I am a big fan of the Talking Biotech Podcast which allows me to keep up with some of the latest issues and research in biotechnology and medicine. A recent episode related to AI and machine learning covered a lot of topics that resonated with me.
There was excellent discussion on the human element involved in this work, and the importance of data data prep/feature engineering (the 80% of work that has to happen before the ML/AI can do its job) and the challenges of non-standard 'omics' data. Also the potential biases that researchers and developers can inadvertently introduce in this process. Much more including applications of machine learning and AI in this space and best ways to stay up to speed on fast changing technologies without having to be a heads down programmer.
I've been in a data science role since 2008 and have transitioned from SAS to R to python. I've been able to keep up within the domain of causal inference to the extent possible, but I keep up with broader trends I am interested in via podcasts like Talking Biotech. Below is a curated list of my favorites related to data science with a few of my favorite episodes highlighted.
1) Casual Inference - This is my new favorite podcast by two biostatisticians covering epidemiology/biostatistics/causal inference - and keeping it casual.
Fairness in Machine Learning with Sherri Rose | Episode 03 - http://casualinfer.libsyn.com/fairness-in-machine-learning-with-sherri-rose-episode-03
This episode was the inspiration for my post: When Wicked Problems Meet Biased Data.
Fairness in Machine Learning with Sherri Rose | Episode 03 - http://casualinfer.libsyn.com/fairness-in-machine-learning-with-sherri-rose-episode-03
This episode was the inspiration for my post: When Wicked Problems Meet Biased Data.
#131 - The One Purpose of Data Science https://www.superdatascience.com/podcast/podcast-one-purpose-data-science-truth-analytics
#051 Understanding the Newest Big Data Technology Buzz Terms https://www.superdatascience.com/podcast/sds-051-understanding-newest-big-data-technology-buzz-terms
#093 Evolutionary Programming -
#266 - Can we trust scientific discoveries made using machine learning
#288 Automated ML for RNA Design https://twimlai.com/twiml-talk-288-automated-ml-for-rna-design-with-danny-stoll/
How social science research can inform the design of AI systems https://www.oreilly.com/radar/podcast/how-social-science-research-can-inform-the-design-of-ai-systems/
#37 Causality and potential outcomes with Irineo Cabreros - https://bioinformatics.chat/potential-outcomes
Jim Manzi 'Uncontrolled' - https://www.econtalk.org/manzi-on-knowledge-policy-and-uncontrolled/
Jim Manzi - Oregon Medicaid Experiment - https://www.econtalk.org/jim-manzi-on-the-oregon-medicaid-study-experimental-evidence-and-causality/
Susan Athey - Big Data and Causality https://www.econtalk.org/susan-athey-on-machine-learning-big-data-and-causation/
Josh Angrist - Econometrics and Causation https://www.econtalk.org/joshua-angrist-on-econometrics-and-causation/
Andrew Gelman - Social Science, Small Samples, and the Garden of Forking Paths https://www.econtalk.org/andrew-gelman-on-social-science-small-samples-and-the-garden-of-the-forking-paths/
James Heckman - Facts, Evidence, and the State of Econometrics https://www.econtalk.org/james-heckman-on-facts-evidence-and-the-state-of-econometrics/
See also:
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