Lifeboat Foundation

Researchers have proposed a new strategy for the shape assembly of robot swarms based on the idea of mean-shift exploration: When a robot is surrounded by neighboring robots and unoccupied locations, it actively gives up its current location by exploring the highest density of nearby unoccupied locations in the desired shape.

The study, titled, “Mean-shift exploration in shape assembly of robot swarms,” has been published in Nature Communications.

This idea is realized by adapting the mean-shift algorithm, an optimization technique widely used in machine learning for locating the maxima of a density function.

An explainable deep learning model using a chemical substructure-based approach for the exploration of chemical compound libraries identified structural classes of compounds with antibiotic activity and low toxicity.

The woke Goody-2 chatbot’s guardrails are so geared towards safety, it won’t answer any question fully — but it’ll definitely tell you why.

An RIT engineering researcher bridges biology and computing to advance innovative neural network processing and storage using DNA molecules.

The automotive industry has experienced rapid advancements due to the integration of edge computing and artificial intelligence (AI) in recent years. As vehicles continue developing self-driving capabilities, these technologies have become increasingly critical for effective decision-making and real-time reactions.

Edge computing processes data and commands locally within a vehicle’s systems, improving road safety and transportation efficiency. Combined with 5G, it enables real-time communication between vehicles and infrastructure, reducing latency and allowing autonomous vehicles to respond faster. AI algorithms enable cars to interpret visual data and make human-like driving decisions.

Edge computing and AI are transforming vehicles into true self-driving machines, filling any gaps in low-latency 5G tech and enabling companies to pioneer advanced autonomy.

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Some experts claim that there is no current evidence that AI can be controlled safely. And if so, should it even be developed?

AI Safety expert Dr. Roman V. Yampolskiy explains in his book “AI: Unexplainable, Unpredictable, Uncontrollable” that the problem of AI control is one of the most important problems facing humanity, but even so it remains poorly understood, poorly defined, and poorly researched.

An air traffic controller’s routine can be disrupted by an aircraft that requires special handling. This could range from an emergency to priority handling of medical flights or Air Force One. Controllers are given the responsibility and the flexibility to adapt how they manage their airspace.

The requirements for the front line of air traffic control are a poor match for AI’s capabilities. People expect air traffic to continue to be the safest complex, high-technology system ever. It achieves this standard by adhering to procedures when practical, which is something AI can do, and by adapting and exercising good judgment whenever something unplanned occurs or a new operation is implemented – a notable weakness of today’s AI.

Indeed, it is when conditions are the worst – when controllers figure out how to handle aircraft with severe problems, airport crises or widespread airspace closures due to security concerns or infrastructure failures – that controllers’ contributions to safety are the greatest.

The robot’s quadruped locomotion helps it look for hazards in cramped and cluttered experiment spaces inaccessible to other robots.

Summary: Dr. Roman V. Yampolskiy, an AI Safety expert, warns of the unprecedented risks associated with artificial intelligence in his forthcoming book, AI: Unexplainable, Unpredictable, Uncontrollable. Through an extensive review, Yampolskiy reveals a lack of evidence proving AI can be safely controlled, pointing out the potential for AI to cause existential catastrophes.

He argues that the inherent unpredictability and advanced autonomy of AI systems pose significant challenges to ensuring their safety and alignment with human values. The book emphasizes the urgent need for increased research and development in AI safety measures to mitigate these risks, advocating for a balanced approach that prioritizes human control and understanding.