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Today’s Topics Are:

- Walmart’s New AI Tool ‘Wally’ Transforms Merchandising Decisions
- Humanoid Robots Learn to Get Up After Falling with New AI Framework

Walmart’s New AI Tool ‘Wally’ Transforms Merchandising Decisions

Quick Summary
Walmart has introduced Wally, an internal generative AI tool designed to help its merchandising team analyze sales, pricing, and inventory faster than ever. By automating complex data reports and offering quick insights, Wally streamlines decision-making, allowing merchants to adapt swiftly to customer demand.

Key Points

• AI-Powered Efficiency: Wally enables merchants to analyze data in seconds instead of hours.

• Smart Data Interpretation: The tool understands industry-specific jargon, making searches more intuitive.

• Enhanced Inventory Management: Helps merchants handle out-of-stock and overstock situations efficiently.

• Company-Wide AI Integration: Walmart continues expanding AI use, with tools improving both customer and employee experiences.

• Quality Control Measures: Automated tests and human feedback ensure Wally provides accurate insights.

Story
Walmart’s merchants play a crucial role in selecting products, setting prices, and managing inventory across online and physical stores. Traditionally, these tasks required running extensive reports using tools like Excel, often taking hours to analyze customer behavior and sales trends. Recognizing the need for speed, Walmart developed Wally, a generative AI tool that delivers insights in seconds through a user-friendly chat interface.

Wally retrieves and organizes data from Walmart’s databases, allowing merchants to generate reports instantly without worrying about specific product naming conventions. Whether tracking television sales, monitoring inventory shortages, or adjusting pricing, Wally offers precise, real-time insights tailored to business needs.

Since its rollout to merchants last month, Wally has significantly improved efficiency, freeing up valuable time for other tasks. The AI tool has also been trained with Walmart’s internal merchandising guidelines, ensuring its recommendations align with company strategies.

Conclusion
Wally marks a major step in Walmart’s AI-driven transformation, making data analysis faster and more accessible for its merchandising team. By reducing manual workload and improving decision-making, Walmart is leveraging AI to stay ahead of shifting consumer demands while ensuring accuracy and efficiency in its retail operations.

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Humanoid Robots Learn to Get Up After Falling with New AI Framework

Quick Summary
Researchers at the University of Illinois Urbana-Champaign have developed HUMANUP, a machine learning framework that enables humanoid robots to autonomously recover after falling. Using reinforcement learning (RL), the system refines movement strategies to help robots stand up smoothly on various terrains, improving their adaptability and autonomy.

Key Points

• New Learning Framework: HUMANUP enables humanoid robots to recover from falls without human assistance.

• Reinforcement Learning Approach: The AI model refines movement strategies for different positions and terrains.

• Real-World Testing: Successfully deployed on the Unitree G1 robot, performing on rough, slippery, and uneven surfaces.

• Two-Phase Training: Initial phase finds effective limb movements; second phase optimizes for smooth, stable recovery.

• Potential Applications: Enhances robotic autonomy for real-world deployment in industries and disaster response.

Story
Humanoid robots, designed to walk and balance like humans, often struggle to get back up after falling. Unlike people, who instinctively recover, these robots require external assistance, limiting their real-world usability. To address this challenge, researchers developed HUMANUP, a machine-learning-based system that trains robots to stand up autonomously, regardless of how they fall or the surface they land on.

The framework follows a two-step process. In the first phase, it identifies effective limb movements that allow a robot to regain its footing. The second phase refines these motions, ensuring they are smooth, stable, and adaptable to different environments. HUMANUP was tested on the Unitree G1 robot across various terrains, including slippery snow, bumpy grass, and sloped concrete, achieving a 78.3% success rate compared to a built-in controller’s 41.7%.

Conclusion
HUMANUP represents a major advancement in humanoid robotics, improving their ability to operate independently in complex environments. As robots become more self-sufficient, this breakthrough paves the way for greater adoption in industries such as manufacturing, logistics, and search-and-rescue missions.

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