Artificial Intelligence • Data Science • Deep Learning
Michael Housman is the Co-Founder and Chief Data Science Officer at RapportBoost.AI, an emotional intelligence engine for conversational commerce that leverages machine learning and deep learning to help companies communicate more effectively with their customers. Prior to RapportBoost.AI, he was the Chief Analytics Officer at Evolv, Inc. (acquired by Cornerstone OnDemand, Inc.) where his team built a hiring platform capable of evaluating millions of job applications to identify candidates that would: (1) stay at the job longest; and (2) and perform best. He has published his work in a variety of peer-reviewed journals, presented his work at dozens of academic and practitioner-oriented conferences, and has had his research profiled by such media outlets as The New York Times, Wall Street Journal, The Economist, and The Atlantic. Dr. Housman received his A.M. and Ph.D. in Applied Economics and Managerial Science from The Wharton School of the University of Pennsylvania and his A.B. from Harvard University.
Recent advances in deep learning have fueled tremendous excitement about the potential for artificial intelligence to solve countless problems. But there are some perils and pitfalls endemic to these new techniques, particularly because they ignore two essential components of the scientific method: (1) understanding the how; and (2) explaining the why. Dr. Michael Housman offers up a two specific examples from his own career as a data scientist to show how a naive application of deep learning algorithms can lead data scientists to the wrong conclusion and offers up some guidance for avoiding these mistakes.
The recent explosion of artificial intelligence technologies offers the potential to disrupt entire industries and transform the way we live. But if the algorithms behind neural nets were developed decades ago, why are we starting to see their impact all of a sudden? In this session, Dr. Michael Housman offers up some insight into why the explosion in artificial intelligence is occurring now, what to expect in the coming decade, and how you can be best positioned to harness the power of artificial intelligence to anticipate exponential change to your industry.
A lot of the recent conversations about artificial intelligence seem to suggest that robots are poised to replace human beings outright across a variety of different roles. This human vs. robot dialog focuses more on how the two are competitive and less on how they might be synergistic. Is it an either / or? Or can the combination of human + machine produce better outcomes than either one alone? We answer this question by exploring some recent trends in machine learning along with a case study of how some retailers are using analytics platforms to communicate more effectively and build stronger relationships with their customers.
The recent explosion of artificial intelligence technologies is disrupting entire industries and will completely transform the way we live. But if the algorithms behind many of these advances were developed decades ago, why are we starting to see their impact all of a sudden? In this session, Dr. Michael Housman offers up some insight into why the explosion in artificial intelligence is occurring now, how you can best position your organization to harness the power of these advances, and what the future looks like for the fields of computer vision, natural language understanding and generation, and cognitive computing.
Advances in machine learning and artificial intelligence have the potential to completely transform virtually every industry. Given the exponential pace of these advances, there are a number of questions that naturally emerge: how can leverage these advances to improve business outcomes? Where do I even begin? With 15 years of experience helping organizations to transform through the application of intelligent design of artificial inteligence applications, Dr. Housman lays out a simple framework for identifying problems can be fixed with data science, offers up several examples from his own career of organizations that successfully made the leap, and then leads a working session to help participants “think about the black box” to identify immediate opportunities for data science to make an impact and walk them through the process of doing so.