Speaker "Dr. Usama Fayyad" Details Back



The good, the hype, and the reality of making AI work in practice: Enterprise lessons from the front lines


Artificial Intelligence (AI) has been receiving a lot of hype as a magical solution for many difficult problems. Some have started to worry that AI would not only take over their jobs but also take over control of work and perhaps personal life. In this talk, I attempt to demystify most of these concerns by simply describing in reality what has worked and what has not. It turns out, most of AI has failed (with some very notable exceptions) and this has led to two AI winters in the early 1980’s and in the Iate 1990’s.  We are on the verge of a third AI winter. I present this in the context of my own version of a “Brief History of AI”.  This will help us to crystallize what has actually worked and why. It turns out – Machine learning (ML) is behind most of what has worked.  This has major implications as most of ML algorithms succeeded not because of great algorithm design, but primarily because a lot more data became available. This means that Data is critical to making ML work, and hence to making most of AI work.  We thus go into what the issues are in making data, especially BigData – which allows for the utilization of the majority of data in any organization (unstructured data) to be utilized properly.  Despite all the hype, with the right Data in place, ML/AI are making their way into all kinds of business operations today as more companies explore how they can put their data to work. Among the most common reasons to apply ML to business processes is the ability of the technology to perform certain functions at scale, which would otherwise require significant amounts of time and resources. It’s also common to rely on ML for processing large volumes of data analysis beyond what’s possible for human capability. Data is the fuel that drives a machine learning solution and machine learning is the key to make most AI algorithms practical.
We will include several case studies on how success was achieved and we highlight the right approach for creating an environment that enables successful machine learning and data science, especially leveraging unstructured data as a resource.


Usama is Chairman at Open Insights focusing on AI, BigData strategy, and new business models for Data. He is the Inaugural Executive Director of the Institute for Experiential AI at Northeastern University where he is also professor of computer science. He was Global Chief Data Officer at Barclays Bank in London (2013-2016) after launching a key tech startup accelerator in MENA (2010-2013) as Executive Chairman of Oasis500. He was Chairman/CEO/CTO at several Seattle/Silicon Valley tech startups and the first person to hold the title: Chief Data Officer when Yahoo! acquired his 2nd startup in 2004. He held leadership roles at Microsoft (1996-2000) and founded the Machine Learning Systems group at NASA's Jet Propulsion Laboratory (1989-1996) where he was awarded Caltech’s top Excellence in Research award & a U.S. Government medal from NASA. Usama published over 100 technical articles, holds over 30 patents, is a Fellow of both Association for Advancement of Artificial Intelligence and the Association of Computing Machinery. He is a recipient of both the ACM SIGKDD Awards for Innovation and for Service. He earned his Ph.D. from the University of Michigan and holds two BSE’s in Electrical and Computer Engineering, MSE Computer Engineering and M.Sc. in Mathematics.