Purdue University introduces new AI working competency requirement for all undergraduate students at West Lafayette and Indianapolis campuses.

Extremists across the US have weaponized artificial intelligence tools to help them spread hate speech more efficiently, recruit new members, and radicalize online supporters at an unprecedented speed and scale, according to a new report from the Middle East Media Research Institute (MEMRI), an American non-profit press monitoring organization. The report found that AI-generated content is now a mainstay of extremists' output: They are developing their own extremist-infused AI models, and are already experimenting with novel ways to leverage the technology, including producing blueprints for 3D weapons and recipes for making bombs. Researchers at the Domestic Terrorism Threat Monitor, a group within the institute which specifically tracks US-based extremists, lay out in stark detail the scale and scope of the use of AI among domestic actors, including neo-Nazis, white supremacists, and anti-government extremists. "There initially was a bit of hesitation around this technology and we saw a lot of debate and discussion among [extremists] online about whether this technology could be used for their purposes," Simon Purdue, director of the Domestic Terrorism Threat Monitor at MEMRI, told reporters in a briefing earlier this week. "In the last few years we've gone from seeing occasional AI content to AI being a significant portion of hateful propaganda content online, particularly when it comes to video and visual propaganda. So as this technology develops, we'll see extremists use it more."

LiDAR simulation and the relevant LiDAR-based applications in Ghallabi et al. [6] used multi-layer LiDAR data to detect lane forestry in Sec. 2, the design and creation of our dataset and metrics markings, which were matched to a prior map using particle filtering in Sec. 3, the extensibility and potential applications in Sec. 4, a to achieve improvements over standard GPS solutions. Jacobsen discussion of future work in Sec. 5, and a conclusion summarizing and Teizer [12] proposed a novel worker safety monitoring system the work in Sec. 6. For its effectiveness, we hope the simulation using LiDAR for precise real-time presence detection near hazards, system and data can catalyze a transformation in simulation systems demonstrably improving over GPS solutions when tested in a virtual and inspire new insights to the digital forestry community.

They can only dream of what it's like to burst onto the field in The Big Show on Opening Day, but Purdue University outfielders Cam Thompson and Curtis Washington Jr. are among thousands of college baseball players with access to more data-juiced tech than ever to use in the hopes of getting to the majors. One of the tools their team has tested tracks and visualizes every joint in their bodies to measure and analyze their dynamic movements, helping them become a split-second faster on the base paths or gain an edge on runners when they throw home. "I was the slowest on the team," said Thompson in a video describing Purdue's use of 3D Athlete Tracking, or 3DAT, technology developed by Intel, which captures video footage and applies computer vision and deep learning to digitize an individual player's skeletal data and calculate biomechanics. The data and analytical insights gave Thompson and his coaches information revealing that he was bent over just slightly when launching himself from a base. "To the eye, you might not see this, but those first four or five steps were actually slowing him down," said John Madia, director of Baseball Player Development at Purdue.

WHEN the human brain learns something new, it adapts. But when artificial intelligence learns something new, it tends to forget information it already learned. As companies use more and more data to improve how AI recognizes images, learns languages and carries out other complex tasks, a paper published in Science this week shows a way that computer chips could dynamically rewire themselves to take in new data like the brain does, helping AI to keep learning over time. "The brains of living beings could continuously learn throughout their lifespan. We have now created an artificial platform for machines to learn throughout their lifespan," said Shriram Ramanathan, a professor in Purdue University's School of Materials Engineering who specializes in discovering how materials could mimic the brain to improve computing.

As companies use more and more data to improve how AI recognizes images, learns languages and carries out other complex tasks, a paper publishing in Science this week shows a way that computer chips could dynamically rewire themselves to take in new data like the brain does, helping AI to keep learning over time. "The brains of living beings can continuously learn throughout their lifespan. We have now created an artificial platform for machines to learn throughout their lifespan," said Shriram Ramanathan, a professor in Purdue University's School of Materials Engineering who specializes in discovering how materials could mimic the brain to improve computing. Unlike the brain, which constantly forms new connections between neurons to enable learning, the circuits on a computer chip don't change. A circuit that a machine has been using for years isn't any different than the circuit that was originally built for the machine in a factory.

When the human brain learns something new, it adapts. But when artificial intelligence learns something new, it tends to forget information it already learned. As companies use more and more data to improve how AI recognizes images, learns languages and carries out other complex tasks, a paper publishing in Science this week shows a way that computer chips could dynamically rewire themselves to take in new data like the brain does, helping AI to keep learning over time. "The brains of living beings can continuously learn throughout their lifespan. We have now created an artificial platform for machines to learn throughout their lifespan," said Shriram Ramanathan, a professor in Purdue University's School of Materials Engineering who specializes in discovering how materials could mimic the brain to improve computing.

The COVID-19 pandemic is leading a Purdue University innovator to make changes as she works to provide new options for people with Parkinson's disease. Jessica Huber, a professor of Speech, Language, and Hearing Sciences and associate dean for research in Purdue's College of Health and Human Sciences, leads Purdue's Motor Speech Lab. Huber and her team are now doing virtual studies to evaluate speech disorders related to Parkinson's using artificial intelligence technology platforms. Huber and her team have been working to develop telepractice tools for the assessment and treatment of speech impairments like Parkinson's disease. They received a National Institutes of Health small business innovation and research grant to develop a telehealth platform to facilitate the provision of speech treatment with the SpeechVive device, which has received attention at the Annual Convention of the American Speech-Language-Hearing Association.

Since carbon sequestration is such an important factor for mitigating climate change, it's critical to understand the efficacy of reforestation efforts and develop solid estimates of forest carbon storage capacity. However, measuring forest properties can be difficult, especially in places that aren't easily reachable. Purdue University's Jingjing Liang, an assistant professor of quantitative forest ecology and co-chair of the Forest Advanced Computing and Artificial Intelligence (FACAI) Laboratory in the Department of Forestry and Natural Resources, led an international team to measure forest carbon capacity in northeast Asia. Their research, which blends remote sensing, field work and machine learning, offers the most up-to-date estimates of carbon capture potential in reclusive North Korea and details the benefits of reforestation efforts over the last two decades in China and South Korea. "Because there is historically scant data from North Korea, people know little about how much carbon is stored in this region," said Liang, whose findings were published in the journal Global Change Biology.

A global panel will consider how to define ethical leadership and the particular challenges posed by emerging technologies in a virtual event from 1-1:45 p.m. ET on Oct. 28. "Defining Ethical Leadership" is free and open to the public. Those who wish to participate may register online. The event is made possible by a grant from Lilly Endowment Inc. to support Leading Ethically in the Age of AI and Big Data, an initiative designed to develop curricula to foster character and ethical values in future leaders, preparing them to respond appropriately to the challenges posed by rapidly evolving technologies, such as artificial intelligence and Big Data management. "As we embark upon the work of our Lilly Endowment grant, a thoughtful conversation about how we define ethical leadership offers an appropriate starting point," said David Reingold, the Justin S. Morrill Dean of Liberal Arts and professor of sociology at Purdue, principal investigator for the grant.