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On-Device Speech Recognition and Text-to-Speech in Google AI Edge Gallery
Google AI Edge Gallery now supports on-device, offline Automatic Speech Recognition (ASR) and Text-to-Speech (TTS) capabilities for building developer-friendly voice applications. Developers can leverage ML Kit for optimized Android performance, or use MediaPipe Tasks and LiteRT for cross-platform open-weight model integration. This local pipeline seamlessly pairs lightweight speech models with on-device small language models like Gemini Nano and Gemma 4…
Google releases Gemini 4 Carbon with agent ‘swarm’ feature
Google is rolling out its latest AI model, Gemini 4 Carbon, to the general public after releasing it to a select group of "cybersecurity defenders"…
Google broadens its AI hardware portfolio with two new specialized chips
Google's new tensor processing units (TPUs) broaden its chip portfolio to more use cases: confidential model training and more memory capacity.
We’re bringing Adobe’s creative and productivity tools closer to your everyday Google apps.
Google and Adobe are integrating creative and productivity tools. Edit PDFs in Drive and use Adobe AI in Gemini. See what’s new in this partnership.
After challenging Google’s Android with Cyanogen, its co-founder takes aim at Windows for AI era
Kora is set to launch as a free-to-use alternative to Windows, targeting users who rely heavily on AI models and agents.
Google Cloud launches Gemini for healthcare.
Google Cloud launches Gemini for healthcare to unify clinical data and automate workflows. Reduce administrative burnout. See how it works.
Introducing AlphaProof Code: a breakthrough in verifying complex software at depth
AlphaProof Code agents wrote three million lines of proofs in Lean to verify Signal’s core encryption.
How skills and agents work in AWS Quick
Repeatable tasks like status updates, release notes, and ticket creation can be encoded in AWS Quick through agents and skills. This post covers how agents, skills, and your data fit together in AWS Quick, how to build and share skills for your team, and how the official catalog and custom agents help you use them.
Announcing Long-Context Training on TPUs with MaxText Context Parallelism
Google Cloud's MaxText framework now supports long-context training up to 14 million tokens on Cloud TPUs by implementing four context-parallel strategies: All-gather, A2A, P2P, and USP. Powered by the Tokamax Splash Attention kernel, these strategies effectively manage attention compute and memory bottlenecks without materializing full key-value sequences in high-bandwidth memory. Developers can adopt these capabilities in MaxText v0.2.4 to achieve…
Gemini Robotics 2.5 accelerates real-world general dexterity
Our most capable vision-language-action model enhances robot dexterity, reliability, and safety
Apple’s $50 Intelligence Usage Packs extend access to some AI features
After publishing a support article in early September outlining usage limits for some Apple Intelligence features, the company has updated Logic Pro with an option…
From prompt to app: Build with Google Maps in AI Studio
Developers can now build applications powered by Google Maps Platform directly within Google AI Studio by prompting Gemini with real-world geospatial concepts. The integration eliminates traditional configuration hurdles by providing one-click demo keys that generate instantly without requiring Cloud Console redirects or credit cards. This setup grants developers immediate access to core APIs—including Maps, Places, and Routes—supported by daily sandbox…
Hold up — I had a great time talking to the RIAA guy
Today, I’m talking with Mitch Glazier, who is CEO and chairman of the RIAA. This conversation was a real full-circle moment for me: I was…
AuDiR: Diagnosing Audio Reasoning with Rule and Percept Interventions
Answer accuracy alone does not distinguish failures in acoustic perception, rule knowledge, and rule application. AuDiR (Audio Diagnostic Reasoning) examines these distinctions through controlled input interventions. Each item contains an audio clip, a multiple-choice question, a task-general rule, and a textual description of the acoustic observations needed to answer. Three conditions progressively supply the rule and observations; a fourth removes the audio while retaining the question and rule. The first correct diagnostic condition assigns an operational response label, without identifying a unique internal cause. The current benchmark contains 201 items in 18 families drawn from 9 sources, with answer references documented separately from percept extraction. In a preliminary evaluation of Gemini-3.8-flash, accuracy is 29.3% without supplied information, 31.3% with the rule, and 98.5% with the observations also supplied. No-audio accuracy is 23.9%, with a confidence interval containing the 25.0% chance rate. A family-level knowledge probe associates incomplete rule recall with larger gains from rule provision. These results show the benefit of supplying acoustic observations as text, while incomplete validation, possible recognition shortcuts, and non-monotonic responses limit stronger failure-attribution claims.