Designing Media for Machine Learning

Apr 19, 2018 · New York, United States of America

Machine learning, data science, and AI are all disciplines that live in the interplay of the applied statistical sciences and computer engineering. When compared to the significant advances in both the science, hardware and computing over the last 75 years the fundamental ways in which we do this work has changed relatively little. To solve a statistical problem we spend most of our time being engineers, typing code into a terminal or similar environment, and dealing with the problems and abstractions of programmers. In this talk, we will evaluate the tools we use today, the utility of their inherent abstractions, and question how we envision the practice of statistical engineering in the years to come.

From the first punch-card machines to distributed cloud computing we’ll explore the evolution of the statistical engineering process, leaving aside specific machine learning implementations or optimizations. Taking Bret Victor’s “Human Representation of Thought” as a starting point we’ll focus on the UX of machine learning and imagine media which allows us to maximize creativity and “think the unthinkable.” We’ll look at prototypical examples, compare these to environments customized for other disciplines, and discuss how current engineering frameworks and API’s could be designed to create UX optimized media and environments.


Daniel Krasner is the Founder/CEO or Merriam Tech, a company devoted to improving human interaction with large volumes of information, the co-founder of KFit Solutions, a data science consulting firm, and the co-creator of the Python’s Rosetta, a library for high-performance NLP/text processing. He is also the Director of Data Science in eDiscovery at Paul Hastings, where he develops technology for litigation and related legal applications.

Daniel works in the intersection of machine learning, high performance statistical engineering, human-computer interaction and user experience. He is interested in how we interact with machines, how we build software and engineering solutions, and how this will evolve in the years to come.

Previously, Daniel was the technology lead of the Columbia University’s History Lab project, chief data scientist at Sailthru, a senior researcher at Johnson Research Labs, a lecturer at the London School of Economics and a professor at Columbia University statistics department. Prior to entering the tech sector, he was an assistant professor of mathematics at UCLA. Daniel holds a PhD in mathematics from Columbia University.

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