Ben & Jerry's brand could be destroyed, says co-founder Ben & Jerry's will be destroyed as a brand if it remains with parent company Magnum, the company's co-founder Ben Cohen has told the BBC. His remarks are the latest in a long-running spat between the ice cream brand and its parent company over its ability to express its social activism and the continued independence of its board. The comments came on the day that the Magnum Ice Cream Company (TMICC) started trading on the European stock market - spinning off from owner Unilever. A spokesperson for Magnum said the firm wanted to build and strengthen Ben & Jerry's powerful, non-partisan values-based position in the world. Ben & Jerry's was sold to Unilever in 2000 in a deal which allowed it to retain an independent board and the right to make decisions about its social mission.

The future of cancer research is in good hands. Six high-school students in the U.S. are dedicated to making progress toward improving the diagnostics and treatment of the disease. The students were finalists in this year's Regeneron Science Talent Search, which is the country's oldest and most prestigious science and mathematics competition hosted by the Society for Science in Washington, D.C. "We are thrilled to honor these bright minds dedicated to making strides in cancer research," said Maya Ajmera, president and CEO of the Society for Science, a partner with Regeneron in the Science Talent Search. "These high-school students are not only advancing our understanding of the way cancer presents in the human body, but are paving the way for potential future therapies and helping unlock new possibilities in the fight against this formidable disease." Four of the six student finalists who specialized in cancer research are shown here.

Deep extreme classification (XC) aims to train an encoder architecture and an accompanying classifier architecture to tag a data point with the most relevant subset of labels from a very large universe of labels. XC applications in ranking, recommendation and tagging routinely encounter tail labels for which the amount of training data is exceedingly small. Graph convolutional networks (GCN) present a convenient but computationally expensive way to leverage task metadata and enhance model accuracies in these settings. This paper formally establishes that in several use cases, the steep computational cost of GCNs is entirely avoidable by replacing GCNs with non-GCN architectures. The paper notices that in these settings, it is much more effective to use graph data to regularize encoder training than to implement a GCN. Based on these insights, an alternative paradigm RAMEN is presented to utilize graph metadata in XC settings that offers significant performance boosts with zero increase in inference computational costs. RAMEN scales to datasets with up to 1M labels and offers prediction accuracy up to 15% higher on benchmark datasets than state of the art methods, including those that use graph metadata to train GCNs. RAMEN also offers 10% higher accuracy over the best baseline on a proprietary recommendation dataset sourced from click logs of a popular search engine. Code for RAMEN will be released publicly.

Robotics, automation, and related Artificial Intelligence (AI) systems have become pervasive bringing in concerns related to security, safety, accuracy, and trust. With growing dependency on physical robots that work in close proximity to humans, the security of these systems is becoming increasingly important to prevent cyber-attacks that could lead to privacy invasion, critical operations sabotage, and bodily harm. The current shortfall of professionals who can defend such systems demands development and integration of such a curriculum. This course description includes details about seven self-contained and adaptive modules on "AI security threats against pervasive robotic systems". Topics include: 1) Introduction, examples of attacks, and motivation; 2) - Robotic AI attack surfaces and penetration testing; 3) - Attack patterns and security strategies for input sensors; 4) - Training attacks and associated security strategies; 5) - Inference attacks and associated security strategies; 6) - Actuator attacks and associated security strategies; and 7) - Ethics of AI, robotics, and cybersecurity.

AI has been hitting the headlines recently, with generative AI, in particular, generating a great deal of interest. Two tools - the large language model chatbot ChatGPT and image generator Dall-E - have caused a big stir since launching as public betas in recent months. These can be thought of as the current cutting-edge, public-facing applications of AI. However, as they are both free to use, their creator – AI research organization OpenAI – has been open about the fact that in order to be sustainable, they will have to start making money at some point. When it comes to commercializing AI technology today, businesses are generally following one of two strategies.

Machines' ability to learn by processing data gleaned from sensors underlies automated vehicles, medical devices and a host of other emerging technologies. But that learning ability leaves systems vulnerable to hackers in unexpected ways, researchers at Princeton University have found. In a series of recent papers, a research team has explored how adversarial tactics applied to artificial intelligence (AI) could, for instance, trick a traffic-efficiency system into causing gridlock or manipulate a health-related AI application to reveal patients' private medical history. As an example of one such attack, the team altered a driving robot's perception of a road sign from a speed limit to a "Stop" sign, which could cause the vehicle to dangerously slam the brakes at highway speeds; in other examples, they altered Stop signs to be perceived as a variety of other traffic instructions. "If machine learning is the software of the future, we're at a very basic starting point for securing it," said Prateek Mittal, the lead researcher and an associate professor in the Department of Electrical Engineering at Princeton.

The use by Russia of Iranian drones in its war against Ukraine makes clear the weaknesses of its domestic industry and Tehran's growing claim on the market for unmanned aircraft, experts say. Washington believes Iran has delivered hundreds of drones, which Ukrainian officials say are now being used in strikes like those launched against cities and energy infrastructure on Monday. So far two models of Iranian drone have been identified in Ukraine's skies, built for two different purposes. One of them, the Shahed 136, is a relatively low-cost "kamikaze drone" that can be programmed to fly automatically to a set of GPS coordinates with a payload of explosives. "It flies quite low, striking a target that must be stationary at a range of a few hundred kilometres," said Pierre Grasser, a researcher tied to Paris' Sorbonne University.

Were you unable to attend Transform 2022? Check out all of the summit sessions in our on-demand library now! Digital twins enable enterprises to model and simulate buildings, products, manufacturing lines, facilities and processes. This can improve performance, quickly flag quality errors and support better decision-making. Today, most digital twin projects are one-off efforts.

Supply chain data and manufacturing data helped many industrial organizations achieve what had been the pinnacle of success -- lean manufacturing or just-in-time manufacturing. You order just the right amounts of raw materials to make the number of your products that are being ordered, and you keep very little inventory on hand. The model worked well in the predictable world and kept production costs down and maximized efficiency. But like so many other things, the pandemic broke all that. The data that went into the system provided a retrospective of what the world used to be like, but not anything that could predict future demand.

Digital transformation is driving all kinds of changes in enterprises, including the growing use of AI. Though AI and data centers have existed for decades, graphics processing units (GPUs) in data centers are a fairly recent development. "GPUs have high levels of parallelism and can apply math operations to highly parallel datasets. CPUs can perform the same task but do not have the parallelism of GPUs so they're not as efficient at these tasks," said Alan Priestley, vice president analyst of emerging technologies and trends at Gartner. He believes that GPUs are best-considered workload accelerators that are optimized for specific sets of operations to complement CPUs.