Data Science newsletter – August 16, 2019

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Businesses across the world are hiring data scientists to beef up their efficiency and competitiveness via artificial intelligence (AI). Startup companies (dubbed AI-First companies) are disrupting traditional industries like banking, insurance, real estate and healthcare using AI technologies.

The demand for data scientists far exceeds supply. And, the problem is exacerbated by the fact that the data scientist profession is itself splitting into multiple sub-disciplines.

Fluid Imaging Technologies, Scarborough, ME (www.fluidimaging.com), and the University of Colorado Boulder have entered into an exclusive agreement to conduct primary laboratory research aimed at determining whether the University’s proprietary artificial intelligence software can detect bloodborne bacteria and identify the species from images collected using the manufacturer’s patented FlowCam® Nano particle imaging and analysis system. In the study, entitled “Application of Convolutional Neural Networks and Flow Imaging Microscopy to Analysis of Blood Infections”, researchers are to evaluate the 10 strains of bacteria most responsible for the 1.5 million sepsis cases and 250,000 fatalities annually in the United States per CDC data.

DeepMind’s AI has beaten chess grandmasters and Go champions. But founder and CEO Demis Hassabis now has his sights set on bigger, real-world problems that could change lives. First up: protein folding

For 50 years now, Intel Corp. has had Advanced Micro Devices Inc. to kick around. AMD has seen good eras, but it has remained an afterthought in the semiconductor business, and less than a decade ago seemed so adrift that analysts predicted it would be acquired or simply go out of business. Today, things are starting to look different.

In its five years under Chief Executive Officer Lisa Su, AMD has scraped its way back to relevance. It has stabilized and improved finances, spent the money needed to develop chips that can outmatch Intel’s, and sold them to major clients who might have laughed it out of the building a few years ago. Those heavyweights include the cloud arms of Amazon.com, Microsoft, and (as of Aug. 8) Google, the trifecta of cloud computing. The big cloud providers are especially desperate for an alternative to Intel’s pricey server chips. “We’re really excited to have AMD as an option for our customers,” says Matt Garman, who heads infrastructure for Amazon’s cloud business. “For a long time, they weren’t.”

Twitter will begin allowing users to follow interests, the company said today, letting users see tweets about topics of their choosing inside the timeline. When the feature goes live, you’ll be able to follow topics including sports teams, celebrities, and television shows, with a selection of tweets about them inserted alongside tweets in your home feed.

Topics will be curated by Twitter, with individual tweets being identified through machine learning rather than editorial curation, the company said. For now, only sports-related interests can be followed, said Rob Bishop, a Twitter product manager. The feature is now being tested on Android.

As a Microsoft technical fellow, Jennifer Tour Chayes has made a name for herself as an expert in the field of network science and a leader of multidisciplinary labs that bring data science tools to bear on a wide range of problems. In January, she will leave her current position at Microsoft to become UC Berkeley’s first associate provost for the Division of Data Science and Information and Dean of the School of Information.

Berkeley News spoke with Chayes about her research, her passion for encouraging people from underrepresented groups to pursue careers in STEM fields and her hopes for the new Division of Data Science and Information at Berkeley.

Amazon has rolled out a new selling format for brands in its third-party marketplace that lets sellers submit products to be priced by Amazon’s algorithm.

To participate in the program, called Sold by Amazon, sellers have to already be enrolled in Fulfilled by Amazon and have Professional Selling accounts in Amazon’s Brand Registry, a trademark registration system that Amazon pushed sellers to earlier this year. According to the SBA overview on the Seller Central portal, the program “provides a new, hands off the wheel selling experience for FBA listings that is designed to help Sellers grow their business.” Amazon began Sold by Amazon at the beginning of August on an invite-only basis to select sellers, and has yet to open up brand enrollment to all eligible sellers as the program starts out in testing mode.

According to Amazon, SBA doesn’t cost anything additional to FBA, which charges sellers a fee to store and ship items from Amazon’s warehouses with Prime Shipping. With SBA, Amazon also exerts control over the product’s sale price, by dynamically pricing products to make sure Amazon’s prices are lowest.

Twelve years ago the Department of Energy (DOE) was just beginning to explore what an exascale computing program might look like and what it might accomplish. Today, DOE is repeating that process for AI, once again starting with science community town halls to gather input and stimulate conversation. The town hall program is being led by a trio of distinguished DOE scientists – Rick Stevens (Argonne National Laboratory), Kathy Yelick (Lawrence Berkeley National Laboratory, and Jeff Nichols (Oak Ridge National Laboratory). HPCwire’s coverage of the first AI Town Hall, held at ANL, can be found here.

With the Exascale Initiative even its name gives a hint to its goal. Scale. The expectation was and is to boost computing power such that important problems currently out of reach of petascale computing can be tackled.

Our understanding of marine microbes, responsible for half the planet’s photosynthesis, is spotty at best. The Simons Collaboration on Computational Biogeochemical Modeling of Marine Ecosystems (CBIOMES) plans to change that.

BASF SE (Ludwigshafen, Germany; www.basf.com) and Technische Universität Berlin (TU Berlin; www.tu-berlin.de) have signed an agreement to cooperate closely in the area of machine learning. The aim of the collaboration, Berlin-based Joint Lab for Machine Learning (BASLEARN), is to develop workable new mathematical models and algorithms for fundamental questions relating to chemistry, for example, from process or quantum chemistry. Both partners are jointly committed to this aim in the coming years. As essential part of the cooperation, BASF supports the research work of Prof. Dr. Klaus Robert Müller, professor for machine learning and spokesperson of the “Berlin Center for Machine Learning” at the TU Berlin, with a total of over €2.5 million over the coming five years.

“Single-molecule fluorescence techniques have revolutionized our understanding of the dynamics of many critical molecular processes, but signals are inherently noisy and experiments require long acquisition times,” explained Marcia Levitus, an associate professor in the School of Molecular Sciences and the Biodesign Institute at Arizona State University.

FCS takes too long, and the chemistry we care about learning might already be done by the time we try to observe it. Furthermore, exposing samples to the laser for long periods of time may result in the photochemical damage of molecules under study, preventing the widespread use of FCS in biological research.

A paper published in Nature Communications by ASU Associate Professor Steve Presse and collaborators now addresses these issues using tools from data science and, more specifically, Bayesian nonparametrics – a type of statistical modeling tool so far largely used outside the natural sciences.

The National Science Foundation awarded a total of $3.8 million to Dartmouth College and the University of New Hampshire for programs related to science, technology, engineering and math education.

Dartmouth will receive $2.8 million to develop teaching materials to introduce data science — analyzing and making sense of data — into first-year courses in science, technology, engineering and math.

Professor Petra Bonfert-Taylor led the team that wrote the grant. She said she and a group of other professors talked to a group of local companies about what skills they needed in workers. One of the most common needs was for data science, Bonfert-Taylor said.

The term “big data” has permeated society without much clarity for many about what it means.

A proposed master’s program in data science at the University of Nebraska at Omaha will cover the topic and capitalize on the need for big data experts in the workforce.

UNO’s proposal will go to the NU Board of Regents on Friday at Varner Hall, 3835 Holdrege St. in Lincoln. Presentations will be made at 10 a.m. on flood recovery efforts and information security. The official board meeting will start at 11 a.m.

Starkville, MS September 12-13 at Mississippi State University. “This year’s theme is ‘Cybernetic City: An Ecosystem for Big, Smart, and Fast Economies.'” [$$]S

Urbana, IL September 27-28 at Krannert Center for Performing Arts, “The free, all ages hack will bring together campus and community to build and innovate for the greater good of Champaign-Urbana.”

Launching your own laboratory marks the beginning of a new and exciting phase of your career. But it can also be overwhelming. Few of us have any training in the management skills necessary to build and run an effective lab. Here, we offer three important tips to keep in mind.

1. Play to your affordances

Facebook AI Research, together with Google’s DeepMind, University of Washington, and New York University, today introduced SuperGLUE, a series of benchmark tasks to measure the performance of modern, high performance language-understanding AI.

Software developers searching for answers might use natural language – “How do I insert an element array in a specific position?” – or they might choose a few important keywords relevant to the programming task at hand and use those as their query with the hope that the search engine would return the relevant solutions. A lot of the time they find the relevant code, but don’t find a clear explanation of how to implement it. Other times they find a great explanation about how one might solve the problem, but not the actual code.

Earlier this year, a team of computer science researchers published a paper with a novel solution to this problem: CROKAGE – the Crowd Knowledge Answer Generator. This service takes the description of a programming task as a query and then provides relevant, comprehensive programming solutions containing both code snippets and their succinct explanations.