Last week, the Center for Data Science welcomed Kx Systems—a software company based in New York—into our office, where they gave two consecutive day-long workshops on their q programming language, and their kdb+ database. Kx Systems’ kdb+ database was designed for time-series analysis, so it’s a tool that is most useful for either collecting real-time data, or working … Continue reading Kx Systems comes to CDS
As data collection and data analysis have become entrenched in our daily lives, the conversations surrounding the ethical treatment and usage of data are becoming increasingly important. Almost every interaction you have—talking with a friend on social media, information you give to your doctor, or a purchase you make online—can be translated into and analyzed … Continue reading Ethics in Data Collection
The financial markets—the stock exchange, bonds, commodities—are a historically high-risk, high-reward avenue towards making, or losing, money. Besides working as a television weatherman, investment banking is one of the few fields where you only have to be correct sixty percent of the time to be considered a smashing success. Investment decisions are generally gauged against the S&P … Continue reading Money, Machines, and Markets with Vasant Dhar
Sam Bowman is one of the leading researchers in the field of natural language processing (NLP), and recently joined NYU as an Assistant Professor in Computational Linguistics, a joint position between NYU’s Linguistics department, and the Center for Data Science. This fall, he will be teaching a course titled “Seminar in Semantics: Artificial Neural Networks.” … Continue reading Faculty Interview: Sam Bowman
One of the most important conversations in the field of machine learning is the debate surrounding the use of predictive methods to influence or inform human decisions. Broadly speaking, the field of machine learning is the practice of programming computers to be more self-sufficient, and to create systems that can operate on their own, with … Continue reading Human Decision Making in Machine Learning Processes
One of the struggles in the field of data science is striking the necessary balance between human decision making, and automated computer processing. The field of machine learning—which looks to create computing systems that can solve problems on their own—is a perfect example of where human intelligence and mechanical computing power must go hand in … Continue reading The “Unknown Unknowns” of Machine Learning
