Deepgram’s new Aura software turns text into generative audio with a “human-like voice.” The 9-year-old voice recognition company has raised nearly $86 million to date on the strength of its Voice AI platform. Aura is an extremely low-latency text-to-speech voice AI that can be used for voice AI agents, the company says. Paired with Deepgram’s Nova-2 speech-to-text API, developers can use it to “easily (and quickly) exchange real-time information between humans and LLMs to build responsive, high-throughput AI agents and conversational AI applications,” according to Deepgram. Continue reading Deepgram’s Speech Portfolio Now Includes Human-Like Aura

Bill Gates has published his thinking about the future of computing, and fascinatingly, it’s the same as his prediction from decades ago: agents. No mere bots — and certainly not anthropomorphized paperclips — agents (to Gates) will abstract almost all HCI to a natural language conversation with systems that have our permission to take meaningful actions. Gates makes a highly specific prediction: within five years, the very idea of an app itself will seem as outdated as a rotary phone dial does next to an iPhone. A conversational UI will sit on top of a language model that has access to as much of our private data as we wish to give it. Continue reading Bill Gates Imagines Agents as the Human-Computer Interface

Nvidia Research has debuted Eureka, an AI agent that autonomously teaches robots complex motor skills. Powered by OpenAI’s GPT-4, Eureka has successfully trained a robotic hand to handle a pen with the dexterity of a human — a first, according to Nvidia. Eureka has also enabled robots to do things like open drawers, manipulate scissors and toss and catch balls, along with dozens of other tasks. “Eureka is a first step toward developing new algorithms that integrate generative and reinforcement learning methods to solve hard tasks,” according to Nvidia Senior Director of AI Research Anima Anandkumar said. Continue reading Nvidia Leverages OpenAI’s GPT-4 to Train Dexterous Robots

Google’s DeepMind division has improved the speed and performance of its machine learning system with technology whose attributes are similar to how animals are thought to dream. Dubbed “Unreal” (Unsupervised Reinforcement and Auxiliary Learning), the system learned to complete Labyrinth, a 3D maze, ten times faster than the best existing artificial intelligence software and can now play up to 87 percent of expert human players’ performance. DeepMind researchers will now be able to try out new ideas much more quickly. Continue reading Google DeepMind Speeds AI Learning with Computer Dreams