Microsoft Research features a talk by Wei Wen on Efficient and Scalable Deep Learning (slides)

In deep learning, researchers keep gaining higher performance by using larger models. However, there are two obstacles blocking the community to build larger models: (1) training larger models is more time-consuming, which slows down model design exploration, and (2) inference of larger models is also slow, which disables their deployment to computation constrained applications. In this talk, I will introduce some of our efforts to remove those obstacles. On the training side, we propose TernGrad to reduce communication bottleneck to scale up distributed deep learning; on the inference side, we propose structurally sparse neural networks to remove redundant neural components for faster inference. At the end, I will very briefly introduce (1) my recent efforts to accelerate AutoML, and (2) future work to utilize my research to overcome scaling issues in Natural Language Processing.

See more on this talk at Microsoft Research:
https://www.microsoft.com/en-us/research/video/efficient-and-scalable-deep-learning/

Microsoft Research posted this video about Project Silica, a research project that was highlighted earlier this week at Ignite 2019.

Data that needs to be stored long-term is growing exponentially. Existing storage technologies have a limited lifetime, and regular data migration is needed, resulting in high cost. Project Silica designs a long-term storage system specifically for the cloud, using quartz glass.

Read the blog at https://aka.ms/AA6faho
Learn more about the project at https://www.microsoft.com/en-us/research/video/project-silica-storing-data-in-glass/

Microsoft Research’s podcast interviews Jenny Sabin, an architectural designer, a professor, a studio principal and MSR’s current Artist in Residence.

On today’s podcast, Jenny and Asta talk about life at the intersection of art and science; tell us why the Artist in Residence program pushes the boundaries of technology in unexpected ways; and reveal their vision of the future of bio-inspired, human-centered, AI-infused architecture.

Microsoft Research  interviews Mark Hamilton to see how MMLSpark is helping to serve business and the environment.

If someone asked you what snow leopards and Vincent Van Gogh have in common, you might think it was the beginning of a joke. It’s not, but if it were, Mark Hamilton, a software engineer in Microsoft’s Cognitive Services group, budding PhD student and frequent Microsoft Research collaborator, would tell you the punchline is machine learning. More specifically, Microsoft Machine Learning for Apache Spark (MMLSpark for short), a powerful yet elastic open source machine learning library that’s finding its way beyond business and into “AI for Good” applications such as the environment and the arts.

Today, Mark talks about his love of mathematics and his desire to solve big, crazy, core knowledge sized problems; tells us all about MMLSpark and how it’s being used by organizations like the Snow Leopard Trust and the Metropolitan Museum of Art; and reveals how the persuasive advice of a really smart big sister helped launch an exciting career in AI research and development.

Microsoft Research has just posted a talk by Kevin Buzzard of the Imperial College of London. Don’t worry, no advanced mathematical knowledge is assumed in the talk.

Slides: https://www.microsoft.com/en-us/research/uploads/prod/2019/09/The-Future-of-Mathematics-SLIDES.pdf 

From the video description:

As a professor of pure mathematics, my job involves teaching, research, and outreach.

Two years ago I got interested in formal methods, and I learned how to use the Lean theorem prover developed at MSR. Since then I have become absolutely convinced that tools like Lean will play a role in the future of mathematics.

Microsoft Research explores how the brains beget the mind.

How do molecules, cells, and synapses effect reasoning, intelligence, language, science? Despite dazzling progress in experimental neuroscience we do not seem to be making progress in the overarching question — the gap is huge and a completely new approach seems to be required.

As Richard Axel recently put it: “We don’t have a logic for the transformation of neural activity into thought.” What kind of formal system would qualify as this “logic”? I will sketch a possible answer.

(Joint work with Santosh Vempala, Dan Mitropolsky, Mike Collins, Wolfgang Maass, and Larry Abbott.)

Talk slides: https://www.microsoft.com/en-us/research/uploads/prod/2019/09/A-Calculus-for-Brain-Computation-SLIDES.pdf

Here’s an interesting talk by Aaditya Ramdas on “Sequential Estimation of Quantiles with Applications to A/B-testing and Best-arm Identification”

From the description:

Consider the problem of sequentially estimating quantiles of any distribution over a complete, fully-ordered set, based on a stream of i.i.d. observations. We propose new, theoretically sound and practically tight confidence sequences for quantiles, that is, sequences of confidence intervals which are valid uniformly over time. We give two methods for tracking a fixed quantile and two methods for tracking all quantiles simultaneously. Specifically, we provide explicit expressions with small constants for intervals whose widths shrink at the fastest possible rate, as determined by the law of the iterated logarithm (LIL).

In this interview on the Microsoft Research Podcast, Dr. Brian Broll gives us an overview of the work he did and the experience he had as a Microsoft AI Resident, talks about his passion for making complex concepts easier and more accessible to novices and young learners, and tells us how growing up on a dairy farm in rural Minnesota helped prepare him for a life in computer science solving some of the toughest problems in AI.

Here’s an interesting talk by Eunice Jun on a language she’s working on.

[Slides]

From the video description:

Current statistical tools place the burden of valid, reproducible statistical analyses on the user. Users must have deep knowledge of statistics to not only identify their research questions, hypotheses, and domain assumptions but also select valid statistical tests for their hypotheses. As quantitative data become increasingly available in all disciplines, data analysis will continue to become a common task for people who may not have statistical expertise. Tea, a high-level declarative language for automating statistical test selection and execution, abstracts the details of analyses from users, empowering them to perform valid analyses by expressing their goals and domain knowledge. In this talk, I will discuss the design and implementation of Tea, lessons learned through the process, and other ongoing work in this vein.