AWS AI Blog

In the Research Spotlight: Alex Smola

by Victoria Kouyoumjian | on | Permalink | Comments |  Share

As AWS continues to support the Artificial Intelligence (AI) community with contributions to Apache MXNet and the release of Amazon Lex, Amazon Polly, and Amazon Rekognition managed services, we are also expanding our team of AI experts, who have one primary mission: To lower the barrier to AI for all AWS developers, making AI more accessible and easy to use. As Swami Sivasubramanian, VP of Machine Learning at AWS, succinctly stated, “We want to democratize AI.”

In our Research Spotlight series, I spend some time with these AI team members for in-depth conversations about their experiences and get a peek into what they’re working on at AWS.


This week, I sat down with Alex Smola. Alex joined AWS in August 2016 as the Director of Machine Learning and Deep Learning. Alex is recognized as one of the world’s top machine learning experts. He comes to AWS after helping grow the widely respected machine learning department at Carnegie Mellon, where he still holds a professorship. Alex is a prolific and widely cited author within the academic research community, writing or contributing to 462 titles with 75,000+ citations.

I asked Alex about the opportunities he sees when he looks at the current state of AI. Without hesitating, he said, “Lowering the barrier of entry to anyone who wants to do machine learning is one of the biggest opportunities out there. One of my main goals is to make machine learning accessible to a million+ developers. You can accomplish this either by training a million developers or by making your tools easier to use.”

For Alex, this means finding the right tools for different levels of customers. He cited Amazon Rekognition, a managed service that addresses computer vision challenges, as an example of this. He continued his thoughts about increasing accessibility: “Lowering the barrier to machine learning includes building tools for data scientists to enable easier experimentation, and providing documentation that is easy for developers to understand. It’s critical we make it easy to bring the power of high-performance computing to developers who want to use the latest deep learning tools.”

Alex has an initiative is to ensure that AWS is reaching out to developer communities. This includes involving them in the MXNet project, which is now part of the Apache Incubator program. He also has a robust and growing internship program for AWS machine learning, with over 30 interns. Alex ensures that AWS is hiring in key areas of machine learning, from computer vision to natural language processing, to systems, to core algorithms — basically, the entire range of competencies. Since joining AWS, Alex has hired many talented machine learning and deep learning researchers, including Animashree (“Anima”) Anandkumar, Mu Li, Edo Liberty, and Hassan Sawaf. Each of them will be profiled in future posts.

Alex is committed to helping AWS engage academia because it’s a two-way street: “Academic talent helps AWS excel. At the same time, we want to make sure we share ideas. We want to train interns and help further their careers. AWS has lots of infrastructure, bigger machines and, of course, lots of data, allowing interns to do things with AWS that would normally be challenging within their university settings.”

In addition to frequently speaking about Apache MXNet, Alex also talks about how to design efficient algorithms at scale. He notes that overarching principles beyond MXNet can be applied to a number of problems, deep learning being one of them. “Although deep learning and AI are exciting, machine learning is much broader. Machine learning is an excellent source of ideas, problems, and tools. If you are aware of them, you can use them in deep learning, too,” he explained. He believes that deep learning is not a new or different way of machine learning, but an interesting confluence. “Now we have enough compute resources and data, a number of optimization techniques, and systems tools, all of which let you go very quickly from specifying a model to actually solving the problem.”

Alex presented a simple introduction to deep learning and how to scale with Apache MXNet at SXSW in Austin, TX., and he travels frequently for speaking engagements at universities and recruiting events. Alex is a self-professed coffee fanatic (to relax, he tells me!) and enjoys piano, languages, and travel. You’ll find him zipping around the Palo Alto office on his Razor scooter, French press in hand.


Victoria Kouyoumjian is a Sr. Product Marketing Manager for the AWS AI portfolio of services which includes Amazon Lex, Amazon Polly, and Amazon Rekognition, as well the AWS marketing initiatives with Apache MXNet. She lives in Southern California on an avocado farm and can’t wait until AI can clone her.