LSU gymnast Olivia Dunne's endorsement of an artificial intelligence essay-writing product is raising questions about whether college athletic programs should provide clearer ethical guidelines for athletes earning money from name, image and likeness contracts. "It does seem problematic to have people sort of promoting plagiarism," said John Basl, a philosophy professor at Northeastern University in Boston who specializes in AI and data ethics, and who also is a faculty affiliate of Harvard's Berkman-Klein Center for Internet and Society. "It does seem like something colleges should teach students not to do," Basl added. Dunne's endorsement of a Caktus.AI product appeared recently in the form of a 10-second video post on social media sites including TikTok and YouTube. Dunne does not speak in the video, but a written quotation representing her thinking states: "Need to get my creativity flowing for an essay due at midnight."

Artificial intelligence has become a prominent issue in education circles after the unveiling of ChatGPT, a large-scale learning model that scours the internet for information it can use to produce text in a conversational format. Many academics have raised concerns that students will use ChatGPT or similar tools to generate written assignments instead of doing the work themselves. In February, the university posted an explainer on its website describing for faculty what ChatGPT and other artificial intelligence tools are and what their limitations can be. "You've likely seen a lot of panic and concerns about how to best adapt," the explainer says. "But as with any technology, this is an ideal opportunity to reflect on our current teaching practices, experiment with new opportunities, and brainstorm ways they could be utilized effectively in a classroom." It is unclear how much Dunne made from her TikTok post about Caktus AI, but the junior gymnast from Hillsdale, New Jersey has captured college athletics' new world of name, image and likeness (NIL) profits unlike any other.

Over the recent twenty years, argumentation has received considerable attention in the fields of knowledge representation, reasoning, and multi-agent systems. However, argumentation in dynamic multi-agent systems encounters the problem of significant arguments generated by agents, which comes at the expense of representational complexity and computational cost. In this work, we aim to investigate the notion of abstraction from the model-checking perspective, where several arguments are trying to defend the same position from various points of view, thereby reducing the size of the argumentation framework whilst preserving the semantic flow structure in the system.

Why, I wondered, was the enthusiasm so high at this week's Techonomy Climate conference in Mountain View? So I asked a smart friend why climate action suddenly commands so much passion. "The pandemic helped people realize a disaster can strike everyone on the planet all at once," they answered. "Almost none of us really thought it was possible before." It was as good an explanation as any.

The study of extension-based semantics within the seminal abstract argumentation model of Dung has largely focused on definitional, algorithmic and complexity issues. In contrast, matters relating to comparisons of representational limits, in particular, the extent to which given collections of extensions are expressible within the formalism, have been under-developed. As such, little is known concerning conditions under which a candidate set of subsets of arguments are "realistic" in the sense that they correspond to the extensions of some argumentation framework AF for a semantics of interest. In this paper we present a formal basis for examining extension-based semantics in terms of the sets of extensions that these may express within a single AF. We provide a number of characterization theorems which guarantee the existence of AFs whose set of extensions satisfy specific conditions and derive preliminary complexity results for decision problems that require such characterizations.

On September 2 in French Guiana, an AI satellite was launched into the Earth's orbit for the first time in history. PhiSat-1 is now soaring at over 17,000 mph about 329 miles above us, monitoring polar ice and soil moisture through a hyperspectral-thermal camera, while also testing inter-satellite communication systems. Onboard the small satellite is an AI system developed by Ubotica and powered by Intel's Myriad 2 VPU -- the same chip inside many smart cameras, Magic Leap's AR goggles, and a $99 selfie drone. Its first task is filtering out images of clouds that impede the analysis. Clouds cover around two-thirds of Earth's surface at any given moment, which can severely disrupt the system's analysis.

Artificial intelligence (AI) is certainly the'flavor of the month' and has become a part of our daily lives. However, there is one area that, until now, hasn't been involved in AI… As ubiquitous as artificial intelligence has become in modern life -- from boosting the understanding of the cosmos to surfacing entertaining videos on a phone -- AI hasn't yet found its way into orbit. That is until September 2, when an experimental satellite about the size of a cereal box was ejected from a rocket's dispenser along with 45 other similarly small satellites. The satellite, named PhiSat-1, is now soaring at over 17,000 mph (27,500 kmh) in sun-synchronous orbit about 329 miles (530 km) overhead. PhiSat-1 contains a new hyperspectral-thermal camera and onboard AI processing from an Intel Movidius Myriad 2 Vision Processing Unit (VPU) -- the same chip inside many smart cameras and even a $99 selfie taken by a drone on Earth.

As ubiquitous as artificial intelligence has become in modern life -- from boosting our understanding of the cosmos to surfacing entertaining videos on your phone -- AI hasn't yet found its way into orbit. That is until Sept. 2, when an experimental satellite about the size of a cereal box was ejected from a rocket's dispenser along with 45 other similarly small satellites. The satellite, named PhiSat-1, is now soaring at over 17,000 mph (27,500 kmh) in sun-synchronous orbit about 329 miles (530 km) overhead. PhiSat-1 contains a new hyperspectral-thermal camera and onboard AI processing thanks to an Intel Movidius Myriad 2 Vision Processing Unit (VPU) -- the same chip inside many smart cameras and even a $99 selfie drone here on Earth. PhiSat-1 is actually one of a pair of satellites on a mission to monitor polar ice and soil moisture, while also testing intersatellite communication systems in order to create a future network of federated satellites.

As ubiquitous as artificial intelligence has become in modern life -- from boosting our understanding of the cosmos to surfacing entertaining videos on your phone -- AI hasn't yet found its way into orbit. That is until Sept. 2, when an experimental satellite about the size of a cereal box was ejected from a rocket's dispenser along with 45 other similarly small satellites. The satellite, named PhiSat-1, is now soaring at over 17,000 mph (27,500 kmh) in sun-synchronous orbit about 329 miles (530 km) overhead. PhiSat-1 contains a new hyperspectral-thermal camera and onboard AI processing thanks to an Intel Movidius Myriad 2 Vision Processing Unit (VPU) -- the same chip inside many smart cameras and even a $99 selfie drone here on Earth. PhiSat-1 is actually one of a pair of satellites on a mission to monitor polar ice and soil moisture, while also testing intersatellite communication systems in order to create a future network of federated satellites.

Intel, Ubotica, and the European Space Agency (ESA) have launched the first AI satellite into Earth's orbit. The PhiSat-1 satellite is about the size of a cereal box and was ejected from a rocket's dispenser alongside 45 other satellites. The rocket launched from Guiana Space Centre on September 2nd. Intel has integrated its Movidius Myriad 2 Vision Processing Unit (VPU) into PhiSat-1 – enabling large amounts of data to be processed on the device. This helps to prevent useless data being sent back to Earth and consuming precious bandwidth.