MAROKO133 Hot ai: Developers can now add live Google Maps data to Gemini-powered AI app ou

📌 MAROKO133 Breaking ai: Developers can now add live Google Maps data to Gemini-po

Google is adding a new feature for third-party developers building atop its Gemini AI models that rivals like OpenAI's ChatGPT, Anthropic's Claude, and the growing array of Chinese open source options are unlikely to get anytime soon: grounding with Google Maps.

This addition allows developers to connect Google's Gemini AI models' reasoning capabilities with live geospatial data from Google Maps, enabling applications to deliver detailed, location-relevant responses to user queries—such as business hours, reviews, or the atmosphere of a specific venue.

By tapping into data from over 250 million places, developers can now build more intelligent and responsive location-aware experiences.

This is particularly useful for applications where proximity, real-time availability, or location-specific personalization matter—such as local search, delivery services, real estate, and travel planning.

When the user’s location is known, developers can pass latitude and longitude into the request to enhance the response quality.

By tightly integrating real-time and historical Maps data into the Gemini API, Google enables applications to generate grounded, location-specific responses with factual accuracy and contextual depth that are uniquely possible through its mapping infrastructure.

Merging AI and Geospatial Intelligence

The new feature is accessible in Google AI Studio, where developers can try a live demo powered by the Gemini Live API. Models that support the grounding with Google Maps include:

• Gemini 2.5 Pro

• Gemini 2.5 Flash

• Gemini 2.5 Flash-Lite

• Gemini 2.0 Flash

In one demonstration, a user asked for Italian restaurant recommendations in Chicago.

The assistant, leveraging Maps data, retrieved top-rated options and clarified a misspelled restaurant name before locating the correct venue with accurate business details.

Developers can also retrieve a context token to embed a Google Maps widget in their app’s user interface. This interactive component displays photos, reviews, and other familiar content typically found in Google Maps.

Integration is handled via the generateContent method in the Gemini API, where developers include googleMaps as a tool. They can also enable a Maps widget by setting a parameter in the request. The widget, rendered using a returned context token, can provide a visual layer alongside the AI-generated text.

Use Cases Across Industries

The Maps grounding tool is designed to support a wide range of practical use cases:

• Itinerary generation: Travel apps can create detailed daily plans with routing, timing, and venue information.

• Personalized local recommendations: Real estate platforms can highlight listings near kid-friendly amenities like schools and parks.

• Detailed location queries: Applications can provide specific information, such as whether a cafe offers outdoor seating, using community reviews and Maps metadata.

Developers are encouraged to only enable the tool when geographic context is relevant, to optimize both performance and cost.

According to the developer documentation, pricing starts at $25 per 1,000 grounded prompts — a steep sum for those trafficking in numerous queries.

Combining Search and Maps for Enhanced Context

Developers can use Grounding with Google Maps alongside Grounding with Google Search in the same request.

While the Maps tool contributes factual data—like addresses, hours, and ratings—the Search tool adds broader context from web content, such as news or event listings.

For example, when asked about live music on Beale Street, the combined tools provide venue details from Maps and event times from Search.

According to Google, internal testing shows that using both tools together leads to significantly improved response quality.

Unfortunately, it doesn't appear that the Google Maps grounding includes live vehicular traffic data — at least not yet.

Customization and Developer Flexibility

The experience is built for customization. Developers can tweak system prompts, choose from different Gemini models, and configure voice settings to tailor interactions.

The demo app in Google AI Studio is also remixable, enabling developers to test ideas, add features, and iterate on designs within a flexible development environment.

The API returns structured metadata—including source links, place IDs, and citation spans—that developers can use to build inline citations or verify the AI-generated outputs.

This supports transparency and enhances trust in user-facing applications. Google also requires that Maps-based sources be attributed clearly and linked back to the source using their URI.

Implementation Considerations for AI Builders

For technical teams integrating this capability, Google recommends:

• Passing user location context when known, for better results.

• Displaying Google Maps source links directly beneath the relevant content.

• Only enabling the tool when the query clearly involves geographic context.

• Monitoring latency and disabling grounding when performance is critical.

Grounding with Google Maps is currently available globally, though prohibited in several territories (including China, Iran, North Korea, and Cuba), and not permitted for emergency response use cases.

Availability and Access

Grounding with Google Maps is now generally available through the Gemini API.

With this release, Google continues to expand the capabilities of the Gemini API, empowering developers to build AI-driven applications that understand and respond to the world around them.

🔗 Sumber: venturebeat.com

📌 MAROKO133 Breaking ai: Adobe Research Unlocking Long-Term Memory in Video World

Video world models, which predict future frames conditioned on actions, hold immense promise for artificial intelligence, enabling agents to plan and reason in dynamic environments. Recent advancements, particularly with video diffusion models, have shown impressive capabilities in generating realistic future sequences. However, a significant bottleneck remains: maintaining long-term memory. Current models struggle to remember events and states from far in the past due to the high computational cost associated with processing extended sequences using traditional attention layers. This limits their ability to perform complex tasks requiring sustained understanding of a scene.

A new paper, “Long-Context State-Space Video World Models” by researchers from Stanford University, Princeton University, and Adobe Research, proposes an innovative solution to this challenge. They introduce a novel architecture that leverages State-Space Models (SSMs) to extend temporal memory without sacrificing computational efficiency.

The core problem lies in the quadratic computational complexity of attention mechanisms with respect to sequence length. As the video context grows, the resources required for attention layers explode, making long-term memory impractical for real-world applications. This means that after a certain number of frames, the model effectively “forgets” earlier events, hindering its performance on tasks that demand long-range coherence or reasoning over extended periods.

The authors’ key insight is to leverage the inherent strengths of State-Space Models (SSMs) for causal sequence modeling. Unlike previous attempts that retrofitted SSMs for non-causal vision tasks, this work fully exploits their advantages in processing sequences efficiently.

The proposed Long-Context State-Space Video World Model (LSSVWM) incorporates several crucial design choices:

1. Block-wise SSM Scanning Scheme: This is central to their design. Instead of processing the entire video sequence with a single SSM scan, they employ a block-wise scheme. This strategically trades off some spatial consistency (within a block) for significantly extended temporal memory. By breaking down the long sequence into manageable blocks, they can maintain a compressed “state” that carries information across blocks, effectively extending the model’s memory horizon.
2. Dense Local Attention: To compensate for the potential loss of spatial coherence introduced by the block-wise SSM scanning, the model incorporates dense local attention. This ensures that consecutive frames within and across blocks maintain strong relationships, preserving the fine-grained details and consistency necessary for realistic video generation. This dual approach of global (SSM) and local (attention) processing allows them to achieve both long-term memory and local fidelity.

The paper also introduces two key training strategies to further improve long-context performance:

• Diffusion Forcing: This technique encourages the model to generate frames conditioned on a prefix of the input, effectively forcing it to learn to maintain consistency over longer durations. By sometimes not sampling a prefix and keeping all tokens noised, the training becomes equivalent to diffusion forcing, which is highlighted as a special case of long-context training where the prefix length is zero. This pushes the model to generate coherent sequences even from minimal initial context.
• Frame Local Attention: For faster training and sampling, the authors implemented a “frame local attention” mechanism. This utilizes FlexAttention to achieve significant speedups compared to a fully causal mask. By grouping frames into chunks (e.g., chunks of 5 with a frame window size of 10), frames within a chunk maintain bidirectionality while also attending to frames in the previous chunk. This allows for an effective receptive field while optimizing computational load.

The researchers evaluated their LSSVWM on challenging datasets, including Memory Maze and Minecraft, which are specifically designed to test long-term memory capabilities through spatial retrieval and reasoning tasks.

The experiments demonstrate that their approach substantially surpasses baselines in preserving long-range memory. Qualitative results, as shown in supplementary figures (e.g., S1, S2, S3), illustrate that LSSVWM can generate more coherent and accurate sequences over extended periods compared to models relying solely on causal attention or even Mamba2 without frame local attention. For instance, on reasoning tasks for the maze dataset, their model maintains better consistency and accuracy over long horizons. Similarly, for retrieval tasks, LSSVWM shows improved ability to recall and utilize information from distant past frames. Crucially, these improvements are achieved while maintaining practical inference speeds, making the models suitable for interactive applications.

The Paper Long-Context State-Space Video World Models is on arXiv

The post Adobe Research Unlocking Long-Term Memory in Video World Models with State-Space Models first appeared on Synced.

🔗 Sumber: syncedreview.com