Past Meetup

Deep Learning Past, Present, and Near Future

This Meetup is past

632 people went

Location image of event venue

Details

In the past 5 years, deep learning has become one of the hottest topics in the intersection of data science, society, and business. For our first Data Science DC Meetup of 2017, we are excited to take a deep dive into deep learning with Dr. John Kaufhold. Dr. Kaufhold will walk us through the past, present, and near future of this exciting field. Dr. Kaufhold is a data scientist and managing partner of Deep Learning Analytics (http://www.deeplearninganalytics.com/), as well as Secretary of the Washington Academy of Sciences (http://www.washacadsci.org/organization/officers/) and a regular contributor to the DC Data Community (http://www.datacommunitydc.org/) (DC2) (http://www.datacommunitydc.org/sponsorship/data-science-dc-sponsorship).

----------------------------

Agenda:

• 6:30pm -- Networking, Empanadas, and Refreshments

• 7:00pm -- Introduction, Announcements

• 7:15pm -- Presentation and Discussion

• 8:30pm -- Data Drinks (Tonic , 2036 G St NW)

----------------------------

Abstract:

In the past 5 years, deep learning has become one of the hottest topics in the intersection of data science, society, and business. Google, Facebook, Baidu and other companies have embraced the technology and in domain after domain, deep learning is outperforming both people and competing algorithms at practical tasks. ImageNet Hit@5 object recognition error rates have fallen >88% since 2011 and now can recognize 1,000 different objects in photos faster and better than you can. All major speech recognition engines (Google’s, Baidu’s, Siri, etc.) now use deep learning. In real time, deep learning can automatically translate a speaker’s voice in one language to the same voice speaking another language. Deep learning can now beat you at Atari and Go. These breakthroughs are visible as both product offerings as well as competitive results on international open benchmarks. This recent disruptive history of deep learning has lead to a student and startup stampede to master key elements of the technology—and this landscape is evolving rapidly. The abundance of open data, Moore’s law, Koomey’s law, Dennard scaling, an open culture of innovation, a number of key algorithmic breakthroughs in deep learning, and a unique investment at the intersection of hardware and software have all converged as factors contributing to deep learning’s recent disruptive successes. And continued miniaturization in the direction of internet-connected devices in the form of the “Internet of Things” promises to flood sensor data across new problem domains to an already large, innovative, furiously active, and well resourced community of practice.

While the machine learning community has experienced “AI winters” in the past due to unsubstantiated and unattainable forecasts of near term capabilities, there does appear to be something qualitatively different about this recent disruptive wave of deep learning. Extrapolating from recent revenue generation in deep learning, Trifacta estimates a worldwide revenue impact of >$500B through 2025, and VC funding of AI startups now constitutes approximately 5% of VC funding worldwide. While public and private enterprises have both benefited from a rapid ramp up in their investments in deep learning, the labor market demand for deep learning continues to outpace supply. But with disruptive AI technologies come apprehension—we now enjoy deep learning benefits like Siri every day, but privacy concerns, economic dislocation, anxieties about self driving cars, and military drones all loom on the horizon and our legal system has struggled to keep pace with technology. This recent history has caught the attention of both investors and futurists alike, alarming society because no one knows exactly which of the possible futures AI will realize—at its most mundane, deep learning begets another AI winter, at its most beneficent, a peaceful worldwide renaissance, or at its darkest, a dystopian big-brotheresque cyborg-controlled police state.

----------------------------

Bio: Dr. John Kaufhold

Dr. Kaufhold is a data scientist and managing partner of Deep Learning Analytics (http://www.deeplearninganalytics.com/), a data science company named one of the four fastest growing companies in Arlington, Virginia in 2015 (http://www.arlingtoneconomicdevelopment.com/resources/blog/meet-the-winners-of-arlington-s-fast-four-competition/), and again in 2016 (https://twitter.com/AEDBizInvest/status/806652592973553664). Dr. Kaufhold also serves as Secretary of the Washington Academy of Sciences (http://www.washacadsci.org/organization/officers/) and is a regular contributor to the DC Data Community (http://www.datacommunitydc.org/), where he moderates the DC2 Deep Learning Discussion list (http://www.datacommunitydc.org/blog/2014/07/deep-learning-discussion-list). Prior to founding Deep Learning Analytics, Dr. Kaufhold investigated deep learning algorithms as a staff scientist at NIH. Prior to NIH, Dr. Kaufhold was the youngest member of the Technical Fellow Council at SAIC. Over 7 years at SAIC, Dr. Kaufhold served as principal investigator or technical lead on a number of large government contracts funded by NIH, DARPA and IARPA, among others, taking a sabbatical at MIT to study deep learning in 2010. Prior to joining SAIC, Dr. Kaufhold investigated machine learning algorithms for medical image analysis and image and video processing at GE's Global Research Center. On a Whitaker fellowship, Dr. Kaufhold earned his Ph.D. from Boston University's biomedical engineering department in 2001. Dr. Kaufhold is named inventor on >10 issued patents in image analysis, and author/coauthor on >40 publications in the fields of machine learning, image understanding and neuroscience.

----------------------------

Sponsors:

This event is sponsored by the George Washington Business School MS in Business Analytics Program (http://business.gwu.edu/programs/specialized-masters/m-s-in-business-analytics/academic-program/), Statistics.com (http://bit.ly/12YljkP), Elder Research (http://datamininglab.com/), Novetta (https://www.novetta.com/), Booz Allen Hamilton (https://www.boozallen.com/consulting/strategic-innovation/nextgen-analytics-data-science), and AOL (http://engineering.aol.com/). (Would your organization like to sponsor too? Please get in touch! (http://www.datacommunitydc.org/sponsorship/data-science-dc-sponsorship))