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Capturing 3D microstructures in real time

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Researchers at the Center for Nanoscale Materials (CNM), a U.S. Department of Energy (DOE) Office of Science User Facility located at the DOE's Argonne National Laboratory, have invented a machine-learning based algorithm for quantitatively characterizing, in three dimensions, materials with features as small as nanometers. Researchers can apply this pivotal discovery to the analysis of most structural materials of interest to industry. "What makes our algorithm unique is that if you start with a material for which you know essentially nothing about the microstructure, it will, within seconds, tell the user the exact microstructure in all three dimensions," said Subramanian Sankaranarayanan, group leader of the CNM theory and modeling group and an associate professor in the Department of Mechanical and Industrial Engineering at the University of Illinois at Chicago. "For example, with data analyzed by our 3D tool," said Henry Chan, CNM postdoctoral researcher and lead author of the study, "users can detect faults and cracks and potentially predict the lifetimes under different stresses and strains for all kinds of structural materials." Most structural materials are polycrystalline, meaning a sample used for purposes of analysis can contain millions of grains.


- C3.ai Digital Transformation Institute

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The C3.ai Digital Transformation Institute (C3.ai DTI) is a new research consortium established by C3.ai, Microsoft Corporation, the University of Illinois at Urbana-Champaign (UIUC), the University of California, Berkeley, Princeton University, the University of Chicago, the Massachusetts Institute of Technology, Carnegie Mellon University, and the National Center for Supercomputing Applications at UIUC. Jointly managed and hosted by UC Berkeley and UIUC, C3.ai DTI was created to establish the new Science of Digital Transformation of Societal Systems. C3.ai DTI's mission is to attract the world's leading scientists to join in a coordinated and innovative effort to advance the digital transformation of business, government, and society. Through partnerships with leading universities and strategic engagement with key industry partners, C3.ai DTI will catalyze advances in mathematical, statistical, and computing research, including Machine Learning (ML), Artificial Intelligence (AI), and the Internet of Things (IoT).


How to Avoid Astronomical AI Computing Costs

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Brock Ferguson is a practice-over-theory kind of guy. The Chicago-based data-science and machine-learning consultancy he co-founded in 2016, Strong Analytics, puts a major focus on productionizing AI models rather than just building out proofs of concept. "We want to minimize that gap between research in the lab and deploying to production," he said. "We think about that a lot." That means thinking a lot about cost -- something that's never far from the minds of machine-learning practitioners and consultants, but which came to the forefront again thanks to a much-circulated recent Andreesen Horowitz review that emphasized the high and ongoing computing costs of building and deploying artificial intelligence models.


Built In Chicago's 50 Startups to Watch in 2020

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Technology is Chicago's fastest-growing industry sector, having grown more 270 percent over the last decade, according to World Business Chicago. And 2019 was a model year that not only encapsulated the growth of technology in the city but also positioned Chicago for further success in 2020 and beyond. Influential leaders in tech launched Chicago's Plan for 2033, or P33, to enhance the city's viability as a global tech hub with a strong and diverse workforce through the next decade. Mayor Lori E. Lightfoot said on Chicago Tech Day that 15 local tech companies have added or will be adding 2,000 jobs through 2020. Uber announced it would be bringing that same number of jobs to Chicago over the next three years and spending more than $200 million annually on the city. But it isn't just major initiatives and companies with household names that will be bringing continued success to Chicago tech. Smaller startups entering the city's tech scene are shaping everything from mental health care to cryptocurrency trading to vehicle leasing. We found 50 such companies -- all less than three years old -- that are poised for growth in the coming year. Brett Quillen contributed in writing this report. Interested in Chicago tech?See all open roles on Built In CHI Arturo wants to take property risk management to the skies by using drones and satellite, aerial and ground imagery to assess residential and commercial property characteristics. The data it collects is powered by predictive analytics to give clients that lend, insure or invest in properties the ability to minimize risk and determine market patterns.


A.I. Versus the Coronavirus

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Advanced computers have defeated chess masters and learned how to pick through mountains of data to recognize faces and voices. Now, a billionaire developer of software and artificial intelligence is teaming up with top universities and companies to see if A.I. can help curb the current and future pandemics. Thomas M. Siebel, founder and chief executive of C3.ai, an artificial intelligence company in Redwood City, Calif., said the public-private consortium would spend $367 million in its initial five years, aiming its first awards at finding ways to slow the new coronavirus that is sweeping the globe. "I cannot imagine a more important use of A.I.," Mr. Siebel said in an interview. Digital Transformation Institute, the new research consortium includes commitments from Princeton, Carnegie Mellon, the Massachusetts Institute of Technology, the University of California, the University of Illinois and the University of Chicago, as well as C3.ai and Microsoft.


U.S. companies, labs rush to produce blood test for coronavirus immunity

The Japan Times

LOS ANGELES/CHICAGO/TORONTO – As the United States works overtime to screen thousands for the novel coronavirus, a new blood test offers the chance to find out who may have immunity -- a potential game-changer in the battle to contain infections and get the economy back on track. Several academic laboratories and medical companies are rushing to produce these blood tests, which can quickly identify disease-fighting antibodies in people who already have been infected but may have had mild symptoms or none at all. This is different from the current, sometimes hard-to-come-by diagnostic tests that draw on a nasal swab to confirm active infection. "Ultimately, this (antibody test) might help us figure out who can get the country back to normal," said Florian Krammer, a professor in vaccinology at Mount Sinai's Icahn School of Medicine. "People who are immune could be the first people to go back to normal life and start everything up again."


DeepScribe AI Can Help Translate Ancient Tablets

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Researchers from the University of Chicago's Oriental Institute and the Department of Computer Science have collaborated to design an AI that can help decode tablets from ancient civilizations. According to Phys.org, the AI is called DeepScribe and was trained on over 6,000 annotated images pulled from the Persepolis Fortification Archive, when it is complete the AI model will be able to interpret unanalyzed tablets, making studying ancient documents easier. Experts who study ancient documents, like the researchers who are studying the documents created during the Achaemenid Empire in Persia, need to translate ancient documents by hand, a long process that is prone to errors. Researchers have been using computers to assist in interpreting ancient documents since the 1990s, but the computer programs that were used were of limited help. The complex cuneiform characters, as well as the three-dimensional shape of the tablets, put a cap on how useful the computer programs could be.


STREETS: A Novel Camera Network Dataset for Traffic Flow

Neural Information Processing Systems

In this paper, we introduce STREETS, a novel traffic flow dataset from publicly available web cameras in the suburbs of Chicago, IL. We seek to address the limitations of existing datasets in this area. Many such datasets lack a coherent traffic network graph to describe the relationship between sensors. The datasets that do provide a graph depict traffic flow in urban population centers or highway systems and use costly sensors like induction loops. These contexts differ from that of a suburban traffic body.


A.I. Is Helping Archaeologists Translate Ancient Tablets Digital Trends

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Deep-learning artificial intelligence is helping grapple with plenty of problems in the modern world. But it also has its part to play in helping solve some ancient problems as well -- such as assisting in the translation of 2,500-year-old clay tablet documents from Persia's Achaemenid Empire. These tablets, which were discovered in modern-day Iran in 1933, have been studied by scholars for decades. However, they've found the translation process for the tablets -- which number in the tens of thousands -- to be laborious and prone to errors. "We have initial experiments applying machine learning to identify which cuneiform symbols are present in images of a tablet," Sanjay Krishnan, assistant professor at the University of Chicago's Department of Computer Science, told Digital Trends.


Researchers use AI to translate text found on ancient clay tablets

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Scientists at the University of Chicago are developing a machine learning system that can automatically transcribe text found on ancient clay tablets. The DeepScribe system will initially focus on transcribing the Cuneiform writing system used in the ancient Iranian Achaemenid Empire (550–330 BC), the University of Chicago News reports. Existing computer systems struggle to translate this script, due to its complex characters and the 3D form of the tablets on which they're written. The team of researchers from the University of Chicago's Oriental Institute and its Department of Computer Science thinks their system could do better. To build the model, they're training it on more than 6,000 annotated images from the Persepolis Fortification Archive.