My pandemic media diet has been mostly newsletters and podcasts. I start my day with The New York Times and Morning Brew, and I end it questioning whether the CIA wrote one of the great post-Cold War rock ballads while cooking dinner.
Data Science Salon posted a message on Monday expressing our support for our community and our commitment to equality, respect, and human rights. We are participating in #BlackoutTuesday by not posting at all. For the remainder of the week, our social media content will be focused on sharing.
Artificial intelligence in media, entertainment, and advertising promises euphoric convenience: perfect satisfaction for consumers who reveal their personal information. There’s a connection between that exchange and a famous philosophical thought experiment from the 1970s.
Presented by Fatih Akici – Manager, Risk Analytics and Data Science at Populus Financial Group during Data Science Salon Austin, you can watch the full talk here. As intelligent systems deepen their footprints in our daily lives, algorithmic bias becomes a more prominent problem in today’s world..
Based on a presentation by Priscilla Boyd – Senior Manager, Data Analytics at Siemens Mobility, watch the full presentation here.
Based on a presentation by Chris Lindner – Manager, Product Science at Indeed, watch the full presentation here.
Based on a presentation by Caitlin Hudon – Lead Data Scientist at OnlineMedEd, watch the full presentation here. At #DSSATX (overall event recap here), we loved the super-practical step-by-step advice on setting up the very first data science infrastructure and team in an organization! Caitlin.
AutoML is a term that appears increasingly in tech industry articles and vendor product claims, and is also a hot topic within AI research in academia. Consider how nearly all of the public cloud vendors promote some form of AutoML service. The tech “unicorns” are developing AutoML services for.
Across the breadth of topics covered by speakers over two (rainy) days at Data Science Salon in Austin, February 18 & 19, 2020, two major themes emerged: the maturation of the data lifecycle, and the intersection of humans and machines.
Consider how the software development life cycle (SLDC) is well-defined at this point: planning, creating, testing, deploying, maintaining – or some variant, depending on your software methodology. The gist remains consistent. Computer software runs “logic” in hardware, the test suites are.