The Future of Computer Vision and Machine Learning is Tiny

Summit Track: 
Keynote

There are 150 billion embedded processors in the world — more than twenty for every person on earth — and this number grows by 20% each year.

Imagine a world in which these hundreds of billions of devices not only collect data, but transform that data into actionable insights — insights that in turn can improve the lives of billions of people.

To do this, we need machine learning, which has radically transformed our ability to extract meaningful information from noisy data. But conventional wisdom is that machine learning consumes a vast amount of processing performance and memory — which is why today you find it mainly in the cloud and in high-end embedded systems.

What if we could change that? What would it take to do that, and what would that world look like?

In this talk, Pete will share his unique perspective on the state of the art and future of low-power, low-cost machine learning. He will highlight some of the most advanced examples of current machine learning technology and applications, which give some intriguing hints about what the future holds. He will also explore the ability of convolutional neural networks to handle a surprisingly diverse array of tasks, ranging from image understanding to speech recognition to malware detection. Looking forward, Pete will share his vision for the opportunities being opened up by this transformative technology, examine the key challenges that remain to be overcome and present his call to action for developers to make this vision a reality.

Speaker(s):

Pete Warden

Staff Research Engineer, Google

Pete Warden is a leading thinker, innovator and entrepreneur in visual AI, software and big data.

In 2003, Pete created a set of 45 image processing filters that were able to detect features in video content at 30 fps on 2003-era laptops. Apple bought his technology and hired him to work on integrating it into Apple’s imaging-related products.

In 2011, Pete co-founded Jetpac, serving as CTO and building the company’s technical team. This venture capital-backed startup created a unique product that analyzed the pixel data of over 140 million photos from Instagram and turned them into in-depth guides for more than 5,000 cities around the world. Pete joined Google in 2014 when Google acquired Jetpac.

Pete is currently a Staff Research Engineer at Google, focused on enabling the deployment of machine learning in cost- and power-constrained systems. At Google, Pete leads the development of the TensorFlow Lite machine learning framework for mobile and embedded applications, including a recently released experimental version of TensorFlow Lite for microcontrollers, which can be used on low-cost chips with less than 100 Kbytes of memory.

Pete is a sought-after speaker and writes a popular blog at www.petewarden.com. He is the author of two O'Reilly books, “Public Data Sources” and the “Big Data Glossary”. He earned his B.S. in computer science from the University of Manchester.

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