January 2017 - Data Science DC:   Deep Learning Past, Present, and Near Future w/ Dr. John Kaufhold

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.



Deep Learning Analytics Senior Data Scientist, Aaron Schumacher, wrote a first principles introduction to TensorFlow titled “Hello, TensorFlow” for OReilly Learning.



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