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Speaker "Dr. Usama Fayyad" Details Back

 

Topic

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

Abstract

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.

Profile

Usama Fayyad is CEO of Open Insights in Silicon Valley which he reactivated after leaving Barclays in London. He is also Interim CTO for Stella.AI a Mountain View, CA VC-Funded startup in AI for recruitment. He is acting as Chief Operations & Technology Officer for MTN’s new division: MTN2.0 aiming to extend Africa’s largest telco into new revenue streams beyond Voice & Data. Until October 2016 he was Group Chief Data Officer at Barclays in London where his global responsibilities included the Data Strategy, Data Governance, performance and management of operational and analytical data systems, as well as delivering value by using BigData and analytics to create growth opportunities and cost savings for the bank. Usama is co-founder of OASIS-500, a tech startup investment fund, following his appointment as Founding Executive Chairman in 2010 by King Abdullah II of Jordan. He was also Chairman, Co-Founder and Chief Technology Officer of Blue Kangaroo Corp, a mobile search engine service for offers based in Silicon Valley 2011-2013. In 2008, Usama founded Open Insights, a US-based data strategy, technology and consulting firm that helps enterprises deploy data-driven solutions that effectively and dramatically grow revenue and competitive advantage. Prior to this, he served as Yahoo!'s Chief Data Officer and Executive Vice President where he was responsible for Yahoo!'s global data strategy, architecting its data policies and systems, and managing its data analytics and data processing infrastructure. The data teams he built at Yahoo! collected, managed, and processed over 25 terabytes of data per day, and drove a major part of ad targeting revenue and data insights businesses globally. He was the first person in the world to hold the CDO title. In 2003 Usama co-founded and led the DMX Group, a data mining and data strategy consulting and technology company specializing in Big Data Analytics for Fortune 500 clients. DMX Group was acquired by Yahoo! in 2004. In 2000 Usama left his leadership position at Microsoft to co-found and serve as Chairman and CEO Audience Science (digiMine). At Microsoft he spent 5 years leading the data mining and exploration group at Microsoft Research and also headed the data mining products group for Microsoft’s server division. From 1989 to 1996 Usama held a leadership role at NASA's Jet Propulsion Laboratory where his work garnered him the Lew Allen Award for Excellence in Research from Caltech, as well as a U.S. Government medal from NASA. Usama has published over 100 technical articles on data mining, Artificial Intelligence, machine learning, and databases. He holds over 30 patents, is a Fellow of the Association for Advancement of Artificial Intelligence and a Fellow of the Association of Computing Machinery. He has edited two influential books on data mining and served as editor-in-chief on two key industry journals. Usama earned his Ph.D. in engineering in AI/Machine Learning from the University of Michigan, Ann Arbor. He holds two BSE’s in Engineering, MSE Computer Engineering and M.Sc. in Mathematics. He is active in the academic community with several adjunct professor posts and is the only person to receive both the ACM’s SIGKDD Innovation Award (2007) and Service Award (2003). He serves on several private and company boards.