Talking Machines – Détails, épisodes et analyse

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Talking Machines

Tote Bag Productions

Technology

News

Fréquence : 1 épisode/22j. Total Éps: 110

Talking Machines is your window into the world of machine learning. Your hosts, Katherine Gorman and Neil Lawrence, bring you clear conversations with experts in the field, insightful discussions of industry news, and useful answers to your questions. Machine learning is changing the questions we can ask of the world around us, here we explore how to ask the best questions and what to do with the answers.

Hosted on Acast. See acast.com/privacy for more information.

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🇬🇧 Grande Bretagne - technology
15/05/2025
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🇬🇧 Grande Bretagne - technology
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16/01/2025
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🇩🇪 Allemagne - technology
20/10/2024
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https://omnystudio.com/listener
834804 partages
https://jupyter.org/
52 partages
https://www.coursera.org/learn/machine-learning
22 partages

https://www.youtube.com/watch?v=jeGJax4SLP0
1 partage
https://www.youtube.com/watch?v=Bn-33vv-cmY
1 partage
https://www.youtube.com/watch?v=uMdHGOhSIJY
1 partage

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Gods and Robots

jeudi 9 septembre 2021 • Durée 40:05

In this episode of the podcast we shake things up! Neil is on the guest side of the table with his partner Rabbi Laura Janner-Klausner to discuss their upcoming project Gods and Robots. Katherine is joined on the host side by friend of the show professor Michael Littman.

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Hosted on Acast. See acast.com/privacy for more information.

Responsibility, Risk, and Publishing

jeudi 19 août 2021 • Durée 25:40

On this episode we feature an interview with Madhulika Shrikumar of the Partnership on AI about their recent work Managing Risk and Responsible Publication

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Prioritizing Problems and 100 episodes

Saison 6 · Épisode 4

vendredi 20 mars 2020 • Durée 30:55

Episode four of season six is our 100th episode! (Well it's Katherine's). We take a break from our regular format for Neil and Katherine to chat about the current situation around Covid-19, understanding exponentials, and what impact this might have on how problems get prioritized.

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Interdisciplinary Data and Helping Humans Be Creative

jeudi 7 mai 2015 • Durée 34:17

In Episode 10 we talk with David Blei of Columbia University. We talk about his work on latent dirichlet allocation, topic models, the PhD program in data that he’s helping to create at Columbia and why exploring data is inherently multidisciplinary. We learn about Markov Chain Monte Carlo and take a listener question about how machine learning can make humans more creative.

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Starting Simple and Machine Learning in Meds

jeudi 23 avril 2015 • Durée 38:24

In episode nine we talk with George Dahl, of the University of Toronto, about his work on the Merck molecular activity challenge on kaggle and speech recognition. George recently successfully defended his thesis at the end of March 2015. (Congrats George!) We learn about how networks and graphs can help us understand latent properties of relationships, and we take a listener question about just how you find the right algorithm to solve a problem (Spoiler: start simple.)

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Spinning Programming Plates and Creative Algorithms

jeudi 9 avril 2015 • Durée 35:18

On episode eight we talk with Charles Sutton, a professor in the School of Informatics University of Edinburgh about computer programming and using machine learning how to better understand how it’s done well. Ryan introduces us to collaborative filtering, a process that helps to make predictions about taste. Netflix and Amazon use it to recommend movies and items. It's the process that the Netflix Prize competition further helped to hone. Plus, we take a listener question on creativity in algorithms.

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The Automatic Statistician and Electrified Meat

jeudi 26 mars 2015 • Durée 45:40

In episode seven of Talking Machines we talk with Zoubin Ghahramani, professor of Information Engineering in the Department of Engineering at the University of Cambridge. His project, The Automatic Statistician, aims to use machine learning to take raw data and give you statistical reports and natural languages summaries of what trends that data shows. We get really hungry exploring Bayesian Non-parametrics through the stories of the Chinese Restaurant Process and the Indian Buffet Process (but remember, there’s no free lunch). Plus we take a listener question about how much we should rely on ourselves and our ideas about what intelligence in electrified meat looks like when we try to build machine intelligences.

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The Future of Machine Learning from the Inside Out

vendredi 13 mars 2015 • Durée 28:14

We hear the second part of our conversation with with Geoffrey Hinton (Google and University of Toronto), Yoshua Bengio (University of Montreal) and Yann LeCun (Facebook and NYU). They talk with us about this history (and future) of research on neural nets. We explore how to use Determinantal Point Processes. Alex Kulesza and Ben Taskar (who passed away recently) have done some really exciting work in this area, for more on DPPs check out their paper on the topic. Also, we take a listener question about machine learning and function approximation (spoiler alert: it is, and then again, it isn’t).

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The History of Machine Learning from the Inside Out

jeudi 26 février 2015 • Durée 32:36

In episode five of Talking Machines, we hear the first part of our conversation with Geoffrey Hinton (Google and University of Toronto), Yoshua Bengio (University of Montreal) and Yann LeCun (Facebook and NYU). Ryan introduces us to the ideas in tensor factorization methods for learning latent variable models (which is both a tongue twister and and one of the new tools in ML). To find out more on the topic, the paper Tensor decompositions for learning latent variable models is a good place to start. You can also take a look at the work of Daniel Hsu, Animashree Anandkumar and Sham M. Kakade Plus we take a listener question about just where statistics stops and machine learning begins.

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Using Models in the Wild and Women in Machine Learning

jeudi 12 février 2015 • Durée 45:06

In episode four we talk with Hanna Wallach, of Microsoft Research. She's also a professor in the Department of Computer Science, University of Massachusetts Amherst and one of the founders of Women in Machine Learning (better known as WiML). We take a listener question about scalability and the size of data sets. And Ryan takes us through topic modeling using Latent Dirichlet allocation (say that five times fast).

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Hosted on Acast. See acast.com/privacy for more information.

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