Indoor positioning is transforming with applications demanding precise location tracking. Traditional methods, including fingerprinting and sensor-based techniques, though widely used, face significant drawbacks, such as the need for extensive training data, poor scalability, and reliance on additional sensor information. Recent advancements have sought to leverage deep learning, yet issues such as low scalability and high computational costs remain unaddressed.
The bustling streets of a modern city are filled with countless individuals using their smartphones for streaming videos, sending messages and browsing the web. In the era of rapidly expanding 5G networks and the omnipresence of mobile devices, the management of cellular traffic has become increasingly complex.
The software development sector stands at the dawn of a transformation powered by artificial intelligence (AI), where AI agents perform development tasks. This transformation is not just about incremental enhancements but a radical reimagining of how software engineering tasks are approached, executed, and delivered. Central to this shift is introducing AI-driven frameworks that transcend traditional code assistance tools, marking a leap toward more autonomous, efficient, and secure software development methodologies.
The integration of AI in software development has been confined largely to providing code suggestions and aiding in file manipulation. This approach, while beneficial, barely scratches the surface of what is technologically feasible. AI-powered tools operate within a constrained scope, missing out on Integrated Development Environments (IDEs)’ vast capabilities, such as comprehensive code building, testing, and version control operations. This limitation underscores a critical gap in the software development toolkit, where the potential for AI to contribute more profoundly to the development lifecycle remains largely untapped.
Microsoft researchers present AutoDev, which empowers AI agents to tackle a broad spectrum of software engineering tasks autonomously, from intricate code editing and comprehensive testing to advanced git operations. This framework is designed to focus on autonomy, efficiency, and security. By housing operations within Docker containers, AutoDev ensures that development processes are streamlined and secure, safeguarding user privacy and project integrity through meticulously designed guardrails.
Researchers claim to have combined the benefits of rolling robots with those of flying drones by creating a device that rotates along the ground but hops over obstacles.
Lex Fridman Podcast full episode: https://www.youtube.com/watch?v=jvqFAi7vkBcPlease support this podcast by checking out our sponsors:- Cloaked: https://cloa…
Psychology studies have demonstrated that by the age of 4–5, young children have developed intricate visual models of the world around them. These internal visual models allow them to outperform advanced computer vision techniques on various object recognition tasks.
Words are important to express ourselves. What we don’t say, however, may be even more instrumental in conveying emotions. Humans can often tell how people around them feel through non-verbal cues embedded in our voice.
Now, researchers in Germany have sought to find out if technical tools, too, can accurately predict emotional undertones in fragments of voice recordings. To do so, they compared three ML models’ accuracy to recognize diverse emotions in audio excepts. Their results were published in Frontiers in Psychology.
“Here we show that machine learning can be used to recognize emotions from audio clips as short as 1.5 seconds,” said the article’s first author Hannes Diemerling, a researcher at the Center for Lifespan Psychology at the Max Planck Institute for Human Development. “Our models achieved an accuracy similar to humans when categorizing meaningless sentences with emotional coloring spoken by actors.”
Whether it’s a powered prosthesis to assist a person who has lost a limb or an independent robot navigating the outside world, we are asking machines to perform increasingly complex, dynamic tasks. But the standard electric motor was designed for steady, ongoing activities like running a compressor or spinning a conveyor belt—even updated designs waste a lot of energy when making more complicated movements.
Researchers at Stanford University have invented a way to augment electric motors to make them much more efficient at performing dynamic movements through a new type of actuator, a device that uses energy to make things move. Their actuator, published in Science Robotics, uses springs and clutches to accomplish a variety of tasks with a fraction of the energy usage of a typical electric motor.
“Rather than wasting lots of electricity to just sit there humming away and generating heat, our actuator uses these clutches to achieve the very high levels of efficiency that we see from electric motors in continuous processes, without giving up on controllability and other features that make electric motors attractive,” said Steve Collins, associate professor of mechanical engineering and senior author of the paper.
Robotic exoskeletons designed to help humans with walking or physically demanding work have been the stuff of sci-fi lore for decades. Remember Ellen Ripley in that Power Loader in “Alien”? Or the crazy mobile platform George McFly wore in 2015 in “Back to the Future, Part II” because he threw his back out?
Last Sunday, Liverpool faced Manchester United in the quarter finals of the FA Cup—and in the final minute of extra time, with the score tied at three-all, Liverpool had the crucial opportunity of a corner kick. A goal would surely mean victory, but losing possession could be risky.
