Podcasts Technology Datacast Episode 32: Economics, Data For Good and AI Research with Sara Hooker

Episode 32: Economics, Data For Good and AI Research with Sara Hooker

Published: May 21, 2020, 8 p.m.

Show Notes:

  • (2:20) Sara shared her childhood growing up in Africa.
  • (4:05) Sara talked about her undergraduate experience at Carleton College studying Economics and International Relations.
  • (9:07) Sara discussed her first job working as an Economics Analyst at Compass Lexecon in the Bay Area.
  • (12:20) Sara then joined Udemy as a data analyst, then transitioned to the engineering team to work on spam detection and recommendation algorithms.
  • (14:58) Sara dig deep into the “hustling period” of her career and how she brute-forced her way to grow as an engineer.
  • (17:24) Sara founded Delta Analytics - a local Bay Area non-profit community of data scientists, engineers, and economists in 2014 that believes in using data for good.
  • (20:53) Sara shared Delta’s collaboration with Eneza Education to empower students to access quizzes by mobile texting in Kenya (check out her presentation at the ODSC West 2016).
  • (25:16) Sara shared Delta’s partnership with Rainforest Connection to identify illegal de-forestation using steamed audio from the rainforest (check out her presentation at MLconf Seattle 2017).
  • (28:22) Sara unpacked her blog post Why “data for good” lacks precision, in which she described 4 key criteria frequently used to qualify an initiative as “data for good” and discussed some open challenges associated with each.
  • (36:34) Sara unpacked her blog post Slow learning, in which she revealed her journey to get accepted into the AI Residency program at Google AI.
  • (41:03) Sara discussed her initial research interest on model interpretability for deep neural networks and her work done at Google called The (Un)reliability of Saliency Methods - which argues that saliency methods are not reliable enough to explain model prediction.
  • (45:55) Sara pushed the research above further with A Benchmark for Interpretability Methods in Deep Neural Networks, which proposes an empirical measure of the approximate accuracy of feature importance estimates in deep neural networks called RemOve And Retrain.
  • (48:46) Sara explained why model interpretability is not always required (check out her talks at PyBay 2018, REWORK Toronto 2018, and REWORK San Francisco 2019).
  • (52:10) Sara explained the typical measurements of model reliability and the limitations of them, such as localization methods and points of failure.
  • (59:04) Sara explained why model compression is an interesting research direction and her work The State of Sparsity in Deep Neural Networks - which highlights the need for large-scale benchmarks in the field of model compression.
  • (01:02:49) Sara discussed her paper Selective Brain Damage: Measuring the Disparate Impact of Model Pruning - which explores the impact of pruning techniques for neural networks trained for computer vision tasks. Check out the paper website!
  • (01:05:08) Sara shared her future research directions on efficient pruning, sparse network training, and local gradient updates.
  • (01:06:56) Sara explained the premise behind her talk Gradual Learning at the Future of Finance Summit in 2019, in which she shared the three fundamental approaches to machine learning impact.
  • (01:12:20) Sara described the AI community in Africa as well as the issues the community is currently facing: both from the investment landscape and the infrastructure ecosystem.
  • (01:18:00) Sara and her brother recently started a podcast called Underrated ML which pitches the underrated ideas in machine learning.
  • (01:20:15) Sara reflected how her background in economics influences her career outlook in machine learning.
  • (01:25:42) Sara reflected on the differences between applied ML and research ML, and shared her advice for people contemplating between these career paths.
  • (01:29:49) Closing segment.

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