Stratos Idreos is an associate professor of Computer Science at Harvard University where he leads the Data Systems Laboratory. His research focuses on making it easy and even automatic to design workload and hardware conscious data structures, algorithms, and data systems with applications on big data analytics and data science. Stratos was awarded the ACM SIGMOD Jim Gray Doctoral Dissertation award for his thesis on adaptive data systems indexing. He received the 2011 ERCIM Cor Baayen award as “most promising European young researcher in computer science and applied mathematics” from the European Research Council on Informatics and Mathematics. In 2015 he was awarded the IEEE TCDE Rising Star Award from the IEEE Technical Committee on Data Engineering for his work on adaptive data systems and in 2020 he received the ACM SIGMOD Contributions award. Stratos is also a recipient of the National Science Foundation Career award, and the Department of Energy Early Career award.
Artificial Intelligence: From ancient Greece to the next 2000 years
Artificial Intelligence is changing the world. AI systems utilize data to make decisions that are usually very complex, slow, and error-prone for humans. The potential to improve human life is unlimited: drastically improving health care, drug discovery, education, dramatically reducing car accidents, and so much more. However, AI today can only be utilized by a tiny number of large organizations. In addition, there are major ethical concerns and environmental impact implications with state-of-the-art AI technology that can have a catastrophic impact on society long-term.
In this talk, we will explain the primary bottlenecks that prevent the wide-spread adoption of AI. We will explain why AI is too complex, slow, and expensive to achieve and why this leads to ethical, environmental, and monopoly concerns. We will present our vision on how AI technology, education, and society need to evolve so that the premise of AI can be realized, is available to everyone, and can be utilized in an ethical way for all humans. We will discuss how data systems and big data processes need to evolve and present a path for a new yet ancient kind of AI that integrates automated learning with human knowledge.