Latest News

A major update has been made to the way developers interact with Google’s Gemini models and agents. The Interactions API, which was launched in public beta last December, is now generally available and serves as the primary interface for these applications. This shift marks a significant change from the legacy generateContent API, although the latter will continue to be supported for the foreseeable future.

The Interactions API offers several key features that make it an attractive choice for developers building with Gemini models and agents. These include server-side state, background execution, tool combination, and multimodal generation capabilities. With this release, a stable schema has been established, and major new functionalities have been added in response to developer requests.

One of the most significant additions is Managed Agents, which allows for more efficient management of agent resources. Background execution enables long-running tasks to be performed without blocking other operations. Gemini Omni will also soon be integrated into the API. These features are designed to support stateful and agentic workflows, making them particularly well-suited for applications that require persistent interactions with users or environments.

The Interactions API is now the default interface across various Google AI tools, including Google AI Studio and the Gemini API. All documentation has been updated to reflect this change, although a toggle remains available to switch back to legacy format snippets. Developers are encouraged to use the Interactions API for new projects and applications, as it offers improved flexibility and efficiency.

For developers already building with supported partners such as LiteLLM, Eigent, or Agno, integrations for the Interactions API are readily available through these platforms. The gemini-interactions-api Skill has also been developed to inject best-practice patterns into agent development contexts, making it easier for agents to stay up-to-date with the latest API developments.

The Interactions API is accessible via Python and JavaScript SDKs. To get started, developers can obtain an API key from Google AI Studio and consult the comprehensive documentation provided by Google. A migration guide has been published to assist those transitioning from the legacy generateContent API, which maps every field to the new schema for a seamless transition.

Google continues to prioritize developer feedback in shaping the Interactions API’s future development. The company encourages developers to share their needs and suggestions on the developer forum, ensuring that the API remains responsive to its users’ requirements.

Fire detection is crucial in subway tunnels, where confined spaces and ventilation systems can complicate safety monitoring. Existing approaches often rely on classical machine learning methods that detect fires based on multivariate correlations but struggle with contextual reasoning.

However, recent studies suggest that Large Language Models (LLMs) may help address this challenge by translating structured sensor summaries into concise semantic descriptions. To examine the role of LLMs in fire detection, researchers have proposed a hybrid framework called HyFiD, which employs an LLM as a semantic feature extractor to augment classical classifiers.

HyFiD converts momentary multi-sensor readings (temperature, smoke, O2, CO, and CO2) into textual assessments of environmental states, generating semantic vectors that are fused with numerical features for supervised classifier training. Experiments on Fire Dynamics Simulator (FDS)-based subway scenarios show that the effect of LLM-derived semantic features is strongly dependent on the downstream classifier.

The results indicate a clear trade-off between detection sensitivity and premature-alarm suppression. Lower thresholds generally preserve higher F1 scores but substantially increase Pre-Alarm Rate, indicating more aggressive alarm behavior. For instance, at τ = 0.25 and k = 1.0 s, SVM + LLM and GBM + LLM achieve high F1 scores of 87.45% and 90.85%, respectively, but their PAR values rise to 99.25% and 51.32%. In contrast, higher thresholds suppress premature alarms more strongly but often degrade F1.

The temporal persistence window further modulates this trade-off. Increasing the persistence window from 1.0 to 2.0 s or 3.0 s generally reduces PAR, but excessive smoothing can substantially reduce F1, especially for SVM + LLM and RF + LLM. GBM + LLM is comparatively more robust under moderate smoothing: at τ = 0.50 and k = 2.0 s, it maintains an F1 score of 88.33% while reducing PAR from 47.55% under the default setting to 28.68%. However, further increasing the window to k = 3.0 s reduces F1 to 83.46%, showing that overly conservative alarm persistence can also suppress valid fire detections.

To justify the architectural design of using the LLM as a semantic feature extractor rather than as an end-to-end decision maker, researchers evaluated direct LLM classification under Zero-shot, One-shot, and Few-shot Chain-of-Thought (CoT) prompting strategies. The results show that direct prompting is highly sensitive to both the prompting setting and the LLM backbone when applied to raw multivariate numerical sensor snapshots.

The final alarm behavior is highly sensitive to the selected threshold and persistence window. Researchers retain τ = 0.50 and k = 1.0 s as the default setting for the main experiments because it provides a consistent operating point for comparing classifiers, while the sensitivity analysis clarifies how alternative alarm rules shift the balance between F1, delay, and pre-ignition false alarms.

Under the Zero-shot CoT setting, several evaluated backbones—including Llama-3-8B41, Qwen-2.5-3B58, Qwen-2.5-7B, and Ministral-8B59—produce near-zero Recall and F1, indicating difficulty in directly mapping raw numerical sensor values to stable fire/no-fire decisions. However, Few-shot CoT improves the strongest prompt-only result, with Llama-3-8B reaching an Accuracy of 65.67% and an F1 score of 70.32%. Nevertheless, this remains below the supervised hybrid approach: GBM + Llama-3-8B achieves an F1 score of 90.77% when the LLM is used as a semantic feature generator rather than as a standalone classifier.

These comparisons suggest that, in this setting, LLMs are more reliable when used to produce intermediate semantic representations for supervised downstream classifiers than when used as direct numerical classifiers. This finding has significant implications for machine learning jobs and AI-generated image applications, where accurate decision-making is critical.

New Taipei City, Taiwan-based Vecow Co., Ltd. has announced its participation in the recent AUTOMATE 2026 event held in Chicago. The company showcased its latest AI robotics platforms and deployment-ready solutions designed to accelerate industrial automation workflows.

San Diego-based Qualcomm Technologies has announced the selection of fifteen startups from Japan, Singapore, and South Korea to participate in its AI Program for Innovators (QAIPI) 2026. The program aims to empower these startups with cutting-edge technologies and resources to develop scalable edge AI solutions that address real-world challenges across various industries.

The integration of generative models like ChatGPT, Claude, and Gemini into daily work has brought about a pressing question in the content creation industry: how to maintain authentic voice. As professionals, students, and creators rely on these tools for assistance, humanization becomes increasingly important. AI Humanizer Tools have emerged as a solution to this problem, rewriting machine-generated text into language that reads naturally and avoids formulaic patterns.

Samsung Electronics is accelerating its adoption of artificial intelligence (AI) across the company by deploying ChatGPT Enterprise and Codex to its employees worldwide. The deployment, one of OpenAI's largest enterprise deployments to date, will make these tools available to all Samsung Electronics employees in Korea and those working in its Device eXperience (DX) division globally.

Epson Robots is set to preview its upcoming collaborative robot platform and demonstrate SafeSense technology at the upcoming Automate 2026 event. The exhibition, being held from June 22 to 25 at McCormick Place in Chicago, will give attendees their first look at Epson's latest innovation in automation solutions for plastics manufacturers.

Chicago will be the hub of industrial automation innovation this June as the Association for Advancing Automation (A3) hosts its annual conference, Automate 2026. The event brings together robotics, machine vision, motion control, and industrial AI communities to showcase the latest advancements in these fields.

Glean, a company that specializes in developing artificial intelligence (AI) assistants, has announced an expansion of its financial services ecosystem. The move is designed to help regulated industries such as banking and finance deploy AI agents and build workflows that address real operational needs. This initiative comes at a critical time for the industry, where firms are under pressure to improve productivity and compliance while managing data privacy and reliability risks.

A major expansion of Adobe's plan to integrate artificial intelligence (AI) into its Creative Cloud suite is now underway, with the company rolling out bespoke AI assistants to its most popular editing and design apps. The new chatbots are designed to automate tasks and provide a more intuitive way for users to work within Photoshop, Premiere, Illustrator, InDesign, and Frame.io.

Google DeepMind has released a comprehensive report titled 'From AGI to ASI,' written by Tim Genewein and 13 coauthors. The report explores four main technical pathways from Artificial General Intelligence (AGI) to Artificial Superintelligence (ASI), analyzing the potential bottlenecks that could slow or shape these paths.

Agentic artificial intelligence startup Convey Inc. has announced the closure of a significant funding round, securing $38 million in early-stage investment to scale up its platform for automating business tasks using AI teammates. The Series A round was led by Andreessen Horowitz and saw participation from Khosla Ventures and Pear VC.

Epic Games has released Unreal Engine 5.8, and among its many features is an experimental plugin that allows large language models (LLMs) to directly control Blueprints, assets, levels, and more within the engine's editor. This integration marks a significant step towards incorporating AI assistants into game development workflows.

Hospitals and health systems are being pushed to formalize oversight before artificial intelligence tools become embedded across clinical and operational workflows. The Joint Commission has launched a voluntary certification program focused on responsible AI use in healthcare organizations, which may force institutions to establish the basic infrastructure required for safe deployment.

A new smart speaker from Google is now available for pre-order, built specifically to work seamlessly with the company's Gemini voice assistant. The device, called Google Home Speaker, promises a more natural and intuitive way of interacting with your home devices, making it easier to control your surroundings without having to memorize rigid commands.

The Association for Advancing Automation (A3) is gearing up for its flagship event, Automate 2026, which will take place at McCormick Place in Chicago from June 22-25. The show promises to be the largest robotics and automation exhibition in North America, with over 1,000 exhibitors showcasing their latest developments in AI, robotics, and automation. More than 50,000 attendees are expected to flock to the event, making it a must-visit for anyone involved in the industry.

The rise of digital communication has made video the most influential form of expression. Businesses, educators, and creators rely on it to convey complex ideas, engage audiences, and market products. However, producing high-quality videos traditionally required expensive equipment, professional editing software, and years of technical expertise.

Travelers are increasingly turning to AI assistants for hotel discovery, shifting the rules of visibility in the process. Instead of typing keywords into a search engine, guests describe their specific needs and preferences, expecting the system to interpret them accurately. This change is significant because it creates new opportunities for hotels to appear in response to richer and more specific guest needs, rather than being grouped into broad categories.

A new player has emerged in the world of artificial intelligence (AI) adoption, particularly among large enterprises and critical institutions. Percepta, a General Catalyst Transformation Company, is helping these organizations achieve meaningful AI transformation by embedding its experts directly inside client organizations to redesign core operations with AI at their center.

The social media marketing landscape has undergone significant changes in the past year, driven by three key forces: the widespread adoption of AI-powered content recommendation, fragmentation of audience attention across multiple platforms, and the professionalization of the creator economy. Marketers who want to achieve competitive results must understand these shifts and adapt their strategies accordingly.