Sakana AI, a Tokyo-based artificial intelligence startup founded by two prominent former Google researchers, released AI models on Wednesday it said were built using a novel method inspired by evolution, akin to breeding and natural selection.
Category: robotics/AI – Page 644
Privacy in the AI era: How do we protect our personal information?
The AI boom, including the advent of large language models (LLMs) and their associated chatbots, poses new challenges for privacy. Is our personal information part of a model’s training data? Are our prompts being shared with law enforcement? Will chatbots connect diverse threads from our online lives and output them to anyone?
Introducing Floorlocator, a system that enhances indoor navigation
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.
New survey on deep learning solutions for cellular traffic prediction
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.
Microsoft Introduces AutoDev: A Fully Automated Artificial Intelligence-Driven Software Development Framework
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.
Flying drone can roll on the ground to save energy over long distances
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.
Sam Altman on when AGI will be created | Lex Fridman Podcast
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Machine learning tools can predict emotion in voices in just over a second
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.”