Amazon Mentors Help UMass Graduate Students Make Concrete Advances on Vital Machine Learning Problems

Earlier this month, Varun Sharma and Akshit Tyagi, two master’s students from the University of Massachusetts Amherst, began summer internships at Amazon, where, like many other scientists in training, they will be working on Alexa’s spoken-language-understanding systems.

But for Sharma and Tyagi, the internship is the culmination of a relationship that began last winter, when they enrolled in a course in UMass Amherst’s College of Information and Computer Sciences called Industry Mentorship Independent Study, taught by distinguished professor Andrew McCallum and managed by the college’s Center for Data Science.

Students in the class were divided into four- to five-person teams, each of which spent the entire spring semester working on a single project, with the guidance of industry mentors from a company with a strong artificial-intelligence research program. Sharma and Tyagi were part of a five-member team mentored by Rahul Gupta, a senior applied scientist, and Bill Campbell, an applied science manager, both of the Alexa Natural Understanding group based in Cambridge, MA.

The entire class met once a week for a two-hour session with McCallum, in which students reported their progress to each other and received feedback from McCallum, the course teaching assistant, and several other PhD-level volunteers. But each team also met separately with its mentors.

“We would talk weekly to brainstorm ideas and discuss current progress and also try and divide tasks among the team members,” Sharma says. “Plus, they have a ton of experience that we don’t have, so they would tell us about things to watch out for or help out with stuff that we were stuck on.”

“But the most beneficial thing, I’d say, would be the access,” Sharma adds. “You don’t have that in other classes. I never had one-on-one office hours that would go for an hour before.”

IMG_4756.jpeg._CB442923193_.jpg
Amazon mentors Bill Campbell and Rahul Gupta meet with students in the UMass Amherst College of Information and Computer Sciences' Industry Mentorship Independent Study. From left to right: Varun Sharma, Lynn Samson, Zihang Wang, Bill Campbell, Rahul Gupta, Nan Zhuang, and Akshit Tyagi

At the beginning of the semester, Gupta and Campbell presented the UMass students with a set of possible research topics that they had developed with other members of the Alexa Natural Understanding group. The students eventually chose “early exit” strategies for neural networks as their topic.

Most recent advances in artificial intelligence — including Alexa’s latest natural-language-understanding systems — are the result of neural networks, dense networks of simple information processors that collectively execute some computation. The more complex the computation, the larger the network tends to be. But larger networks are also slower, presenting challenges for real-time systems such as Alexa.

Typically, neural networks are arranged into layers, with data bubbling up through the layers until, finally, the output of the top layer represents the result of the computation. Early-exit strategies are techniques for “bailing out” when the outputs of lower layers already represent reliable computation results, reducing processing time. The key is making this determination on the fly, so that more-challenging inputs are still processed by the full network.

“There’s a need in devices and clouds and also in edge computing” — or decentralized computing schemes that push computational resources closer to the edge of the network — “to potentially split the computation or to reduce the load,” Campbell says. “That also has the advantage that you may get insight into what kind of features are being extracted by the system. If you early exit, you say, ‘Well, the neural net has pretty good features at this point already for this particular problem.’ So the motivation is computational but also a qualitative understanding of how things are making decisions and potentially splitting the computation between some edge device and the cloud.”

“This is of particular importance to our devices that are in offline mode,” Gupta adds. “We support a very limited set of functionalities offline. With this we can expand the set of functionalities, where more of those decisions can be made on the device. Even devices that require an Internet connection, if the Internet connection goes down, they can still maintain this model functionality.”

Sharma, Tyagi, and the other members of their UMass team — Nan Zhuang, Zihang Wang, and Lynn Samson — experimented with a neural net consisting of three stacked long short-term memory layers, or LSTMs. LSTMs process ordered inputs in sequence, so that the output corresponding to any given input factors in both the inputs and outputs that preceded it. This is a useful property in natural-language processing, where word order is a valuable source of information.

Neural networks are typically trained on labeled data, and during training, their goal is to minimize “loss”, or the difference between the labels they apply to the data and the true labels. Usually, the loss function applies only to the output of the network’s last layer.

In their experiments, the UMass students instead correlated labels with the outputs of each of the network’s three layers, and the loss function factored in all three layers’ outputs. In fact, the loss function assigned greater weight to the outputs of the networks’ lower layers, essentially forcing them to produce labels that were as accurate as possible.

The outputs of neural networks are also probabilistic. Suppose, for instance, that a request to the Alexa music service is classified according to one of a dozen “intents”, such as playing music, playing a radio station, creating a new station, getting details about music, or the like. Then the output of the intent classification network would indicate the probability that the request belonged to each of those classes.

At each layer of their network, the UMass students used those probabilities as a confidence measure, to determine whether or not to exit early. Where previous early-exit strategies had used a threshold confidence score as a hard cutoff, the UMass system instead uses entropy, an information measure that considers not only the likelihood of the most probable classification but also the relative probabilities of all the others.

Sharma, Tyagi, and their teammates found that with their LSTM network, the number of operations the system had to perform (floating-point operations, or FLOPs) was roughly proportional to the number of network layers that processed an input: 23,084 FLOPs with exit after one layer, 46,143 with exit after two, and 69,202 with exit after three. A reference model without early exit required 69,192 FLOPs on the same input, so the additional machinery for early exit added very little overhead.

Moreover, the early-exit model was actually, on average, more accurate than the reference model, despite reducing computation time significantly. The researchers suspect that that’s because forcing the network’s early layers to produce more-accurate representations “regularized” the network, or ensured that computations were evenly distributed across it. This prevents overfitting, or tailoring the network’s computations too narrowly to the training data.

Results like these mean that the UMass students’ project was no mere academic exercise. “Programs like the UMass Amherst Center for Data Science mentorship class not only strengthen our ties to the academic community and help us identify promising young researchers, but they also help us make real progress on projects that will help Alexa become smarter and more trustworthy,” Gupta says.

About the Author
Larry Hardesty is a science writer at Amazon. Previously, he was managing editor of the Boston Book Review, a senior editor at MIT Technology Review, and the computer science writer at the MIT News Office.

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At Amazon, we are committed to being the Earth’s most customer-centric company. The International Technology group (InTech) owns the enhancement and delivery of Amazon’s cutting-edge engineering to all the varied customers and cultures of the world. We do this through a combination of partnerships with other Amazon technical teams and our own innovative new projects.You will be joining the EU Lifecycle team is to evolve the onsite experience by understanding and serving the needs of customers at each stage of their relationship with Amazon. You will contribute to doing this by helping us identify the right promotions to show a customer given their interests and intent on Amazon using challenging machine learning and data analysis solutions. You will be exposed to cutting edge big data and machine learning technologies and you'll be part of a key effort to improve our customers experience.We are looking for a passionate, talented, and inventive Scientist with a strong machine learning background to help build industry-leading machine learning solutions. As the successful applicant for this role, you will with work closely with your business partners to identify opportunities for innovation. You will apply machine learning solutions to automate manual processes, to scale existing systems, to name just a few. You will own the definition, training and validation of the machine learning models, and you will work closely with the software engineering team to deploy and operate these models at scale.Your work will improve the experience of millions of daily customers using Amazon in Europe and in other regions. You will have the chance to have great customer impact and continue growing in one of the most innovative companies in the world. You will learn a huge amount - and have a lot of fun - in the process!
US, WA, Seattle
The Director of Economics, WW Consumer Finance will lead a team of Data Scientists, Economists and Software Engineers responsible for reporting and attribution on changes to the Worldwide Consumer business due to both Macro (external) and Micro (internal) factors.The existing team focuses on Macro factors and delivers business insights by building automated tools and algorithms to understand the impact of trends and shocks (e.g. Brexit, Hurricanes) on Amazon customer behavior. The team’s mission is to provide Amazon’s Consumer Shopping business with tools and analysis to measure, test, and predict the impact of external events and trends on our customers’ interaction with the Consumer business. The team owns software systems that provide tools for scaling data science and econometric insights to Financial Analysts, provides advanced analysis education, and manages external data that benefits the WW Consumer organization.In addition to leading the existing team, the Director will build out a Micro attribution capability. The Micro function will add the tools and skill sets necessary to quickly report on causal attribution for internal Amazon changes. The Micro team will have the audit capability to track how projected downstream program benefits are actually realized and adjust future projections accordingly. The Micro function will rely on data science capability and tooling to be able to accommodate the more granular Micro data and rapidly changing internal business drivers.The Director will also provide thought leadership and career development coaching to other Data Scientists and Economists embedded on other teams throughout the WW Consumer Finance organization. The Director and his/her team will be a central science resource, and will provide consulting and governance across the WW Consumer Finance organization.We are looking for a seasoned economist and leader who is able to lead a multi-disciplinary team of experts into success through innovation and cross-functional collaborations, with the ability to translate science and data into practical advice that the business will use to make decisions. In this role you’ll need to be a strategic thought partner who brings people together across functions, a persuasive executive coach, and an organized and inspiring team leader.
US, NY, New York
Amazon is investing heavily in building a world class advertising business and we are responsible for defining and delivering a collection of self-service performance advertising products that drive discovery and sales. Our products are strategically important to our Retail and Marketplace businesses driving long term growth. We deliver billions of ad impressions and millions of clicks daily and are breaking fresh ground to create world-class products. We are highly motivated, collaborative and fun-loving with an entrepreneurial spirit and bias for action. With a broad mandate to experiment and innovate, we are growing at an unprecedented rate with a seemingly endless range of new opportunities.The Sponsored Brands Recommendations team is a versatile environment, with a wide variety of challenges. We focus on helping advertisers by providing recommendations to help them achieve their goals. We look at mega size data from both retail and advertising space, and coming up with ML based recommendations through multiple products.As a Data Scientist, you will solve real world problems by analyzing large amounts of business data, diving deep to identify business insights and opportunities, designing simulations and experiments, developing statistical and ML models by tailoring to business needs, and collaborating with scientists, engineers, BIE's, product managers. The successful candidate should have a strong quantitative background and excellent data analytics / math skills.Amazon is an Equal Opportunity-Affirmative Action Employer – Minority / Female / Disability / Veteran / Gender Identity / Sexual Orientation#sspa
US, CA, Sunnyvale
Amazon Lab126 is an inventive research and development company that designs and engineers high-profile consumer electronics. Lab126 began in 2004 as a subsidiary of Amazon.com, Inc., originally creating the best-selling Kindle family of products. Since then, we have produced groundbreaking devices like Fire tablets, Fire TV, and Amazon Echo. What will you help us create?The Role:“If you do not work on an important problem, it's unlikely you'll do important work.” – Richard HammingWe have important problems to solve. There are great, world-changing products that should exist, but do not, because the technology to enable them does not exist. Yet. That’s where you come in.We are a smart team of doers that work passionately to apply cutting-edge advances in and to solve real-world challenges that will transform our customers’ experiences in ways we can’t even imagine yet.Key responsibilities will be to conduct research and development in algorithms and to collaborate with cross-functional engineering teams, including Amazon Robotics, to put the concepts you develop into production. You will determine where commercially available solution and academic research can be applied to solve Amazon business problems, as well as identify opportunities for innovation. You will use a large amount of data to train and test algorithms to bring them up to production level quality.If this describes you, come join our team at Lab126 in the heart of Silicon Valley. An Applied Scientist on this team will translate business and functional requirements into working code. Comfort with a high degree of ambiguity and ability to solve problems that haven’t been solved to scale before are essential. The role includes the following:· Research, design, implement and evaluate novel algorithms· Work on large-scale datasets, focusing on creating robust, scalable and accurate systems in versatile application fields· Collaborate closely with team members on developing systems from prototyping to production level· Collaborate with teams spread all over the world· Work closely with engineering teams to drive scalable, real-time implementations· Track general business activity and provide clear, compelling management reports on a regular basis