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Artificial Intelligence and Antitrust Activity Subscribe

#artificialintelligence

In a recently published paper, a pair of academics propose that the application of artificial intelligence can offer a potent weapon against antitrust behavior in the Big Tech sector. This is the very industry that has advanced this technology, noted one of those academics, Giovana Massarotto, a Center for Technology, Innovation and Competition academic fellow at the University of Pennsylvania Carey Law School and an adjunct professor at the University of Iowa. She underscored this fact in an article for Bloomberg Law, in which she maintains that "the present economic democracy propaganda against Big Tech is not the solution to increase competition in fast-moving technology markets." In fact, she says, the industry's ingenuity is needed to achieve our nation's pro-competition goals. Massarotto and University of Liege (Belgium) Associate Professor Ashwin Ittoo write about their "antitrust machine learning application" (AML) which shows the potential for AI to "assist antitrust agencies in detecting anticompetitive practices faster."


Artificial Intelligence and Antitrust Activity

#artificialintelligence

In a recently published paper, a pair of academics propose that the application of artificial intelligence can offer a potent weapon against antitrust behavior in the Big Tech sector. This is the very industry that has advanced this technology, noted one of those academics, Giovana Massarotto, a Center for Technology, Innovation and Competition academic fellow at the University of Pennsylvania Carey Law School and an adjunct professor at the University of Iowa. She underscored this fact in an article for Bloomberg Law, in which she maintains that "the present economic democracy propaganda against Big Tech is not the solution to increase competition in fast-moving technology markets." In fact, she says, the industry's ingenuity is needed to achieve our nation's pro-competition goals. Massarotto and University of Liege (Belgium) Associate Professor Ashwin Ittoo write about their "antitrust machine learning application" (AML) which shows the potential for AI to "assist antitrust agencies in detecting anticompetitive practices faster."


New institute aims to unlock the secrets of corn using artificial intelligence

#artificialintelligence

Iowa State University researchers are growing two kinds of corn plants. If you drive past the many fields near the university's campus in Ames, you can see row after row of the first. But the second exists in a location that hasn't been completely explored yet: cyberspace. The researchers, part of the AI Institute for Resilient Agriculture, are using photos, sensor data and artificial intelligence to create "digital twins" of corn plants that, through analysis, can lead to a better understanding of their real-life counterparts. They hope the resulting software and techniques will lead to better management, improved breeding, and ultimately, smarter crops.


An interaction regression model for crop yield prediction - Scientific Reports

#artificialintelligence

Crop yield prediction is crucial for global food security yet notoriously challenging due to multitudinous factors that jointly determine the yield, including genotype, environment, management, and their complex interactions. Integrating the power of optimization, machine learning, and agronomic insight, we present a new predictive model (referred to as the interaction regression model) for crop yield prediction, which has three salient properties. First, it achieved a relative root mean square error of 8% or less in three Midwest states (Illinois, Indiana, and Iowa) in the US for both corn and soybean yield prediction, outperforming state-of-the-art machine learning algorithms. Second, it identified about a dozen environment by management interactions for corn and soybean yield, some of which are consistent with conventional agronomic knowledge whereas some others interactions require additional analysis or experiment to prove or disprove. Third, it quantitatively dissected crop yield into contributions from weather, soil, management, and their interactions, allowing agronomists to pinpoint the factors that favorably or unfavorably affect the yield of a given location under a given weather and management scenario. The most significant contribution of the new prediction model is its capability to produce accurate prediction and explainable insights simultaneously. This was achieved by training the algorithm to select features and interactions that are spatially and temporally robust to balance prediction accuracy for the training data and generalizability to the test data.


This Machine Learning Research Finds The Relationship Between Body Shape And Income

#artificialintelligence

A new study published in the journal PLOS One revealed a link between a person's body type and their family's earnings. According to the study's findings, physically appealing people are likely to earn more than those who aren't. According to researchers, the beauty premium is a reality. However, a University of Iowa associate professor and his colleagues found that the metrics employed to assess physical attractiveness had some severe shortcomings. Most earlier studies frequently defined physical appearance from subjective evaluations based on surveys. In addition, these metrics are too simplistic to provide a thorough description of body forms.


Short men and obese women earn $1,000 less a year than taller, thinner people, study warns

Daily Mail - Science & tech

Short men and obese women earn up to $1,000 (£700) less per year than their taller, skinnier counterparts, according to a new study into body shape and salary. This is evidence of a long suspected'beauty premium' that suggests physical attractiveness demands a higher value in the labour market, according to lead author Suyong Song from the University of Iowa. Researchers examined data from 2,383 volunteers, including whole body scans and information on their family income and gender. They found that in men earning over $70,000 (£50,000) per year, a centimetre increase in height was worth $1,000 (£700) extra in income per year. For women earning the same amount, every single point decrease in BMI was worth an extra $1,000 (£700) per year in their pay cheque, the researchers discovered.


A Machine Learning Method to Block Ads Based on Local Browser Behavior

#artificialintelligence

Researchers in Switzerland and the US have devised a new machine learning approach to the detection of website advertising material that's based on the way such material interacts with the browser, rather than by analyzing its content or network behavior – two approaches which have proved ineffective in the long term in the face of CNAME cloaking (see below). Dubbed WebGraph, the framework uses a graph-based AI ad-blocking approach to detect promotional content by concentrating on such essential activities of network advertising – including telemetry attempts and local browser storage – that the only effective evasion technique would be to not conduct these activities. Though previous approaches have achieved slightly higher detection rates than WebGraph, all of them are prone to evasive techniques, while WebGraph is able to approach 100% integrity in the face of adversarial responses, including more sophisticated hypothesized responses that may emerge in the face of this novel ad-blocking method. The paper is led by two researchers from the Swiss Federal Institute of Technology, in concert with researchers from University of California, Davis and the University of Iowa. The work is a development from a 2020 research initiative with Brave browser called AdGraph, which featured two of the researchers from the new paper.


Weak Supervision in Biomedicine

#artificialintelligence

We discuss Jason's path into machine learning, empowering doctors and scientists with weak supervision, and utilizing organizational resources in biomedical applications of Snorkel. This episode is part of the #ScienceTalks video series hosted by the Snorkel AI team. Jason: Originally, during my undergraduate days, I intended to go into medicine. However, I enjoyed engineering classes way more than biology classes, so I shifted and majored in Computer Science and English. I also worked with a research group at the University of Iowa to track infections in hospitals.


Iowa Board of Regents approves artificial intelligence degree program for Iowa State

#artificialintelligence

A new artificial intelligence graduate degree program at Iowa State University will be the first of its kind in the state. The Iowa Board of Regents approved the two-year master's of science degree program Thursday through consent agenda after being presented with the program Wednesday in committee. The graduate program is expected to begin this fall. Hridesh Rajan, a professor and chairperson of ISU's Department of Computer Science, said the new program seeks to produce graduates that can work on building and enhancing components of artificial intelligence -- not only to be able to understand and make practical use of machine learning and big data, but also be able to communicate the capabilities and limitations of AI. Artificial intelligence, or AI, is the study of techniques that help incorporate intelligence into software, Rajan said.


Preparing for emergency response with partial network information

AIHub

Natural disasters cause considerable economic damage, loss of life, and network disruptions each year. As emergency response and infrastructure systems are interdependent and interconnected, quick assessment and repair in the event of disruption is critical. School of Computational Science and Engineering (CSE) Associate Professor B. Aditya Prakash is leading a collaborative effort with researchers from Georgia Institute of Technology, University of Oklahoma, University of Iowa, and University of Virginia to determine the state of an infrastructure network during such a disruption. Prakash's group has also been collaborating closely with the Oak Ridge National Laboratory on such problems in critical infrastructure networks. However, according to Prakash, quickly determining which infrastructure components are damaged in the event of a disaster is not easily done after a disruption.