ADS meets CIDR
For our first event of 2020, we joined forces with CIDR to host a meetup at the end of the conference. We heard talks from Turing Award winner Michael Stonebraker, Stanford associate professor Christopher Ré and University of Amsterdam professor Hinda Haned.
The Conference on Innovative Data Systems Research (CIDR) is a systems-oriented conference, emphasizing the systems architecture perspective. It is complementary in its mission to the mainstream database conferences like SIGMOD and VLDB. Taking advantage of the presence of prominent data systems researchers visiting CIDR, ADS hosted this special meetup at the Mövenpick Hotel in Amsterdam.
The first speaker was Michael Stonebraker. He is the 2014 Turing Award winner, adjunct professor of Computer Science at M.I.T, co-director of the Intel Science and Technology Center focused on big data, and CTO of Paradigm4 and Tamr. Michael’s talk was all about the top 10 Data Science blunders. These include the belief that traditional data integration techniques will solve previous issues, and that deep learning is the answer. Michael was particularly keen to remind the audience that “there is no such thing as clean data”. Check out Michael’s presentation.
Chris Ré, associate professor at Stanford and co-founder of companies including Lattice, now part of Apple, and SambaNova, was the second speaker. He talked about the theory and systems for weak supervision. If you want to build a high-quality machine learning product, build a large, high-quality training set. At first glance, this seems as useful as the statement “if you want to be rich, get a lot of money.” However, a key idea driving Chris’ work is that new theoretical and systems concepts including weak supervision, automatic data augmentation policies, and more, can enable engineers to build training sets more quickly and cost effectively. Check out Chris’ presentation.
The final speaker was Hinda Haned. Hinda is professor by special appointment of AI at the University of Amsterdam and lead data scientist at Ahold Delhaize. Hinda’s talk covered the challenges of bringing eXplainable AI into practice. Providing explanations about how a machine learning model produced a particular outcome can help enhance users’ trust and their willingness to adopt the model for high-stake applications. Check out Hinda’s presentation.
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