Note: If you are attending workshops or participating in competitions, make sure to bring your laptop!
Tech Talk Descriptions
Big Data in the Supply Chain and Manufacturing : In this talk, Anup Rao will introduce how Cisco uses big data platforms and data science in the area of Supply Chain Management and Manufacturing. Specifically use cases and examples in the area of product costing and forecasting, backlog management, product testing and digital thread will be discussed.
Data Science at a Startup : Lumo Bodytech is a motion science company that combines sensor data with advanced algorithms to optimize human movement for better health, performance, and injury prevention. As a startup, overcame many challenges around building their data infrastructure. In this talk, they will share their experiences with data engineering and which unique analytics and machine learning projects have had an impact on their product and business.
Competitive Machine Learning as a way to improve your chances to get a Data Science job: The specifics of Competitive Machine Learning will be discussed and compared to the Machine Learning in industry and academia. Skills obtained through Competitive Machine Learning may benefit your academic performance and / or help you find a good Data Science job. The examples will include several machine competitions hosted at Kaggle.com.
River City Bank
Business of Banking and Applications of Data Science
Machine Intelligence Research Institute (MIRI)
Existential AI Risk : How should algorithms reason about algorithms? In this talk, Andrew Critch will present a new algorithm which provably reasons well about other algorithms and mathematical questions more generally, using Brouwer's fixed point theorem and a particular stock-market-like mechanism. This work is part of a growing research effort to prepare for the existence of highly intelligent AI systems in the future, and there remain many open technical problems whose solutions could help ensure the co-existence of human beings with increasingly automated economies. These range from developing learning algorithms that are more amenable to human feedback and context changes, to bargaining mechanisms for ensuring peaceful joint ownership of powerful AI systems, to further theoretical results on how algorithms should safely reason about other algorithms.
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