Apple announces Siri AI overhaul with Google Gemini-derived model at WWDC 2026
Apple announced a redesigned Siri AI at WWDC 2026, powered by a custom Gemini-derived model from Google that runs on Apple's Private Cloud Compute infrastructure. The company also introduced Core AI, a new library that integrates with PyTorch to enable developers to deploy models on Apple hardware.
Apple announces Siri AI overhaul with Google Gemini-derived model at WWDC 2026
Apple announced a redesigned Siri AI at WWDC 2026, according to the company, powered by a custom Gemini-derived model licensed from Google. The model runs on Apple's Private Cloud Compute infrastructure rather than on-device.
Vision LLM integration
According to Apple, the new Siri will use vision language models to extract information directly from users' screens. This approach eliminates the need for individual applications to ship custom integration code for Apple Intelligence, a significant architectural shift from the 2024 Apple Intelligence announcement.
Vision LLMs have matured substantially since June 2024, when Apple first announced Apple Intelligence features that failed to deliver as promised.
Core AI developer library
Apple introduced Core AI, a new library that bridges PyTorch models to Apple hardware. The library includes Core AI PyTorch Extensions (coreai-torch), a Python package that converts PyTorch models exported as torch.export.ExportedProgram into Core AI AIProgram objects.
According to Apple's documentation, the library "traverses the FX graph node-by-node and maps ATen operators to Core AI operations," enabling developers to run existing PyTorch models on Apple silicon without complete rewrites.
Availability and waitlist
iOS 27 Developer Beta is available as of June 8, 2026, but access to the new Siri AI features requires joining a waitlist. Aaron Perris from MacRumors reports gaining access after the waitlist period, suggesting initial rollout has begun.
No pricing information for Private Cloud Compute access has been disclosed. Apple has not specified whether the Gemini-derived model is a custom-trained version or a fine-tuned variant of an existing Gemini model.
What this means
Apple's partnership with Google for a Gemini-derived model represents a significant strategic shift, moving away from purely in-house AI development after the 2024 Apple Intelligence shortcomings. The vision LLM approach for screen context is technically sound and avoids the fragmentation that would come from requiring per-app integrations. However, given Apple's track record with the 2024 announcements, actual performance will need independent verification once the waitlist clears and the features reach broader testing.
Related Articles
Google prepares voice customization for Gemini with speed, energy, formality, and warmth controls
Google is preparing to let users customize Gemini's voice output across four parameters: speed, energy, formality, and warmth, according to code discovered in the Google app 17.41.12 beta. The controls will apply to both Gemini Live and standard chat interactions.
Google renames NotebookLM to Gemini Notebook, adds code execution for Ultra and Workspace users
Google is renaming its AI note-taking app NotebookLM to Gemini Notebook while keeping it as a standalone app. The company is also rolling out code execution capabilities to Google AI Ultra and Workspace business customers, with Pro user access coming in the following weeks.
Google Rebrands NotebookLM to Gemini Notebook, Brings Gemini 3.5 and Antigravity to AI Pro
Google renamed NotebookLM to Gemini Notebook and announced that the Gemini 3.5 model with Antigravity code execution capability will roll out to AI Pro subscribers in the coming weeks. The research tool now has over 30 million users and 600,000+ organizations.
NASA Runs Google's Gemma 3 Aboard Orbiting Satellite for On-Board Image Analysis
NASA's Jet Propulsion Laboratory ran a 4-bit quantized version of Google's Gemma 3 4B on board a Loft Orbital YAM-9 satellite, marking the first in-orbit demonstration of a vision-language model analyzing imagery from a satellite's own sensor. The unmodified open-weights model hit 88 percent accuracy on a 7,960-image benchmark and ran live capture tests over Toulouse, France and coastal Argentina.
Comments
Loading...