Principles for AI in the Real World
Join us for a fireside chat with Martin Casado, General Partner at Andreessen Horowitz and Nick Feamster, Neubauer Professor of Computer Science at the University of Chicago to discuss what businesses, governments, and researchers need to know about operationalizing AI in the real world (and why it is different than traditional software). They will focus on the state of the field both in academia and industry and where they see opportunities for innovation.
Recent breakthroughs in artificial intelligence and big data have the potential to significantly advance science, healthcare, technology, and industry over the next decade. Despite the immense possibilities, many machine learning techniques present barriers to entry that make them difficult, if not prohibitive to implement, deploy, and maintain for all but the very largest cloud providers and content delivery networks.
Research groups, municipal governments, small businesses, and even Internet service providers report difficulty implementing and utilizing machine learning. Unlike traditional software, AI systems have unique properties that can become barriers when operationalizing a learning solution such as data acquisition and management, large-scale compute, storage costs, privacy, edge cases, and potential environmental impacts of training models. These constraints require new data architectures, machine learning algorithms, and fundamentally new business models to succeed. The discussion will also include practical advice for industry leaders, researchers, founders, and students to overcome or mitigate these challenges.
Part of the CDAC 2021 Data & Technology Outlook Series, in collaboration with the Data Discovery Summit
Many of the data science and computational tools that revolutionized the modern business landscape resulted from close collaborations between industry and academia. Tomorrow’s innovations are under construction today in R&D departments and university laboratories, where researchers develop solutions to the most pressing data challenges across fields.
This year, the Center for Data and Computing (CDAC) at the University of Chicago presents a series of critical conversations between industry leaders and researchers pushing the frontiers of data science forward, presenting and discussing new tools in artificial intelligence, data analysis and discovery, security and privacy, that will define the next decade of data technology in science and industry. The Outlook series will provide a balanced understanding of the trends reshaping business and tech with the goal of translating hype into realistic predictions.
Martin Casado is a general partner at the venture capital firm Andreessen Horowitz where he focuses on enterprise investing. He was previously the cofounder and chief technology officer at Nicira, which was acquired by VMware for $1.26 billion in 2012. While at VMware, Martin served as senior vice president and general manager of the Networking and Security Business Unit, which he scaled to a $600 million run-rate business by the time he left VMware in 2016.
Martin started his career at Lawrence Livermore National Laboratory where he worked on large-scale simulations for the Department of Defense before moving over to work with the intelligence community on networking and cybersecurity. These experiences inspired his work at Stanford where he created the software-defined networking (SDN) movement, leading to a new paradigm of network virtualization. While at Stanford he also cofounded Illuminics Systems, an IP analytics company, which was acquired by Quova Inc. in 2006.
For his work, Martin was awarded both the ACM Grace Murray Hopper award and the NEC C&C award, and he’s an inductee of the Lawrence Livermore Lab’s Entrepreneur’s Hall of Fame. He holds both a PhD and Masters degree in Computer Science from Stanford University.
Martin serves on the board of the following Andreessen Horowitz portfolio companies: ActionIQ, Astranis, DeepMap, Fishtown Analytics/dbt, Fivetran, Imply, Isovalent, Kong, Pindrop Security, Preset, RapidAPI, Tecton, and Yubico.
Nick Feamster is Neubauer Professor of Computer Science and Faculty Director of Research at the Data Science Institute. Feamster designs and deploys network protocols and systems that make the Internet work better, and uses empirical network measurement and machine learning to understand and improve network performance, security, and privacy