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Causal AI, Modularity & Learning || Andrew Lawrence || Causal Bandits Ep. 002 (2023)

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Konten disediakan oleh Alex Molak. Semua konten podcast termasuk episode, grafik, dan deskripsi podcast diunggah dan disediakan langsung oleh Alex Molak atau mitra platform podcast mereka. Jika Anda yakin seseorang menggunakan karya berhak cipta Anda tanpa izin, Anda dapat mengikuti proses yang diuraikan di sini https://id.player.fm/legal.

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`from causality import solution`
Recorded on Sep 04, 2023 in London, United Kingdom
A Python package that would allow us to address an arbitrary causal problem with a one-liner does not yet exist.
Fortunately, there are other ways to implement and deploy causal solutions at scale.
In this episode, Andrew shares his journey into causality and gives us a glimpse into the behind-the-scenes of his everyday work at causaLens.
We discuss new ideas that Andrew and his team use to enhance the capabilities of available open-source causal packages, how they strive to build and maintain a highly modularized and open platform.
Finally, we talk about the importance of team work and what Andrew's parents did to make him feel nurtured & supported.
Ready?
About The Guest
Andrew Lawrence is the Director of Research at causaLens (https://causalens.com/) Connect with Andrew:

About The Host
Aleksander (Alex) Molak is an independent ML researcher, educator, entrepreneur and a best-selling author in the area of causality.
Connect with Alex:

Links

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

Chapter

1. Causal AI, Modularity & Learning || Andrew Lawrence || Causal Bandits Ep. 002 (2023) (00:00:00)

2. [Ad] Rumi.ai (00:25:53)

3. (Cont.) Causal AI, Modularity & Learning || Andrew Lawrence || Causal Bandits Ep. 002 (2023) (00:26:42)

28 episode

Artwork
iconBagikan
 
Manage episode 382502643 series 3526805
Konten disediakan oleh Alex Molak. Semua konten podcast termasuk episode, grafik, dan deskripsi podcast diunggah dan disediakan langsung oleh Alex Molak atau mitra platform podcast mereka. Jika Anda yakin seseorang menggunakan karya berhak cipta Anda tanpa izin, Anda dapat mengikuti proses yang diuraikan di sini https://id.player.fm/legal.

Send us a text

Support the show
`from causality import solution`
Recorded on Sep 04, 2023 in London, United Kingdom
A Python package that would allow us to address an arbitrary causal problem with a one-liner does not yet exist.
Fortunately, there are other ways to implement and deploy causal solutions at scale.
In this episode, Andrew shares his journey into causality and gives us a glimpse into the behind-the-scenes of his everyday work at causaLens.
We discuss new ideas that Andrew and his team use to enhance the capabilities of available open-source causal packages, how they strive to build and maintain a highly modularized and open platform.
Finally, we talk about the importance of team work and what Andrew's parents did to make him feel nurtured & supported.
Ready?
About The Guest
Andrew Lawrence is the Director of Research at causaLens (https://causalens.com/) Connect with Andrew:

About The Host
Aleksander (Alex) Molak is an independent ML researcher, educator, entrepreneur and a best-selling author in the area of causality.
Connect with Alex:

Links

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

Chapter

1. Causal AI, Modularity & Learning || Andrew Lawrence || Causal Bandits Ep. 002 (2023) (00:00:00)

2. [Ad] Rumi.ai (00:25:53)

3. (Cont.) Causal AI, Modularity & Learning || Andrew Lawrence || Causal Bandits Ep. 002 (2023) (00:26:42)

28 episode

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