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Category: robotics/AI – Page 76

Japan’s nuclear reactor robot inspector boosts power plant safety

Mitsubishi says that the robot has been developed to carry out non-destructive inspections of nuclear reactor vessels in underwater environments.

It states that the robot has been working at pressurized water reactor power plants across Japan since 1995, and has been used at least 50 times.

The robot can be controlled remotely using a computer and joystick by operators. The robot navigates around the hazardous environment, swimming in the water inside the nuclear reactor vessel, sticking to the walls with vacuum-pad feet, and using a probe to carry out ultrasonic testing.

AI tools can help hackers plant hidden flaws in computer chips, study finds

Widely available artificial intelligence systems can be used to deliberately insert hard-to-detect security vulnerabilities into the code that defines computer chips, according to new research from the NYU Tandon School of Engineering, a warning about the potential weaponization of AI in hardware design.

In a study published by IEEE Security & Privacy, an NYU Tandon research team showed that like ChatGPT could help both novices and experts create “hardware Trojans,” malicious modifications hidden within chip designs that can leak , disable systems or grant unauthorized access to attackers.

To test whether AI could facilitate malicious hardware modifications, the researchers organized a competition over two years called the AI Hardware Attack Challenge as part of CSAW, an annual student-run cybersecurity event held by the NYU Center for Cybersecurity.

Software tool shows clear advantage in water purity prediction

A powerful new software tool that can accurately predict the performance of biofilters used by the water industry could reduce the challenge of maintaining the purity of tap water.

Researchers from the University of Glasgow’s James Watt School of Engineering developed the tool, called the Environmental Buckingham Pi Neural Network, or EnviroPiNet.

It uses machine learning techniques paired with sophisticated physical modeling to predict the ability of biofilters to remove organic carbon compounds from water with up to 90% accuracy. The tool is now available online for free use.

Michael Freedman | The Poincaré Conjecture and Mathematical Discovery

Millennium Prize Problems Lecture 9/17/2025
Speaker: Michael Freedman, Harvard CMSA and Logical Intelligence.

Title: the poincaré conjecture and mathematical discovery.

Abstract: The AI age requires us to re-examine what mathematics is about. The Seven Millenium Problems provide an ideal lens for doing so. Five of the seven are core mathematical questions, two are meta-mathematical – asking about the scope of mathematics. The Poincare conjecture represents one of the core subjects, manifold topology. I’ll explain what it is about, its broader context, and why people cared so much about finding a solution, which ultimately arrived through the work of R. Hamilton and G. Perelman. Although stated in manifold topology, the proof requires vast developments in the theory of parabolic partial differential equations, some of which I will sketch. Like most powerful techniques, the methods survive their original objectives and are now deployed widely in both three-and four-dimensional manifold topology.

A Review of Artificial Intelligence-Based Down Syndrome Detection Techniques

In this section, the authors reveal the findings of this review. The findings are categorized based on data modalities, showcasing the effectiveness of AI models in terms of evaluation metrics. Figure 1 summarizes the extraction process, providing a clear representation of the progression from article identification to final selection of studies. The initial search yielded a substantial total of 1,175 articles. Based on the inclusion and exclusion criteria, the subsequent screening process excluded irrelevant articles. By meticulously filtering the literature, 25 studies were deemed suitable for inclusion into this review.

A chronology of research studies on the uses of AI in DS diagnosis is shown in Figure 2. This timeline highlights a considerable growth in academic interest over the course of the years. A single study was published per year between the years 2013 and 2017. Technical restrictions and the availability of datasets restricted the early attempts to integrate AI into DS diagnoses. Advancements in deep learning and machine learning technologies have been driven by continuous growth in research, representing a milestone in 2021. These developments are signs of increasing confidence in the ability of artificial intelligence to identify and resolve challenging diagnostic problems. The year 2021 reaches a high with four studies, indicating a surge of innovation. This may result from improved computing tools and a more extensive understanding of the usefulness of artificial intelligence in the medical field. However, the minor decline in 2022 and 2023, with three studies, may indicate difficulties in maintaining the rapid pace of research. These challenges may include restricted access to different datasets or limitations to clinical adoption.

In 2024, there was a significant increase in DS diagnostics approaches, achieving a total of seven studies. This increase is a result of developments in AI algorithms, collaborations across diverse fields, and the significant role of AI in medical diagnosis. It demonstrates the increased academic and multidisciplinary interest in developing effective AI-powered DS detection models. In addition, an increasing trajectory highlights the importance of maintaining research efforts in order to overcome current challenges in implementing AI applications in the healthcare sector.

Dr. Aliza Apple, Ph.D. — VP, Catalyze360 AI/ML and Global Head, Lilly TuneLab, Eli Lilly

Accelerating Promising Biotech Innovation — Dr. Aliza Apple, Ph.D. — Vice President, Catalyze360 AI/ML and Global Head, Lilly TuneLab, Eli Lilly and Company.

Dr. Aliza Apple, Ph.D. is a Vice President of Catalyze360 AI (https://www.lilly.com/science/partners/catalyze-360 and Global Head of Lilly TuneLab (https://tunelab.lilly.com/) at Eli Lilly where she leads the strategy, build and launch of Lilly’s external-facing AI/ML efforts for drug discovery.

Lilly Catalyze360 represents a comprehensive approach to enabling the early-stage biotech ecosystem, agnostic of the therapeutic area, designed to accelerate emerging and promising science, strategically removing barriers to support biotech innovation.

In her previous role at Lilly, Dr. Apple served as the COO and head of Lilly Gateway Labs West Coast, where she supported the local biotech ecosystem through early engagement and providing tailored offerings to meet their needs.

Prior to Lilly, Dr. Apple served as a co-founder at Santa Ana Bio, a venture-backed precision biologics company focused on autoimmune disease, and as an advisor to the founders of Firefly Biologics.

Continue reading “Dr. Aliza Apple, Ph.D. — VP, Catalyze360 AI/ML and Global Head, Lilly TuneLab, Eli Lilly” | >

AI2 Incubator launches $80M fund as it doubles down on real-world AI applications in Seattle and beyond

The Seattle-based startup organization — known for spinning out companies at the intersection of AI and real-world applications — has closed an $80 million third fund to support about 70 new tech ventures over the next four years.

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