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Вміст надано Alex Molak. Весь вміст подкастів, включаючи епізоди, графіку та описи подкастів, завантажується та надається безпосередньо компанією Alex Molak або його партнером по платформі подкастів. Якщо ви вважаєте, що хтось використовує ваш захищений авторським правом твір без вашого дозволу, ви можете виконати процедуру, описану тут https://uk.player.fm/legal.
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Causal AI, Justin Bieber & Optimal Experiments || Jakob Zeitler || Causal Bandits Ep. 007 (2024)

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Manage episode 394078547 series 3526805
Вміст надано Alex Molak. Весь вміст подкастів, включаючи епізоди, графіку та описи подкастів, завантажується та надається безпосередньо компанією Alex Molak або його партнером по платформі подкастів. Якщо ви вважаєте, що хтось використовує ваш захищений авторським правом твір без вашого дозволу, ви можете виконати процедуру, описану тут https://uk.player.fm/legal.

Send us a text

Support the show
Video version of this episode is available here
Recorded on Sep 5, 2023 in Oxford, UK

Have you ever wondered if we can answer seemingly unanswerable questions?
Jakob's journey into causality started when he was 12 years old.
Deeply dissatisfied with what adults had to offer when asked about the sources of causal knowledge, he started to look for the answers on his own.
He studied philosophy, politics and economics to find his place at UCL's Centre for Artificial Intelligence, where he met his future PhD advisor, Prof. Ricardo Silva.
At the center of Jakob's interests lies decision-making under partial knowledge.
He's passionate about partial identification, sensitivity analysis, and optimal experiments, yet he's far from being just a theoretician.
He implements causal ideas he finds promising in the context of material discovery at Matterhorn Studio, earlier he worked on sensitivity analysis for quasi-experimental methods at Spotify.
Want to learn what a 1000-years-old church, communism and Justin Bieber have to do with causality?
Tune in! ------------------------------------------------------------------------------------------------------
About The Guest
Jakob Zeitler is a researcher at Centre for Artificial Intelligence at University College London (UCL) and a Head of R&D at Matterhorn Studio. His research focuses on partial identification, sensitivity analysis and optimal experimentation. He works on solutions for automated material design.
Connect with Jakob:
- Jakob Zeitler on Twitter/X
- Jakob Zeitler on LinkedIn
- Jakob Zeitler's web page
About The Host
Aleksander (Alex) Molak is an independent machine learning researcher, educator, entrepreneur and a

Rumi.ai
All-in-one meeting tool with real-time transcription & searchable Meeting Memory™
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

  continue reading

Розділи

1. Causal AI, Justin Bieber & Optimal Experiments || Jakob Zeitler || Causal Bandits Ep. 007 (2024) (00:00:00)

2. [Ad] Rumi.ai (00:16:07)

3. (Cont.) Causal AI, Justin Bieber & Optimal Experiments || Jakob Zeitler || Causal Bandits Ep. 007 (2024) (00:16:56)

27 епізодів

Artwork
iconПоширити
 
Manage episode 394078547 series 3526805
Вміст надано Alex Molak. Весь вміст подкастів, включаючи епізоди, графіку та описи подкастів, завантажується та надається безпосередньо компанією Alex Molak або його партнером по платформі подкастів. Якщо ви вважаєте, що хтось використовує ваш захищений авторським правом твір без вашого дозволу, ви можете виконати процедуру, описану тут https://uk.player.fm/legal.

Send us a text

Support the show
Video version of this episode is available here
Recorded on Sep 5, 2023 in Oxford, UK

Have you ever wondered if we can answer seemingly unanswerable questions?
Jakob's journey into causality started when he was 12 years old.
Deeply dissatisfied with what adults had to offer when asked about the sources of causal knowledge, he started to look for the answers on his own.
He studied philosophy, politics and economics to find his place at UCL's Centre for Artificial Intelligence, where he met his future PhD advisor, Prof. Ricardo Silva.
At the center of Jakob's interests lies decision-making under partial knowledge.
He's passionate about partial identification, sensitivity analysis, and optimal experiments, yet he's far from being just a theoretician.
He implements causal ideas he finds promising in the context of material discovery at Matterhorn Studio, earlier he worked on sensitivity analysis for quasi-experimental methods at Spotify.
Want to learn what a 1000-years-old church, communism and Justin Bieber have to do with causality?
Tune in! ------------------------------------------------------------------------------------------------------
About The Guest
Jakob Zeitler is a researcher at Centre for Artificial Intelligence at University College London (UCL) and a Head of R&D at Matterhorn Studio. His research focuses on partial identification, sensitivity analysis and optimal experimentation. He works on solutions for automated material design.
Connect with Jakob:
- Jakob Zeitler on Twitter/X
- Jakob Zeitler on LinkedIn
- Jakob Zeitler's web page
About The Host
Aleksander (Alex) Molak is an independent machine learning researcher, educator, entrepreneur and a

Rumi.ai
All-in-one meeting tool with real-time transcription & searchable Meeting Memory™
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

  continue reading

Розділи

1. Causal AI, Justin Bieber & Optimal Experiments || Jakob Zeitler || Causal Bandits Ep. 007 (2024) (00:00:00)

2. [Ad] Rumi.ai (00:16:07)

3. (Cont.) Causal AI, Justin Bieber & Optimal Experiments || Jakob Zeitler || Causal Bandits Ep. 007 (2024) (00:16:56)

27 епізодів

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