So far, ACE has demonstrated advanced virtual AI dogfights involving both within-visual-range (WVR) and beyond-visual-range (BVR) multi-aircraft scenarios with simulated weapons, plus live flying using an instrumented jet to measure pilot physiology and trust in AI. Throughout the ACE program, which began last year, DARPA has stressed the importance of establishing human pilots' trust in AI, allowing it to conduct the actual combat maneuvers while the human concentrates on overarching battle management decisions. The process of "capturing trust data" has seen test pilots fly in an L-29 Delfin jet trainer at the University of Iowa Technology Institute's Operator Performance Laboratory. This aircraft has been adapted with cockpit sensors to measure the pilot's physiological responses, giving an insight into whether or not the pilot trusts the AI. In these missions, the L-29 has been flown by a safety pilot in the front seat, who makes flight control inputs based on AI decisions.
The Google Home Mini, like the Amazon Echo Dot, really started the smart speaker revolution -- and while the Google Home Mini launched in 2016, it's still humming along with more smarts than ever before. Right now at StackSocial the Google Home Mini is just $19.99 -- nearly 60% off its original $49.95 price tag. The big appeal of the Home Mini is adding the Google Assistant to your room. You can ask for your favorite music, a trivia game show to entertain the children and even questions. The assistant knows how far Earth is from the sun and the weather in Cedar Rapids, Iowa, alike.
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.
If you want a quick model that does not require you to separate the numerical columns from categorical ones, does not require you to ordinal encode or one hot encode them, and does not require you to standardise the independent variables? If your answer is yes then maybe you need to try CatBoost. CatBoost is an open source library, based on the concept of gradient boosting, which has been developed by the Russian company, Yandex. CatBoost is an especially powerful library because it yields state-of-the-art results without extensive data training typically required by other machine learning methods, and provides powerful out-of-the-box support for the more descriptive data formats that accompany many business problems. In order to show that CatBoost can make predictions on categorical data that has not been encoded and scaled, I selected a very popular dataset to experiment on: Kaggle's Ames House Price dataset, which forms part of one of their competitions on advanced regression, the link being found here:- House Prices -- Advanced Regression Techniques Kaggle "Ask a home buyer to describe their dream house, and they probably won't begin with the height of the basement ceiling or the proximity to an east-west railroad. But this playground competition's dataset proves that much more influences price negotiations than the number of bedrooms or a white-picket fence. With 79 explanatory variables describing (almost) every aspect of residential homes in Ames, Iowa, this competition challenges you to predict the final price of each home. The Ames Housing dataset was compiled by Dean De Cock for use in data science education. It's an incredible alternative for data scientists looking for a modernized and expanded version of the often cited Boston Housing dataset."
He also shares his experience about using robotics for K-12 education. Jivko Sinapov received his Ph.D. in Computer Science and Human-Computer Interaction from Iowa State University (ISU). While working toward his Ph.D. at ISU's Developmental Robotics Lab, he developed novel methods for behavioral object exploration and multi-modal perception. He went on to be a clinical assistant professor with the Texas Institute for Discovery, Education, and Science at UT Austin and a postdoctoral associate working with Peter Stone at the Artificial Intelligence lab. Sinapov's research interests include developmental robotics, computational perception, autonomous manipulation, and human-robot interaction.
Unlike traditional time series, the action sequences of human decision making usually involve many cognitive processes such as beliefs, desires, intentions and theory of mind, i.e. what others are thinking. This makes predicting human decision making challenging to be treated agnostically to the underlying psychological mechanisms. We propose to use a recurrent neural network architecture based on long short-term memory networks (LSTM) to predict the time series of the actions taken by the human subjects at each step of their decision making, the first application of such methods in this research domain. We trained our prediction networks on the behavioral data from several published psychological experiments of human decision making, and demonstrated a clear advantage over the state-of-the-art methods in predicting human decision making trajectories in both single-agent scenarios such as Iowa Gambling Task and multi-agent scenarios such as Iterated Prisoner's Dilemma.
As Computers and Artificial Intelligence (AI) play a key role in improving medical fields, experts in medical and engineering research at the University of Iowa are merging disciplines to work towards the advancement of medical AI with the help of a $1 million grant from the National Science Foundation. Assistant Professor of Industrial and Systems...
The First Five Consortium, a nod to the importance of the first five minutes in responding to a natural disaster, aims to build between 10 and 30 different AI-powered systems. Microsoft will provide technological resources, including its Azure cloud for AI model training and inference. Other organizations, including public- and private-sector entities, are expected to participate. The Morning Download delivers daily insights and news on business technology from the CIO Journal team. The announcement comes as California confronts another summer of raging wildfires, while Iowa reels from devastating windstorms.
"We showed that opioid medication utilization could be decreased by more than a third in an at-risk patient population by delivering psychotherapy via a chatbot," said the study's lead author, Christopher Anthony, MD, the associate director of Hip Preservation at Penn Medicine and an assistant professor of Orthopaedic Surgery. "While it must be tested with future investigations, we believe our findings are likely transferrable to other patient populations." Although opioids can be appropriate to treat the pain that results from an injury like a broken leg or arm, there is a concern that a large prescription of opioids might be an on-ramp to dependence for many. The researchers -- who included Edward Octavio Rojas, MD, a resident in Orthopaedic Surgery at the University of Iowa Hospitals & Clinics -- believe a low-effort, patient-centered approach to reducing the number of opioids taken can be a valuable method for cutting into the opioid epidemic. To test this approach, 76 patients who went to a Level 1 Trauma Center at the University of Iowa Hospitals & Clinics for fractures that required a surgery to fix were randomly divided into two groups.
Browsers including Firefox, Safari, Opera, and Chrome have begun providing protections against cross-site tracking methods employing cookies and IP addresses. It's an encouraging development, but there's a fear it will push trackers to adopt more opaque, "stateless" tracking like browser fingerprinting, which tracks browsers by the configuration information they make visible. To combat fingerprinting in particular, in a recent study, researchers at The University of Iowa, Mozilla, and the University of California, Davis investigated a machine learning-based approach called FP-Inspector that trains classifiers to learn fingerprinting. By extracting syntactic and semantic features through a combination of static and dynamic analyses that effectively complement each others' limitations, FP-Inspector overcomes the coverage issues of dynamic analysis while addressing the inability of static analysis to handle obfuscation, the coauthors say. Some browsers and privacy tools have tried to mitigate fingerprinting using techniques including API changes and network request blocking.