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

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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

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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.


#IROS2020 Plenary and Keynote talks focus series #4: Steve LaValle & Sarah Bergbreiter

Robohub

In this new release of our series showcasing the plenary and keynote talks from the IEEE/RSJ IROS2020 (International Conference on Intelligent Robots and Systems) you'll meet Steve LaValle (University of Oulu) talking about the area of perception, action and control, and Sarah Bergbreiter (Carnegie Mellon University) talking about bio-inspired microrobotics. Bio: Steve LaValle is Professor of Computer Science and Engineering, in Particular Robotics and Virtual Reality, at the University of Oulu. From 2001 to 2018, he was a professor in the Department of Computer Science at the University of Illinois. He has also held positions at Stanford University and Iowa State University. His research interests include robotics, virtual and augmented reality, sensing, planning algorithms, computational geometry, and control theory.


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

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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.


Artificial intelligence may reveal how microbiome affects vaccine response

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Researchers have been using artificial intelligence to study how the microbiome interacts with the human system to improve vaccine response. A team of researchers at Iowa State University, US, are employing innovative artificial intelligence (AI) to investigate how the microbiome interacts with the immune system. The team, led by Dr Gregory Phillips, said that they are focusing on gut bacteria that have adapted to live in the human digestive system to improve vaccine response. We want to go beyond associations to get causes, something in the microbiota that influences the host whereby vaccines can be improved" The team are leading trials in mice monitoring changes in microbiota spurred by vaccine delivery and immune response. As the interactions they will be observing are so complex, the team have collaborated with Indiana University, US, to apply machine learning to find patterns in vast amounts of data.


Iowa State part of US National Science Foundation newly established artificial intelligence …

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One of the 11 institutes, called the AI Institute for Intelligent Cyberinfrastructure with Computational Learning in the Environment (ICICLE), is led by The …


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

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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

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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.


After a tough year, schools are axing virtual learning. Some families want to stay online.

USATODAY - Tech Top Stories

During the throes of the pandemic, many parents, weary of monitoring their children's online classes, yearned for schools to reopen. Then vaccines expanded, schools reopened in many cities, and teachers returned – but huge numbers of students didn't. Weeks passed; safety protocols became routine. President Joe Biden's administration urged in-person attendance. And still millions of students stayed remote, their parents concerned about the virus, not to mention bullying, racism, misbehavior, or child care.