Category: AI

When humans communicate through writing—whether by email, on social media, or in casual conversation—we often imply more than we say outright. Beneath the surface of our words lies latent meaning: subtext, emotion, intent, and even political bias. Traditionally, we rely on the reader to interpret this subtext. But what happens when the reader is not a person, but an artificial intelligence system?

As conversational AI becomes more advanced, researchers are beginning to explore whether these systems can grasp what’s left unsaid. The emerging field of latent content analysis focuses on uncovering deeper meanings and subtle cues in text, including emotional tone, sarcasm, and ideological leanings. This kind of analysis is important across many domains—from mental health and public safety to customer service and journalism.

Visitors to the Palace of Versailles can now engage in real-time conversations with its iconic garden statues, thanks to a new collaboration with artificial intelligence companies OpenAI and French startup Ask Mona. The initiative replaces traditional audio guides with interactive, multilingual AI experiences designed to deepen engagement with the historic site.

The experience is simple: visitors scan a QR code next to one of around 20 statues throughout the gardens. This launches an AI-powered conversation available in French, English, or Spanish, offering historical insights and stories tied to the statues and the palace’s rich heritage.

Breaking new ground in data transmission, Europe has achieved a record-setting 1.2 terabit-per-second quantum-safe data transfer over 2,175 miles (3,500 kilometers). This milestone was made possible through a collaborative effort by CSC – IT Center for Science (Finland), SURF (Netherlands), and Nokia, showcasing the future of secure, high-speed, cross-border research connectivity.

The successful trial connected Amsterdam and Kajaani using quantum-safe, high-capacity fibre-optic infrastructure. The data—both real and synthetic—was transferred directly disk-to-disk across five operational research and education networks: NORDUnet (Nordic backbone), Sunet (Sweden), SIKT (Norway), and Funet (CSC’s Finnish network). This trial not only tested the technical feasibility but also laid a foundation for operational deployment.

South Korea’s Electronics and Telecommunications Research Institute (ETRI) has developed advanced AI technology capable of generating hyper-realistic avatars that can speak and interact naturally using only a single portrait photo. This breakthrough is positioned as a next-generation interface for intuitive communication between humans and machines, particularly in the context of fully autonomous vehicles.

Unlike conventional AI assistants, which typically follow scripted commands with minimal facial movement, ETRI’s avatars feature detailed facial expressions and precise mouth synchronization. This enables the avatars to engage in lifelike, conversational interactions, transforming the experience of in-vehicle communication. The avatars can simulate realistic communication not only with drivers but also with pedestrians, supporting more natural and human-centered dialogue.

Artificial intelligence is often associated with heavy computing demands and high energy consumption—obstacles that limit its use in the Internet of Things (IoT), where sensors and devices typically run on minimal power and processing capabilities. However, researchers from the E-MINDS project have developed methods to make AI efficient enough to run on extremely limited hardware, paving the way for smarter, low-power applications in industry and beyond.

A joint initiative involving the COMET K1 center Pro2Future, Graz University of Technology (TU Graz), and the University of St. Gallen, the E-MINDS project has demonstrated how specialized AI models can operate locally on devices with just 4 kilobytes of memory. These models are able to perform tasks such as identifying sources of interference in ultra-wideband (UWB) localization systems, without relying on cloud computing or external processors.

Forest fire prevention and control agencies in São Carlos, a city in the interior of São Paulo, Brazil, will soon benefit from advanced aerial technology to detect and combat fires more rapidly.

Researchers at the São Carlos Engineering School of the University of São Paulo (EESC-USP) are developing drones equipped with gas sensors and artificial intelligence to identify forest fires in their early stages. The project was introduced during the aeronautics session of FAPESP Week Toulouse, held from June 10 to 12 in Toulouse, southern France.

Researchers developed a new way to identify how diseases might cause or influence one another by analyzing scientific literature and validating the results using real-world patient data. They searched through PubMed articles for phrases suggesting that one disease leads to another, then standardized those disease names using ICD-10-CM medical codes to keep the data consistent.

To test whether these suggested relationships were credible, the team used a combination of five validation methods. They looked at how strongly diseases were statistically linked in the UK Biobank dataset, whether the timing of diagnoses followed the expected pattern (with the “cause” usually diagnosed before the “effect”), and how frequently the relationships appeared in the literature. They also tested how dependent the diseases were on each other and asked GPT-4, a powerful AI language model, to assess the plausibility of each connection. All of this information was combined into a confidence score for each relationship.

Chatbots like ChatGPT have made it easy for users to access complex information through simple questions. The same principle is now being applied to one of biology’s most complex challenges: protein design. Traditionally, creating custom proteins required deep technical expertise and reliance on naturally evolved templates. But scientists are now building AI models that can generate novel proteins from plain English prompts, much like asking ChatGPT for a summary or essay.

Enter Pinal, a new AI designed to act as a conversational protein engineer. Developed by an international team of researchers, Pinal allows scientists to describe the desired type, function, or structure of a protein in natural language. In response, the AI generates candidate proteins that can be tested in living cells. In one demonstration, Pinal successfully designed enzymes that broke down alcohol, some even functioning at high temperatures.