Ben Lorica and Roger Chen assess the state of AI technologies and adoption in 2019.
Roger Chen is Co-Founder & CEO of Computable Labs, and he serves as Program Chair for the Artificial Intelligence Conference. Previously, he was a Principal at O'Reilly AlphaTech Ventures (OATV), where he invested in and worked with early-stage startups primarily in the realm of data, machine learning, and robotics. Roger has a deep and hands-on history with technology. Before startups and venture capital, he was an engineer at Oracle, EMC, and Vicor. He also developed novel nanoscale and quantum optics technology as a Ph.D. researcher at UC Berkeley. Roger holds a BS from Boston University and a Ph.D. from UC Berkeley, both in electrical engineering..
Ben Lorica and Roger Chen highlight recent trends in data, compute, and machine learning.
Ben Lorica and Roger Chen provide a glimpse into tools and trends poised to accelerate AI innovation.
Dave Patterson and other industry leaders discuss how MLPerf will define an entire suite of benchmarks to measure performance of software, hardware, and cloud systems.
Ben Lorica and Roger Chen discuss the state of reinforcement learning and automation.
Probabilistic computation holds too much promise for it to be stifled by playing zero sum games with data.
AI Conference chairs Ben Lorica and Roger Chen reveal the current AI trends they've observed in industry.
AI fighting extremism, intuitive physics, and schema networks.
Drawing with AI, Apple AI API, United Nations and AI for good, and smart oil and gas.
Sukiyaki in French style, brick-and-mortar conversion tracking, route-based pricing, and technological productivity.
AutoML, AI photo editing, AI product studio, and Apple and dark data.
Medical ImageNet, NVIDIA GTC, corporate responsibility in tech, online pricing
How Stitch Fix systematizes collaboration between stylists and AI software.
Caffe2, deep learning best practices, intelligent design and wizard hats
Creative deep neural networks, AI black box, robot food delivery, and brute force productivity.
Diogo Almeida examines the capabilities and challenges in deep learning.
Kenny Daniel on implementing neural networks in production.
Song Han on compression techniques and inference engines to optimize deep learning in production.
Turning physical resource management into a data and learning problem.
The next big technologies are defined by their emerging market value.