Bandits Podcast

1
49% Less Loss with Causal ML | Stefan Feuerriegel S2E1 | CausalBanditsPodcast.com 28:35

7 weeks назад28:35

28:35

Send us a text Stefan Feuerriegel is the Head of the Institute of AI in Management at LMU. His team consistently publishes work on causal machine learning at top AI conferences, including NeurIPS, ICML, and more. At the same time, they help businesses implement causal methods in practice. They worked on projects with companies like ABB Hitachi, and Booking.com. Stefan believes his team thrives because of its diversity and aims to bring more causal machine learning to medicine. I had a great conversation with him, and I hope you'll enjoy it too! >> Guest info: Stefan Feuerriegel is a professor and the Head of the Institute of AI in Management at LMU. Previously, he worked as a consultant at McKinsey & Co. and ran his own AI startup. >> Episode Links: Papers - Feuerriegel, S. et al. (2024) - Causal machine learning for predicting treatment outcomes ( https://www.nature.com/articles/s41591-024-02902-1 ) - Kuzmanivic, M. et al. (2024) - Causal Machine Learning for Cost-Effective Allocation of Development Aid ( https://arxiv.org/abs/2401.16986 ) - Schröder, M. et al. (2024) - Conformal Prediction for Causal Effects of Continuous Treatments ( https://arxiv.org/abs/2407.03094 ) >> WWW: https://www.som.lmu.de/ai/ >> LinkedIn: https://www.linkedin.com/in/stefan-feuerriegel/ 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 Bandits Podcast

1
Causal AI at cAI 2024 London | CausalBanditsPodcast.com 20:01

12 weeks назад20:01

20:01

Send us a text Causal Bandits at cAI 2024 (The Royal Society, London) The cAI Conference in London slammed the door on baseless claims that causality cannot be used in industrial practice. In the episode of Causal Bandits Extra we interview participants and speakers at Causal AI Conference London, who share their main insights from the event, and the challenges they face in applying causal methods in their everyday work. Time codes: 00:29 - Eyal Kazin (Zimmer Biomet) 01:44 - Athanasios Vlontzos (Spotify) 04:02 - Mimie Liotsiou (Dunnhumby) 06:13 - Fernanda Hinze (Croud) 09:00 - Clara Higuera Cabañes (BBVA) 10:28 - Javier Moral Hernández (BBVA) 11:25 - Álvaro Ibraín Rodríguez (BBVA) 12:10 - Hugo Proença (Booking.com) 13:21 - Debora Andrade (Seamless AI) 15:09 - Puneeth Nikin (Croud) 17:54 - Puneet Gupta (Cisco) 19:43 - Arthur Mello (Sephora) ============================= 🔔Unlock the power of Python in AI and machine learning. Subscribe for simple insights into Causal Inference and Discovery. / @causalpython ✅ Important Links to Follow 🔗Medium Blog / aleksander-molak 🔗Newsletter Web https://causalpython.io/ 🔗 Links https://bit.ly/m/alex-bio 🔗 GitHub https://github.com/AlxndrMlk ✅ Stay Connected With Me. 👉Twitter (X): / aleksandermolak 👉Linkedin: / aleksandermolak 👉Facebook: / causalpython 👉Instagram: / alex.molak 👉Tiktok: / alex.molak 👉Causal Bandits Podcast Website: https://causalbanditspodcast.com/ ✅ For Business Inquiries: hello@causalpython.io ============================= ✅ Recommended Playlists 👉 Causal Bandits Podcast • Matej Zečević On Causality In AI: Can... 👉 Causal Bandits Podcast Shorts • Answer with Causal Identification #po.. ================================= © Causal Python with Alex 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…

Causal Bandits Podcast

1
Causal Bandits @ CLeaR 2024 | Part 2 | CausalBanditsPodcast.com 22:05

18 weeks назад22:05

22:05

Send us a text Which models work best for causal discovery and double machine learning? In this extra episode, we present 4 more conversations with the researchers presenting their work at the CLeaR 2024 conference in Los Angeles, California. What you'll learn: - Which causal discovery models perform best with their default hyperparameters? - How to tune your double machine learning model? - Does putting your paper on ArXiv early increase its chances of being accepted at a conference? - How to deal with causal representation learning with multiple latent interventions? Time codes: 00:24 Damian Machlanski - Hyperparameter Tuning for Causal Discovery 08:52 Oliver Schacht - Hyperparameter Tuning for DML 14:41 Yanai Elazar - Causal Effect of Early ArXiving on Paper Acceptance 18:53 Simon Bing - Identifying Linearly-Mixed Causal Representations from Multi-Node Interventions ============================= 🔔Unlock the power of Python in AI and machine learning. Subscribe for simple insights into Causal Inference and Discovery. https://www.youtube.com/@CausalPython/?sub_confirmation=1 ✅ Stay Connected With Me. 👉Twitter (X): https://twitter.com/AleksanderMolak 👉Linkedin: https://www.linkedin.com/in/aleksandermolak/ 👉Facebook: https://www.facebook.com/CausalPython 👉Instagram: https://www.instagram.com/alex.molak/ 👉Tiktok: https://www.tiktok.com/@alex.molak 👉Causal Bandits Podcast Website: https://causalbanditspodcast.com/ ✅ For Business Inquiries: hello@causalpython.io ============================= ✅ About Causal Python with Alex Molak. Welcome to my official YouTube channel, Causal Python, with Alex Molak. Dive into the fascinating world of Causal AI, unraveling the complexities of Causal Inference and Discovery with Python. My content simplifies these intricate topics, making them accessible whether you’re starting or advancing your knowledge. Here, I explore the intersections of causality, AI, machine learning, optimization, and decision-making, all through Python’s versatile capabilities. This is also the home of The Causal Bandits Podcast. For more insightful discussions, check out my Causal Bandit Podcast Website. ============ 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 Bandits Podcast

1
Causal Bandits @ CLeaR 2024 | Part 1 | CausalBanditsPodcast.com 22:11

21 weeks назад22:11

22:11

Send us a text Root cause analysis, model explanations, causal discovery. Are we facing a missing benchmark problem? Or not anymore? In this special episode, we travel to Los Angeles to talk with researchers at the forefront of causal research, exploring their projects, key insights, and the challenges they face in their work. Time codes: 0:15 - 02:40 Kevin Debeire 2:41 - 06:37 Yuchen Zhu 06:37 - 10:09 Konstantin Göbler 10:09 - 17:05 Urja Pawar 17:05 - 23:16 William Orchard Enjoy! 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 Bandits Podcast

1
Causal Bandits @ AAAI 2024 | Part 2 | CausalBanditsPodcast.com 22:38

23 weeks назад22:38

22:38

Send us a text *Causal Bandits at AAAI 2024 || Part 2* In this special episode we interview researchers who presented their work at AAAI 2024 in Vancouver, Canada. Time codes: 00:12 - 04:18 Kevin Xia (Columbia University) - Transportability 4:19 - 9:53 Patrick Altmeyer (Delft) - Explainability & black-box models 9:54 - 12:24 Lokesh Nagalapatti (IIT Bombay) - Continuous treatment effects 12:24 - 16:06 Golnoosh Farnadi (McGill University) - Causality & responsible AI 16:06 - 17:37 Markus Bläser (Saarland University) - Fast identification of causal parameters 17:37 - 22:37 Devendra Singh Dhami (TU/e) - The future of causal AI 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 Bandits Podcast

1
Causal Bandits @ AAAI 2024 | Part 1 | CausalBanditsPodcast.com 19:35

25 weeks назад19:35

19:35

Send us a text Causal Bandits at AAAI 2024 || Part 1 In this special episode we interview researchers who presented their work at AAAI 2024 in Vancouver, Canada and participants of our workshop on causality and large language models (LLMs) Time codes: 00:00 Intro 00:20 Osman Ali Mian (CISPA) - Adaptive causal discovery for time series 04:35 Emily McMilin (Independent/Meta) - LLMs, causality & selection bias 07:36 Scott Mueller (UCLA) - Causality for EV incentives 12:41 Andrew Lampinen (Google DeepMind) - Causality from passive data 15:16 Ali Edalati (Huawei) - About Causal Parrots workshop 15:26 Adbelrahman Zayed (MILA) - About Causal Parrots workshop 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 Bandits Podcast

1
Free Will, LLMs & Intelligence | Judea Pearl Ep 21 | CausalBanditsPodcast.com 54:07

29 weeks назад54:07

54:07

Send us a text Meet The Godfather of Modern Causal Inference His work has pretty literally changed the course of my life and I am honored and incredibly grateful we could meet for this great conversation in his home in Los Angeles To anybody who knows something about modern causal inference, he needs no introduction. He loves history, philosophy and music, and I believe it's fair to say that he's the godfather of modern causality. Ladies & gentlemen, please welcome, professor Judea Pearl. Subscribe to never miss an episode About The Guest Judea Pearl is a computer scientist, and a creator of the Structural Causal Model (SCM) framework for causal inference. In 2011, he has been awarded the Turing Award, the highest distinction in computer science, for his pioneering works on Bayesian networks and graphical causal models and "fundamental contributions to artificial intelligence through the development of a calculus for probabilistic and causal reasoning". Connect with Judea: Judea on Twitter/X Judea's webpage 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 Pearl, J. - "The Book of Why" Kahneman, D. - "Thinking, Fast and Slow" Molak, A. - "Causal Inference & Discovery in Python" 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 Bandits Podcast

1
Causal AI & Individual Treatment Effects | Scott Mueller Ep. 20 | CausalBanditsPodcast.com 52:40

32 weeks назад52:40

52:40

Send us a text Can we say something about YOUR personal treatment effect? The estimation of individual treatment effects is the Holy Grail of personalized medicine. It's also extremely difficult. Yet, Scott is not discouraged from studying this topic. In fact, he quit a pretty successful business to study it. In a series of papers, Scott describes how combining experimental and observational data can help us understand individual causal effects. Although this sounds enigmatic to many, the intuition behind this mechanism is simpler than you might think. In the episode we discuss: 🔹 What made Scott quit a successful business he founded and study causal inference? 🔹 How a false conviction about his own skills helped him learn? 🔹 What are individual treatment effects? 🔹 Can we really say something about individual treatment effects? Ready to dive in? About The Guest Scott Mueller is a researcher and a PhD candidate in causal modeling at UCLA, supervised by Prof. Judea Pearl. He's a serial entrepreneur and the founder of UCode, a coding school for kids. His current research focuses on the estimation of individual treatment effects and their bounds. He works under the supervision of professor Judea Pearl. Connect with Scott: - Scott on Twitter/X - Scott's webpage 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 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 Bandits Podcast

1
Causal AI in Personalization | Dima Goldenberg Ep 19 | CausalBanditsPodcast.com 1:06:40

35 weeks назад1:06:40

1:06:40

Send us a text Video version of this episode is available here Causal personalization? Dima did not love computers enough to forget about his passion for understanding people. His work at Booking.com focuses on recommender systems and personalization, and their intersection with AB testing, constrained optimization and causal inference. Dima's passion for building things started early in his childhood and continues up to this day, but recent events in his life also bring new opportunities to learn. In the episode, we discuss: What can we learn about human psychology from building causal recommender systems? What it's like to work in a culture of radical experimentation? Why you should not skip your operations research classes? Ready to dive in? About The Guest Dima Goldenberg is a Senior Machine Learning Manager at Booking.com, Tel Aviv, where he leads machine learning efforts in recommendations and personalization utilizing uplift modeling. Dima obtained his MSc in Tel Aviv University and currently pursuing PhD on causal personalization at Ben Gurion University of the Negev. He led multiple conference workshops and tutorials on causality and personalization and his research was published in top journals and conferences including WWW, CIKM, WSDM, SIGIR, KDD and RecSys. Connect with Dima: Dima 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 (https://amzn.to/3QhsRz4). Connect with Alex: - Alex on the Internet Links The full list of links is available here #machinelearning #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 Bandits Podcast

1
Causal Inference for Drug Repurposing & CausalLib | Ehud Karavani Ep 18 | CausalBanditsPodcast.com 53:32

37 weeks назад53:32

53:32

Send us a text Was Deep Learning Revolution Bad For Causal Inference? Did deep learning revolution slowed down the progress in causal research? Can causality help in finding drug repurposing candidates? What are the main challenges in using causal inference at scale? Ehud Karavani, the author of the CausalLib Python library and Researcher at IBM Research shares his experiences and thoughts on these challenging questions. Ehud believes in the power of good code, but for him code is not only about software development. He sees coding as an inseparable part of modern-day research. A powerful conversation for anyone interested in applied causal modeling. In this episode we discuss: Can causality help in finding drug repurposing candidates? Challenges in data processing for causal inference at scale Motivation behind Python causal inference library CausalLib Working at IBM Research Ready to dive in? About The Guest Ehud Karavani, MSc is Research Staff Member at IBM Research in the Causal Machine Learning for Healthcare & Life Sciences Group. He focuses on high-throughput causal inference for finding new indications for existing drugs using electronic health records and insurance claims data. He's the original author of Causallib - one of the first Python libraries specialized in causal inference. Connect with Ehud: Ehud on Twitter/X Ehud on LinkedIn Ehud'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 Links for this episode can be found here Video version of this episode 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 Bandits Podcast

1
From Physics to Causal AI & Back | Bernhard Schölkopf Ep 17 | CausalBanditsPodcast.com 34:20

39 weeks назад34:20

34:20

Send us a text Causal AI: The Melting Pot. Can Physics, Math & Biology Help Us? What is the relationship between physics and causal models? What can science of non-human animal behavior teach causal AI researchers? Bernhard Schölkopf's rich background and experience allow him to combine perspectives from computation, physics, mathematics, biology, theory of evolution, psychology and ethology to build a deep understanding of underlying principles that govern complex systems and intelligent behavior. His pioneering work in causal machine learning has revolutionized the field, providing new insights that enhance our ability to understand causal relationships and mechanisms in both natural and artificial systems. In the episode we discuss: Does evolution favor causal inference over correlation-based learning? Can differential equations help us generalize structural causal models? What new book is Bernhard working on? Can ethology inspire causal AI researchers? Ready to dive in? About The Guest Bernhard Schölkopf, PhD is a Director at Max Planck Institute for Intelligent Systems. He's one of the cofounders of European Lab for Learning & Intelligent Systems (ELLIS) and a recepient of the ACM Allen Newell Award, BBVA Foundation Frontiers of Knowledge Award, and more. His contributions to modern machine learning are hard to overestimate. He's a an affiliated professor at ETH Zürich, honorary professor at the University of Tübingen and the Technical University Berlin. His pioneering work on causal inference and causal machine learning inspired thousands of researchers and practitioners worldwide. Connect with Bernhard: Bernhard on Twitter/X Bernhard on LinkedIn Bernhard'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: https://bit.ly/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…

Causal Bandits Podcast

1
Open Source Causal AI & The Generative Revolution | Emre Kıcıman Ep 16 | CausalBanditsPodcast.com 42:20

41 weeks назад42:20

42:20

Send us a text What makes two tech giants collaborate on an open source causal AI package? Emre's adventure with causal inference and causal AI has started before it was trendy. He's one of the original core developers of DoWhy - one of the most popular and powerful Python libraries for causal inference - and a researcher focused on the intersection of causal inference, causal discovery, generative modeling and social impact. His unique perspective, inspired by his experience with low-level programming combined with his vivid interest in how humans interact with technology, is driven by a deep seated desire to solve problems that matter to people. In the episode we discuss: 🔹 What makes Microsoft and Amazon collaborate on an open source Python package? 🔹 Causal AI and the core of science 🔹 Is language model a world model? 🔹 When modeling physics is useful? Ready to dive in? Join the insightful discussions at https://causalbanditspodcast.com/ About The Guest Emre Kıcıman, PhD is a Senior Principal Research Manager at Microsoft Research. He's one of the core developers of the DoWhy Python package, alongside Amit Sharma. He holds a PhD in computer science from Stanford University. Privately, he loves to climb and spend time with his family. Connect with Emre: - Emre on Twitter/X - Emre on LinkedIn - Emre'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 Libraries - DoWhy (https://www.pywhy.org/dowhy/v0.11.1/) - EconML (https://econml.azurewebsites.net/) - CausalPy (https://causalpy.readthedocs.io/en/latest/) Books - Molak, A. - "Causal Inference and Discovery in Python" - Pearl, J. - "Causality" Causal Bandits Team Project Coordinator: 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 Bandits Podcast

1
Why Hinton Was Wrong, Causal AI & Science | Thanos Vlontzos Ep 15 | CausalBanditsPodcast.com 1:05:29

43 weeks назад1:05:29

1:05:29

Send us a text 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 Bandits Podcast

1
Causal Inference & Financial Modeling with Alexander Denev Ep 14 | CausalBanditsPodcast.com 1:04:09

46 weeks назад1:04:09