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Category: robotics/AI – Page 398
AI model 98% accurate in detecting diseases — just by looking at your tongue
“These results confirm that computerized tongue analysis is a secure, efficient, user-friendly and affordable method for disease screening that backs up modern methods with a centuries-old practice,”
This technology could be aah-mazing!
Researchers in Iraq and Australia say they have developed a computer algorithm that can analyze the color of a person’s tongue to detect their medical condition in real time — with 98% accuracy.
“Typically, people with diabetes have a yellow tongue; cancer patients a purple tongue with a thick greasy coating; and acute stroke patients present with an unusually shaped red tongue,” explained senior study author Ali Al-Naji, who teaches at Middle Technical University in Baghdad and the University of South Australia.
Agent Q: Breakthrough AI Research in Self-Healing Web Agents
Introducing Agent Q: Research Breakthrough for the Next Generation of AI Agents with Planning & Self Healing Capabilities.
In recent years, the…
Conclusion
MultiOn’s Agent Q sets a new major milestone for autonomous web agents, combining advanced search techniques, AI self-critique, and reinforcement learning to overcome current limitations, representing a substantial leap forward in autonomous agents capabilities. As we continue to refine these methods and address associated challenges, moving closer to a full release in our products, the future of intelligent autonomous web agents in the real world looks promising.
DARPA Aims to Ditch C Code, Move to Rust
The US military agency responsible for developing new technologies plans to embark on an effort to rewrite significant volumes of C code by funding a new research challenge to create an automated translator capable of converting old C code with function written in the security-focused Rust language.
The Defense Advanced Research Projects Agency (DARPA) will hold a workshop, known as Proposers Day, on Aug. 26 to outline its vision for the Translating All C to Rust (TRACTOR) project. The effort calls for academic and industry research groups to compete to create a system that can turn C code into idiomatic — that is, using native features — Rust code. The project’s ultimate goal is to provide tools so that any organization with large volumes of software written in C can convert that code to Rust and eliminate the memory-safety errors that account for a large source of software vulnerabilities.
Without an automated system, developers are unlikely to take on the task, says Dan Wallach, program manager in DARPA’s Information Innovation Office (I2O).
Researchers create new method for orchestrating successful collaboration among robots
New research from the University of Massachusetts Amherst shows that programming robots to create their own teams and voluntarily wait for their teammates results in faster task completion, with the potential to improve manufacturing, agriculture and warehouse automation. The study is published in 2024 IEEE International Conference on Robotics and Automation (ICRA).
This research was recognized as a finalist for Best Paper Award on Multi-Robot Systems at the IEEE International Conference on Robotics and Automation 2024.
“There’s a long history of debate on whether we want to build a single, powerful humanoid robot that can do all the jobs, or we have a team of robots that can collaborate,” says one of the study authors, Hao Zhang, associate professor at the UMass Amherst Manning College of Information and Computer Sciences and director of the Human-Centered Robotics Lab.
AMD acquires Silo AI for $665 million in cash, ‘AI is our number one strategic priority’
$665 million in cash is nothing to sneeze at, and for AMD, the acquisition is the latest step in the company’s broader pivot that puts its main focus on AI and AI-related technologies. This is nothing new; we’ve seen the same shift happen in other companies like Google, Meta, Apple, and, of course, NVIDIA. However, NVIDIA’s AI focus started many years ago.
“AI is our number one strategic priority,” said Vamsi Boppana, AMD senior vice president, AIG. “We continue to invest in both the talent and software capabilities to support our growing customer deployments and roadmaps.”
“The Silo AI team has developed state-of-the-art language models that have been trained at scale on AMD Instinct accelerators, and they have broad experience developing and integrating AI models to solve critical problems for end customers,” Vamsi Boppana adds. “We expect their expertise and software capabilities will directly improve the experience for customers in delivering the best performing AI solutions on AMD platforms.”
