🤖 NVIDIA to Boost AI Education

LIVE Webinar Alert: Gen AI Chatbot Security

Discover how to secure your enterprise’s generative AI chatbots against emerging threats. Join industry experts, including our founder Steve Nouri and Rohit Valia, CEO of Tumeryk.com, as they explore cutting-edge AI security solutions.

Key Highlights:

• Understanding the evolving threat landscape

• Best practices for AI security

• Insights into Tumeryk's AI Guard tools

🗓️ Date: August 14th, 8 AM EDT

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California Partners with NVIDIA to Boost AI Education

California has teamed up with NVIDIA to improve AI education across the state. This partnership will help schools, colleges, and adult education programs teach students and workers the skills needed in AI, especially in generative AI.

Educators will get certified through NVIDIA’s training programs, gaining access to top-quality resources and cloud-based tools. This initiative aims to prepare students and professionals for AI-related jobs, making AI education more accessible and bridging the digital divide in the state.

Gemma Scope: Open Sparse Autoencoders Everywhere All At Once on Gemma 2

The Gemma Scope paper explores the use of sparse autoencoders on the Gemma 2 platform to manage large-scale data processing across multiple domains simultaneously.

By optimizing these models for real-time applications, the research demonstrates how sparse autoencoders can efficiently handle diverse tasks, making them versatile tools in areas such as finance, healthcare, and beyond.

This approach not only improves performance but also broadens the potential use cases for sparse autoencoders in processing vast amounts of data across different industries.

The study suggests that this method could lead to wider adoption of advanced AI techniques in various sectors.

AI in Healthcare: New Benchmark Shows Promising Progress

The new GMAI-MMBench benchmark is a big step forward in testing AI’s ability to help in medical. It covers a wide range of medical tasks, using data from 285 different sources, and is designed to see how well AI models perform in real-world clinical scenarios.

This benchmark aims to test the capabilities of AI models in real-world clinical scenarios, a crucial step towards realizing the vision of General Medical AI (GMAI).

The results are both promising and challenging. Even the most advanced models, like GPT-4o, achieve only around 52% accuracy, highlighting significant room for improvement.

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