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.
This talk by Joshua Malina, Senior Machine Learning Engineer at AMEX, focuses on how Pandas makes time series data investigation more accessible.
This blog post by Douglas Hamilton, Chief Data Scientist and Managing Director of NASDAQ’s Machine Intelligence Lab, explores applying machine learning to the portfolio management problem to derive better indexes.