Hume AI's CEO, Alan Cowen, will join Google DeepMind along with several top engineers as part of a major licensing deal. Google DeepMind is hiring the CEO and several top engineers from Hume AI, a startup working on emotionally intelligent voice interfaces, as part of a new licensing agreement, WIRED has learned. Financial details of the deal are confidential, but Hume AI says the company will continue to supply its technology to other frontier AI labs. The deal is the latest sign that AI companies expect voice mode to become an increasingly important interface for interacting with customers--and that understanding a user's emotions and mood based on their voice interactions is key. Hume AI expects to bring in $100 million in revenue in 2026 as it works with AI labs on tuning AI models to be more capable and useful voice helpers, says John Beadle, cofounder and managing partner of AEGIS Ventures, which invested in Hume AI.

Recent progress in mixed reality (MR) and robotics is enabling increasingly sophisticated forms of human-robot collaboration. Building on these developments, we introduce a novel MR framework that allows multiple quadruped robots to operate in semantically diverse environments via a MR interface. Our system supports collaborative tasks involving drawers, swing doors, and higher-level infrastructure such as light switches. A comprehensive user study verifies both the design and usability of our app, with participants giving a "good" or "very good" rating in almost all cases. Overall, our approach provides an effective and intuitive framework for MR-based multi-robot collaboration in complex, real-world scenarios.

In this study, we propose a solution based on a multi-agent LLM architecture and a voice user interface (VUI) designed to update the knowledge base of a digital assistant. Its usability is evaluated in comparison to a more traditional graphical content management system (CMS), with a focus on understanding the relationship between user preferences and the complexity of the information being provided. The findings demonstrate that, while the overall usability of the VUI is rated lower than the graphical interface, it is already preferred by users for less complex tasks. Furthermore, the quality of content entered through the VUI is comparable to that achieved with the graphical interface, even for highly complex tasks. Obtained qualitative results suggest that a hybrid interface combining the strengths of both approaches could address the key challenges identified during the experiment, such as reducing cognitive load through graphical feedback while maintaining the intuitive nature of voice-based interactions. This work highlights the potential of conversational interfaces as a viable and effective method for knowledge management in specific business contexts.

Small and medium sized businesses often struggle with data driven decision making do to a lack of advanced analytics tools, especially in African countries where they make up a majority of the workforce. Though many tools exist they are not designed to fit into the ways of working of SMB workers who are mobile first, have limited time to learn new workflows, and for whom social and business are tightly coupled. To address this, the Dukawalla prototype was created. This intelligent assistant bridges the gap between raw business data, and actionable insights by leveraging voice interaction and the power of generative AI. Dukawalla provides an intuitive way for business owners to interact with their data, aiding in informed decision making. This paper examines Dukawalla's deployment across SMBs in Nairobi, focusing on their experiences using this voice based assistant to streamline data collection and provide business insights

A new "empathic voice interface" launched today by Hume AI, a New York–based startup, makes it possible to add a range of emotionally expressive voices, plus an emotionally attuned ear, to large language models from Anthropic, Google, Meta, Mistral, and OpenAI--portending an era when AI helpers may more routinely get all gushy on us. "We specialize in building empathic personalities that speak in ways people would speak, rather than stereotypes of AI assistants," says Hume AI cofounder Alan Cowen, a psychologist who has coauthored a number of research papers on AI and emotion, and who previously worked on emotional technologies at Google and Facebook. WIRED tested Hume's latest voice technology, called EVI 2 and found its output to be similar to that developed by OpenAI for ChatGPT. Later, a real movie star, Scarlett Johansson, claimed OpenAI had ripped off her voice.) Like ChatGPT, Hume is far more emotionally expressive than most conventional voice interfaces. If you tell it that your pet has died, for example, it will adopt a suitable somber and sympathetic tone.

Siri and Alexa were only the beginning. As voice recognition and speech synthesis technologies continue to mature, the days of typing on keyboards to interact with the digital world around us could be coming to an end -- and sooner than many of us anticipated. Where today's virtual assistants exist on our mobile devices and desktops to provide scripted answers to specific questions, the LLM-powered generative AI copilots of tomorrow will be there, and everywhere else too. This is the "voice-first" future Tobias Dengel envisions in his new book, The Sound of the Future: The Coming Age of Voice Technology. Using a wide-ranging set of examples, and applications in everything from marketing, sales and customer service to manufacturing and logistics, Dengel walks the reader through how voice technologies can revolutionize the ways in which we interact with the digital world.

Robots have become ubiquitous tools in various industries and households, highlighting the importance of human-robot interaction (HRI). This has increased the need for easy and accessible communication between humans and robots. Recent research has focused on the intersection of virtual assistant technology, such as Amazon's Alexa, with robots and its effect on HRI. This paper presents the Virtual Assistant, Human, and Robots in the loop (VAHR) system, which utilizes bidirectional communication to control multiple robots through Alexa. VAHR's performance was evaluated through a human-subjects experiment, comparing objective and subjective metrics of traditional keyboard and mouse interfaces to VAHR. The results showed that VAHR required 41% less Robot Attention Demand and ensured 91% more Fan-out time compared to the standard method. Additionally, VAHR led to a 62.5% improvement in multi-tasking, highlighting the potential for efficient human-robot interaction in physically- and mentally-demanding scenarios. However, subjective metrics revealed a need for human operators to build confidence and trust with this new method of operation.

How conversational AI has evolved, and why it is rapidly redefining how customers interact with businesses. Imagine if customers could talk to a business whenever they need something, not just during working hours, not just when a human is available, and not just through protracted processes. People have conversations in person or via text, phone, video chats--the obstacles have never been lower. But when it comes to interacting with a business, points of friction are more numerous, with customers often forced to peruse bespoke websites and apps, deal with phone trees, wait for email responses, or accommodate the limited availability of human staff during business hours. That needn't be the case: it's time for organizations to reevaluate the role of conversational AI in customer experiences.

Recently, the popularity and adoption of artificial intelligence (AI) and machine learning (ML) into various business processes has been actively growing. This is evidenced, for example, by the steadily increasing media coverage, which indicates an increasing relevance of the technology. A growing number of application practices confirm the fact: ICT.Moscow database, in 2021 alone, collected more than a hundred of them. However, the use of AI in business processes is accompanied by a rather significant stop factor: in order to effectively use ML algorithms to solve problems, you need to be a specialist in ML and AI. This problem can be solved in various ways. For example, Cornell University in the United States is developing a platform with a "transfer learning" approach that allows people without special skills to use ML algorithms. Is the field of AI on the verge of a tipping point, when, thanks to such specialized interfaces, ML will in fact become a publicly available tool that does not require deep specialized knowledge?

Integrating voice commands into a Java application has been a traditionally daunting task. While JDK provides a Speech API, it is unfortunately only an interface to a collection of outdated products and third-party cloud providers. Let's leave all this behind and add some modern, offline speech recognition to our Java application. By keeping it offline we can ensure our user's data is kept on-device, thereby prioritizing their privacy and security. The Picovoice SDK provides a class that encapsulates both the Porcupine wake word engine and the Rhino Speech-to-Intent engine.