FEBRUARY 20178Dr. Viral B. Shah Co-Founder & CEO and Ranjan Anantharaman, Applications Engineer, Julia ComputingIN MY OPINION WHY LANGUAGE MATTERSArtificial Intelligence has risen to prominence in the past ten years across academia and industry. One of the most popular branches in Artificial Intelligence is Deep Learning. Deep neural networks have been around since the 1940s, but have only recently been deployed in research and analytics because of strides and improvements in computer technology and computational horsepower afforded by modern GPUs. Neural networks can now carry out tasks that were previously considered difficult for computers such as vision and speech processing. These are being combined into really interesting applications such as self driving cars, Amazon Echo, personal assistants, bots, and many others. Interesting and exciting as these applications are, businesses today are faced with a number of challenges:1. A major challenge in the new age of AI applications is safety and regulation. How do we ensure that the algorithms do what they are intended to do? How do we eliminate model risk from financial markets? How do we keep the self driving cars and our UAVs safe?2. While companies such as Google, Facebook, and Amazon can devote dozens of engineers to fine tune their deep learning models and extract the best performance from their programs, it is simply not easy for the rest of the world to do so. 3. These applications need to run on a wide variety of hardware such as clusters of GPUs for training petabytes of data. The trained models then may be deployed on the web or on smartphones or the tiniest of ARM systems.There are a number of ways to address these challenges. One popular approach has been to build sophisticated libraries for deep learning. Examples Julia is an open-source language for high-performance technical computing and data science created by some of the best minds in mathematical and statistical computing.Ranjan Anantharaman
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