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Category: robotics/AI

AI makes a major breakthrough in a math problem that had stumped experts for decades

For nearly 80 years, mathematicians have struggled to solve a classic geometry puzzle first posed by Paul Erdős in 1946: the planar unit distance problem. The question posed by the legendary Hungarian mathematician was, on the surface, deceptively simple.

It asks: if you take a piece of paper and add some dots, how many pairs can be exactly the same distance apart? Erdős himself proposed that the maximum number grows only slightly faster than the number of dots. Although many mathematicians agreed with him, no one could find a way to mathematically prove it.

Smartphones may soon be able to track hidden objects using LiDAR

Modern smartphones are packed with incredible technology, from high-resolution cameras and advanced graphics chips to AI processors. In premium models, this hardware includes LiDAR (light detection and ranging), which helps power augmented reality features and improve depth sensing.

And that capability could soon be in for a seriously impressive upgrade. Researchers at the Massachusetts Institute of Technology (MIT) have developed an algorithm that lets a phone’s LiDAR sensor detect objects hidden around corners. Details are in a paper published in the journal Nature.

Typically, this type of non-line-of-sight (NLOS) capability is found in labs and relies on bulky, expensive research-grade hardware. But the team’s breakthrough makes it possible for consumer LiDAR sensors to peek behind obstacles.

New framework helps robots turn complex language into precise 3D actions

Over the past few decades, roboticists worldwide have introduced increasingly advanced robots that can understand human instructions, move in their surroundings and reliably complete basic manual tasks. While they perform well in some scenarios, many of these robots still struggle to translate the instructions of users into precise and executable actions that would allow them to successfully complete desired tasks.

Recently, computer scientists have been trying to improve how robots respond to user commands or queries using vision-language models (VLMs), artificial intelligence (AI) systems trained to process both images and texts. These models can typically interpret basic requests such as “place the bottle onto the plate,” yet they often do not exhibit the spatial reasoning capabilities required to interpret more elaborate instructions and translate them into executable actions in real-world settings.

Researchers at the Chinese University of Hong Kong, the Zhejiang Humanoid Robot Innovation Center Co. Ltd and other institutes recently introduced Retrieval-Augmented Manipulation (RAM), a framework that could improve the ability of robots to connect abstract instructions with three-dimensional (3D) representations of the space around them. The new framework, presented in a Science Robotics paper, was found to improve the spatial reasoning capabilities of robots, allowing them to reliably follow more elaborate instructions, without requiring task-specific training.

The Growing Cybersecurity Risks To The Supply Chain In The AI Era

#cybersecurity #suppychains #ai #tech

Supply chains are a primary target for cybercriminals and provide the foundation of global commerce in the hyper-connected digital ecosystem of today. Artificial intelligence (AI) simultaneously exacerbates vulnerabilities as it revolutionizes operations through predictive analytics, automation, and real-time visibility. Sophisticated threat actors, ransomware groups, and nation-state actors employ AI to exploit the vulnerable links in intricate, multi-tiered supply networks.

Artificial intelligence can create dual-use dynamics. It promotes efficiency by facilitating real-time data transfers and hyper-connected operations, while simultaneously significantly expanding the attack surface. Compromises of a single vendor or update have been shown to have a cascading effect on economies, governments, and critical infrastructure through supply chain attacks.

In The AI Era, Supply Chains Are Prime Targets.

The complexity of supply chains is inherent, as they encompass continents, jurisdictions, and a multitude of third-party vendors, contractors, and software components. Each link—whether it be legacy systems, unvetted code, IoT devices, or 5G-enabled connections—provides potential entry points. AI exacerbates these risks by allowing attackers to automate reconnaissance, create polymorphic malware that evades detection, create personalized phishing campaigns, and identify vulnerabilities quicker than defenders can apply patches.

AI-powered stretchable computing patch can run algorithms directly on the body

A new skin-like computing patch developed at the University of Chicago Pritzker School of Molecular Engineering (UChicago PME) can analyze health data using artificial intelligence in an unprecedented way. Unlike today’s wearable devices, it carries out its AI computations directly on the body, in mere milliseconds, without relying on a wireless connection.

While your current smartwatch may be able to track your heart rate or movements, it doesn’t analyze what it finds. The analysis happens elsewhere, after it shuttles data to an external server. In some situations—detecting ventricular fibrillation in the heart, for instance—that few-seconds lag to communicate with the server is too long.

The new device, designed and tested in collaboration with researchers at Argonne National Laboratory, was made possible by the development of manufacturing processes that allow organic electrochemical transistors to be printed onto flexible surfaces.

Novel origami pattern turns flat sheets into load-bearing 3D technology

McGill University researchers have discovered a new way to fold flat sheets into smooth, curved shells that can switch from floppy and flexible to stiff and load-bearing on demand. By designing a special origami pattern and threading cable-like elements through it, they can control the material’s final three-dimensional shape and how rigid it becomes.

The result, a “doubly curved lens box,” could advance the technology of such objects as temporary emergency tents, morphing robots and smart fabrics, the researchers said. “Smooth doubly curved origami shells with reprogrammable rigidity,” by Morad Mirzajanzadeh and Damiano Pasini, was published in Nature Communications.

“Existing foldable structures face a trade-off: if they are smooth and nicely curved, they tend to be soft and floppy; if they are strong and stiff, they usually look faceted, jagged or uncomfortable, and their shape is hard to tune once built,” said Damiano Pasini, study co-author and professor of mechanical engineering.

Geordie Rose: Machine Learning is Progressing Faster Than You Think

“Machine learning is progressing faster than you think.”

Geordie Rose said that to me in 2013.

Back then, it sounded like the kind of thing a quantum computing CEO says to drum up attention. Today it reads like a weather report.

Thirteen years ago, the D-Wave founder and CTO sat down with me for over two hours and laid out a thesis most observers found extreme: machine learning would become broadly available far faster than anyone hoped, and quantum computers would help us build AI by 2029.

The 2029 date sounded like science fiction.

It does not sound like science fiction anymore.

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