Stay ConnectedTalking 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.
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In episode seven of season five of we chat about MARS and Re: MARS OpenAI's status changes and We talk with Jasper Snoek of Google Brain
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In episode six of season five we talk about Richard Sutton's A Bitter Lesson. Chat about IEEE's new Ethical Guidelines and talk with Andrew Beam Senior Fellownn at Flagship Pioneering, Head of Machine Learning for Flagship VL57 and Assistant Professor, Department of Epidemiology, Harvard T.H. Chan School of Public Health.
Here are some of the papers we got to chat about! Also, VL57 is hiring!
Adversarial attacks on Medical ML Science paper
Finlayson, S.G., Bowers, J.D., Ito, J., Zittrain, J.L., Beam, A.L. and Kohane, I.S., 2019. Adversarial attacks on medical machine learning. Science, 363(6433), pp.1287-1289.
Link: https://cyber.harvard.edu/story/2019-03/adversarial-attacks-medical-ai-health-policy-challenge
JAMA Papers
Beam, A.L. and Kohane, I.S., 2016. Translating artificial intelligence into clinical care. Jama, 316(22), pp.2368-2369.
Link: https://www.dropbox.com/s/4o1va07tqwvrxsn/Beam_TranslatingAI_2016.pdf?dl=0
Beam, A.L. and Kohane, I.S., 2018. Big data and machine learning in health care. Jama, 319(13), pp.1317-1318.
Link: https://www.dropbox.com/s/q1cixzmsdugq3vy/Beam_BigData_ML.pdf?dl=0
Opportunities in machine learning for healthcare:
Ghassemi, M., Naumann, T., Schulam, P., Beam, A.L. and Ranganath, R., 2018. Opportunities in machine learning for healthcare. arXiv preprint arXiv:1806.00388.
Link: https://arxiv.org/abs/1806.00388
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In episode five of season five we talk about the Stu Hunter conference, Summer schools options (DLRLSS!) and chat with Adrian Weller of the Alan Turing Institute
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In episode four of season five we talk about Jupyter Notebooks and Neil's dream of a world craft software and devices, we take a listener question about the conversation surrounding Open AI's GPT-2 its announcement and the coverage and we hear an interview with Brooks Paige of the Alan Turing Instiute
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In season five episode three we chat about take a listener question about Five Papers for Mike Tipping, take a listener question on AIAI and chat with Eoin O'Mahony of Uber
Here are Neil's five papers. What are yours?
Stochastic variational inference by Hoffman, Wang, Blei and Paisley
http://arxiv.org/abs/1206.7051
A way of doing approximate inference for probabilistic models with potentially billions of data ... need I say more?
Austerity in MCMC Land: Cutting the Metropolis Hastings by Korattikara, Chen and Welling
http://arxiv.org/abs/1304.5299
Oh ... I do need to say more ... because these three are at it as well but from the sampling perspective. Probabilistic models for big data ... an idea so important it needed to be in the list twice.
Practical Bayesian Optimization of Machine Learning Algorithms by Snoek, Larochelle and Adams
http://arxiv.org/abs/1206.2944
This paper represents the rise in probabilistic numerics, I could also have chosen papers by Osborne, Hennig or others. There are too many papers out there already. Definitely an exciting area, be it optimisation, integration, differential equations. I chose this paper because it seems to have blown the field open to a wider audience, focussing as it did on deep learning as an application, so it let's me capture both an area of developing interest and an area that hits the national news.
Kernel Bayes Rule by Fukumizu, Song, Gretton
http://arxiv.org/abs/1009.5736
One of the great things about ML is how we have different (and competing) philosophies operating under the same roof. But because we still talk to each other (and sometimes even listen to each other) these ideas can merge to create new and interesting things. Kernel Bayes Rule makes the list.
http://www.cs.toronto.edu/~hinton/absps/imagenet.pdf
An obvious choice, but you don't leave the Beatles off lists of great bands just because they are an obvious choice.
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In episode two of season five we unpack the Bezos Paradox (TM Neil Lawrence) take a listener question about best papers and chat with Dougal Maclaurin of Google Brain.
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In episode one of season five we talk about Bit by Bit, take a listener question on machine learning gatherings on the African continent (Deep Learning INDABA! DSA!) and hear an interview with Daphne Koller recorded at ODSC West
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For the end of season four we take a break from our regular format and bring you a talk from Professor Finale Doshi Velez of Harvard University on the possibility of explanation Tune in next season!
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In episode twenty one of season four we talk about distributed intelligence systems (mainly those internal to humans), talk about what were excited to see at the Conference on Neural Information Processing Systems and in advance of our trek to Canada we chat with Garth Gibson president and CEO of the Vector Institute.
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In episode twenty of season four we talk about the importance of crediting your data, answer a listener question about internships vs salaried positions and talk with Matt Kusner of the Alan Turing institute the UK’s national institute for data science and AI.
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