Chimp infants are three times more likely to take risks than adults. Breakthroughs, discoveries, and DIY tips sent six days a week. Given the many similarities between humans and chimpanzees, one might assume that both species similarly engage in risky behavior within the same age range. However, according to a study recently published in the journal, it turns out that in chimps, it's the infants you have to watch out for. After studying videos of 119 wild chimpanzees, researchers found that chimpanzees' risky behavior peaks in their infancy, and then lessens as they get older.

Detecting AI risks becomes more challenging as stronger models emerge and find novel methods such as Alignment Faking to circumvent these detection attempts. Inspired by how risky behaviors in humans (i.e., illegal activities that may hurt others) are sometimes guided by strongly-held values, we believe that identifying values within AI models can be an early warning system for AI's risky behaviors. We create LitmusValues, an evaluation pipeline to reveal AI models' priorities on a range of AI value classes. Then, we collect AIRiskDilemmas, a diverse collection of dilemmas that pit values against one another in scenarios relevant to AI safety risks such as Power Seeking. By measuring an AI model's value prioritization using its aggregate choices, we obtain a self-consistent set of predicted value priorities that uncover potential risks. We show that values in LitmusValues (including seemingly innocuous ones like Care) can predict for both seen risky behaviors in AIRiskDilemmas and unseen risky behaviors in HarmBench.

Young motorcyclists, particularly those aged 15 to 24 years old, face a heightened risk of severe crashes due to factors such as speeding, traffic violations, and helmet usage. This study aims to identify key factors influencing crash severity by analyzing 10,726 young motorcyclist crashes in Texas from 2017 to 2022. Two advanced tabular deep learning models, ARMNet and MambaNet, were employed, using an advanced resampling technique to address class imbalance. The models were trained to classify crashes into three severity levels, Fatal or Severe, Moderate or Minor, and No Injury. ARMNet achieved an accuracy of 87 percent, outperforming 86 percent of Mambanet, with both models excelling in predicting severe and no injury crashes while facing challenges in moderate crash classification. Key findings highlight the significant influence of demographic, environmental, and behavioral factors on crash outcomes. The study underscores the need for targeted interventions, including stricter helmet enforcement and educational programs customized to young motorcyclists. These insights provide valuable guidance for policymakers in developing evidence-based strategies to enhance motorcyclist safety and reduce crash severity.

Amazon is reportedly using artificial intelligence (AI) to determine how much its delivery drivers should be paid by and their employment status. According to The Information, which first reported the news, the AI-powered surveillance cameras in delivery trucks are monitoring the driver's behavior and scoring them on safety infractions like tailgating, speeding or illegal U-turns. The news outlet says it obtained confidential documents that reveal cameras inside vans count the number of potentially dangerous actions – most equal one point, but others like running a stop sign are worth 10 points. The documents also states that contracted drives receive a report card each week, showing their performance that ranges from'fantastic' to'poor' that shows how many infractions occurred for every 100 trips. Those with five or fewer violations per 100 trips usually receive a'fantastic' score, according to The Information. The Amazon documents also states that the firm will remove some infractions to balance to account for'edge cases' where the cameras' AI software misidentifies violations.

As the end of the year approaches, we here at Constructech typically spend quite a bit of time diggi Register for a free membership or log in to read the rest of this content. Register for a free membership or log in to read the rest of this content. Share This Story, Choose Your Platform!

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Starting a new life is difficult for criminals transitioning from prison back to regular society. To help those individuals, Purdue University Polytechnic Institute researchers are using artificial intelligence to uncover risky behaviors which could then help identify when early intervention opportunities could be beneficial. Results of a U.S. Department of Justice study indicated more than 80 percent of people in state prisons were arrested at least once in the nine years following their release. Almost half of those arrests came in the first year following release. Marcus Rogers and Umit Karabiyik of Purdue Polytechnic's Department of Computer and Information Technology, are leading an ongoing project focused on using AI-enabled tools and technology to reduce the recidivism rates for convicted criminals who have been released.

To help prisoners transitioning back to regular society, Purdue University Polytechnic Institute researchers are using artificial intelligence to uncover risky behaviors which could then help identify when early intervention opportunities could be beneficial. A U.S. Department of Justice study found that more than 80 percent of people in state prisons were arrested at least once in the nine years following their release from prison. Almost half of those arrests came in the first year following release. Marcus Rogers and Umit Karabiyik of Purdue Polytechnic's Department of Computer and Information Technology are leading an ongoing project focused on using AI-enabled tools and technology to reduce the recidivism rates for convicted criminals who have been released. Both are examining the forensic psychology aspect by identifying risky behaviors, stressful situations, and other behavioral and physiological factors connected to a risk of individuals returning to criminal behavior. Rogers is a professor and Karabiyik is an assistant professor in the fields of digital and cyber forensics.

Starting a new life is difficult for criminals transitioning from prison back to regular society. To help those individuals, Purdue University Polytechnic Institute researchers are using artificial intelligence to uncover risky behaviors which could then help identify when early intervention opportunities could be beneficial. Results of a U.S. Department of Justice study indicated more than 80 percent of people in state prisons were arrested at least once in the nine years following their release. Almost half of those arrests came in the first year following release. Marcus Rogers and Umit Karabiyik of Purdue Polytechnic's Department of Computer and Information Technology, are leading an ongoing project focused on using AI-enabled tools and technology to reduce the recidivism rates for convicted criminals who have been released.

Starting a new life is difficult for criminals transitioning from prison back to regular society. To help those individuals, Purdue University Polytechnic Institute researchers are using artificial intelligence to uncover risky behaviors which could then help identify when early intervention opportunities could be beneficial. Results of a U.S. Department of Justice study indicated more than 80 percent of people in state prisons were arrested at least once in the nine years following their release. Almost half of those arrests came in the first year following release. Marcus Rogers and Umit Karabiyik of Purdue Polytechnic's Department of Computer and Information Technology, are leading an ongoing project focused on using AI-enabled tools and technology to reduce the recidivism rates for convicted criminals who have been released.