🌍 Pokémon Go Data Used to Build AI Model

Welcome, AI enthusiasts

Are you playing Pokémon Go? If so, your camera data is getting used for Niantic’s revolutionary Large Geospatial Model (LGM) . Meanwhile, Google’s Gemini chatbot is stepping up its game with a new memory feature, delivering personalized experiences that truly “get” you. Over in the enterprise realm, Microsoft is supercharging AI workflows with Fabric updates, seamlessly bridging transactional data and cutting-edge AI tools. Let’s dive in! 

In today’s insights:

• Pokémon Go Data Used to Build AI Model

• Google Gemini Gains Memory Functionality

• Microsoft Fabric Gains New AI Tools to Power Enterprise Workflows

Read time: 3 minutes

🗞️ LATEST DEVELOPMENTS

NIANTIC
🌍 Pokémon Go Data Used to Build AI Model

Evolving AI: Niantic leverages millions of scans from Pokémon Go and Ingress players to build a groundbreaking Large Geospatial Model (LGM).

Key Points:

• Niantic’s LGM uses player-sourced data to enhance spatial intelligence.

• The model builds on a Visual Positioning System (VPS) refined over five years.

• Pedestrian perspectives provide a unique and detailed 3D map of the world.

Details:

Niantic’s LGM integrates millions of scans from smartphones of Pokémon Go and Ingress players. These scans feed the company’s Visual Positioning System, which determines phone orientation and location using a 3D map. Unlike traditional mapping data, this is captured from a pedestrian perspective, offering details cars can't access. The result? A rich dataset encompassing geometry and semantic context—sky, trees, and more.

Why It Matters:

Niantic’s approach bridges gaming and advanced AI, highlighting the power of crowdsourced data. This model could fuel applications in robotics, AR, and urban planning, transforming how machines interact with human environments. It’s another leap toward a digitally integrated world.

Watch Niantic’s video about Neural Mapping below:

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GOOGLE
 🌟 Google Gemini Gains Memory Functionality

Evolving AI: Google’s Gemini chatbot has introduced a memory feature, enhancing personalization by recalling user preferences and details.

Key Points:

• Gemini now remembers user-specific data like preferences and work habits.

• Available only through Google One’s AI Premium plan.

• Data is not shared or used for training; deletable by users.

• Security vulnerabilities have emerged in similar AI memory features.

Details:

Gemini’s memory allows personalized suggestions, such as restaurant recommendations based on food preferences. Currently available on the web client, the feature is exclusive to Google One AI Premium subscribers ($20/month). Users can manage memory settings, ensuring data is stored only until manually deleted. While data won’t train the model, vulnerabilities like false memory exploits could pose risks, as seen with other chatbots.

The Relevance:

This feature underscores the growing trend toward highly personalized AI tools while it also highlights the balance between convenience and security. Developers must address potential exploitation risks, ensuring trust in AI systems remains intact. Gemini’s approach is a step towards personal user interactions.

MICROSOFT
🚀 Microsoft Fabric Gains New AI Tools to Power Enterprise Workflows

Evolving AI: Microsoft accelerates enterprise AI with new updates to Fabric, enabling seamless integration of transactional data and advanced AI capabilities.

Key Points:

• Microsoft integrates Azure SQL and other transactional databases with Fabric for unified AI-driven data operations.

• New tools simplify data exploration, governance, and management across Fabric’s open lakehouse architecture.

• Simplified APIs and enhanced AI skills enable natural language queries, sentiment analysis, and advanced analytics.

• Features like Real-Time Intelligence and workspace monitoring improve scalability, governance, and operational efficiency.

Details:

Fabric, Microsoft’s unified SaaS platform, now integrates transactional databases like Azure SQL and Cosmos DB directly into its OneLake architecture. This advancement allows developers to access live transactional data and bulk analytics from one seamless interface, enabling next-gen AI apps.

The new OneLake catalog adds "Explore" and "Govern" tabs for better data management and oversight. Meanwhile, Fabric notebooks now preview simplified APIs for AI text enrichment tasks and conversational AI features that can query data using natural language.

Updates also include Real-Time Intelligence for streaming data insights, GraphQL API for efficient querying, and open mirroring for dynamic data synchronization. Workspace monitoring and surge protection add robust operational controls.

Why It Matters:

These updates signify Microsoft's focus on building a robust foundation for enterprise AI. By unifying data and AI tools, Fabric empowers developers and enterprises to streamline workflows and reduce integration complexities. This not only accelerates business innovation but also underscores Microsoft’s commitment to democratizing AI capabilities for broader economic and societal benefits.

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