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The Global Reach of CMU AI

CMU School of Computer Science

As intractable problems accrue and grow, artificial intelligence is increasingly being called upon as part of the solution. Carnegie Mellon University AI researchers have stepped up to help surmount these obstacles where large data sets must be analyzed and patterns discovered to find answers. Last year, the National Science Foundation teamed up with the U.S. Department of Agriculture, the U.S. Department of Homeland Security, as well as corporate sponsors Accenture, Amazon, Google and Intel to provide $220 million in grants to create 11 new institutes specifically dedicated to AI research across a wide range of sectors. CMU's School of Computer Science and College of Engineering faculty will work with four of these new institutes: the AI Institute for Resilient Agriculture, the AI Institute for Collaborative Assistance and Responsive Interaction for Networked Groups, the AI Institute for Future Edge Networks and Distributed Intelligence, and the USDA-NIFA Institute for Agricultural AI for Transforming the Workforce and Decision Support. Learn more about these institutes and meet the researchers leading the work in our magazine, The Link.

Antimicrobial resistance with Artificial Intelligence


Minh-Hoang Tran,1 Ngoc Quy Nguyen,2 Hong Tham Pham1,3 1Department of Pharmacy, Nhan Dan Gia Dinh Hospital, Ho Chi Minh City, Vietnam; 2Institute of Environmental Technology and Sustainable Development, Nguyen Tat Thanh University, Ho Chi Minh City, Vietnam; 3Department of Pharmacy, Nguyen Tat Thanh University, Ho Chi Minh City, Vietnam Correspondence: Hong Tham Pham, Department of Pharmacy, Nguyen Tat Thanh University, Ho Chi Minh City, Vietnam, Tel 84 919 559 085, Email [email protected] Abstract: Recent years have witnessed the rise of artificial intelligence (AI) in antimicrobial resistance (AMR) management, implying a positive signal in the fight against antibiotic-resistant microbes. The impact of AI starts with data collection and preparation for deploying AI-driven systems, which can lay the foundation for some effective infection control strategies. Primary applications of AI include identifying potential antimicrobial molecules, rapidly testing antimicrobial susceptibility, and optimizing antibiotic combinations. Aside from their outstanding effectiveness, these applications also express high potential in narrowing the burden gap of AMR among different settings around the world. Despite these benefits, the interpretability of AI-based systems or models remains vague.

The teeniest robot in the world is a jumping crab


Engineers from Northwestern University in Evanston, Illinois have developed the smallest walking robot ever, and it's a crab. The half-millimeter robot is modeled after a peekytoe crab and is just the latest iteration in a long line of small robots created by the researchers. The goal for creating a bot so small is to move towards more practical uses of the technology and gaining entry to more hard to reach, tightly confined spaces.

AI reveals unsuspected math underlying search for exoplanets


Artificial intelligence (AI) algorithms trained on real astronomical observations now outperform astronomers in sifting through massive amounts of data to find new exploding stars, identify new types of galaxies and detect the mergers of massive stars, accelerating the rate of new discovery in the world's oldest science. But AI, also called machine learning, can reveal something deeper, University of California, Berkeley, astronomers found: Unsuspected connections hidden in the complex mathematics arising from general relativity--in particular, how that theory is applied to finding new planets around other stars. In a paper appearing this week in the journal Nature Astronomy, the researchers describe how an AI algorithm developed to more quickly detect exoplanets when such planetary systems pass in front of a background star and briefly brighten it--a process called gravitational microlensing--revealed that the decades-old theories now used to explain these observations are woefully incomplete. In 1936, Albert Einstein himself used his new theory of general relativity to show how the light from a distant star can be bent by the gravity of a foreground star, not only brightening it as seen from Earth, but often splitting it into several points of light or distorting it into a ring, now called an Einstein ring. This is similar to the way a hand lens can focus and intensify light from the sun. But when the foreground object is a star with a planet, the brightening over time--the light curve--is more complicated.

Using Data Science to Catch Criminals


The power of data science is not limited to solving technical or business issues. Its usage is not limited to data analytics to create new technologies, target ads to consumers, and maximize profits and sales in business. The concept of open science has led organizations to use data to handle social problems. It can offer a statistical and data-driven solution to hidden human behavior and cultural patterns. We will be using data from the San Francisco crime department to understand the relation between civilian-reported incidents of crime and police-reported incidents of crime. To store and readily access a large amount of data, we will be using GridDB as our database platform.

Where the billions spent on autonomous vehicles by U.S. and Chinese giants is heading


For years, Alphabet's Waymo and others leaders have promised autonomous vehicles are just around the bend. But that future has not arrived yet. "In one word, it's complexity," said James Peng, CEO and co-founder of, an autonomous vehicle company. "Every time there is a technical breakthrough, there are challenges. We have the AI, the fast computer chips, the sensors. Despite promises of life-saving, climate-change fighting, and cost-efficient driving, the reality is that "the autonomous vehicle nirvana is 10 years out," said Michael Dunne, CEO of autotech consultancy ZoZoGo. "While it's not impossible to get there, even the most advanced technologies are not there yet and used mainly in confined areas where things are predictable.

Understanding media narratives with machine learning and NLP


Storytelling and narrative crafting are central to communication techniques -- so much so that they drive the way news media, advertising, and public relations operate today. But the way narratives are used in these communications, as well as how they impact the opinions of individuals or an entire society, is extremely complex and difficult to express with any specificity. A new project at the University of Michigan supported by the Air Force Office of Scientific Research (AFOSR) aims to use computational tools to conceptualize these narratives and the impact they have on readers. "It remains unclear how to effectively represent and extract narratives at scale," says Computer Science and Engineering Prof. Lu Wang, the project's lead investigator, "and little is known about how they interact with people's inclination to have an impact and confirm their own values." This uncertainty stems from the problem's scope: understanding the narratives used in news media, for example, and how they affect millions of unique individuals involves countless variables.

How AI and machine learning are reshaping the way transit systems move traffic patterns – REJournals


Of the many ways artificial intelligence and machine learning are poised to improve modern life, the promise of impacting mass transit is significant. The world is much different compared with the early days of the pandemic, and people around the world are again leveraging mobility and transit systems for work, leisure and more. Across the U.S., traditional mass transit systems including buses, subways and personal vehicles have returned to struggling through gridlock, rider levels and congestion. However, advanced AI and machine learning solutions built on cloud-based platforms are being deployed to reduce these frustrations. Transportation is one of the most important areas in which modern AI provides a significant advantage over conventional algorithms used in traditional transit system technology.

Amazon flexes retail muscle with physical clothing store – TechCrunch


Signaling its ambitions to make a dent in the apparel market, Amazon today opened its first physical clothing store, Amazon Style, in the Greater Los Angeles Area. Offering a twist on the traditional experience, visitors to the Glendale, California shop at The Americana At Brand use an app to scan codes on displayed items from Steve Madden, Levi's, Lacoste and other brands to send them directly to a fitting room or pickup counter. As TechCrunch previously reported, Amazon Style features hundreds of brands chosen by "fashion creators" and "feedback provided by millions of customers shopping on" Scanning the QR code next to an item pops up a selector for sizes and colors, as well as details such as customer ratings and adds the item to a list for later perusing. Amazon Style doesn't use the cashierless "Just Walk Out" tech found in Amazon Fresh and Whole Foods locations, instead opting for Amazon's controversial Amazon One palm recognition service.

Global Big Data Conference


Artificial Intelligence (AI) Patent Application filings continue their explosive growth trend at the U.S. Patent Office (USPTO). At the end of 2020, the USPTO published a report finding an exponential increase in the number of patent application filings from 2002 to 2018. In addition, current data shows that AI-related application filings pertaining to graphics and imaging are taking the lead over AI modeling and simulation applications. In the last quarter of 2020, the United States Patent and Trademark Office (USPTO) reported that patent filings for Artificial Intelligence (AI) related inventions more than doubled from 2002 to 2018. See Office of the Chief Economist, Inventing AI: Tracking The Diffusion Of Artificial Intelligence With Patents, IP DATA HIGHLIGHTS No. 5 (Oct.