Behind LinkedIn’s Generative AI Tech Stack

LinkedIn shared how they built a scalable, modular tech stack to power generative AI features like personalized summaries and messaging for 900M+ users. Their approach emphasizes flexibility, human-in-the-loop refinement, and seamless integration into existing workflows. Key lessons? Scalability, modularity, and user trust are essential for AI success at scale. LinkedIn’s journey is a great example of how to operationalize AI for meaningful, real-world impact.

For organizations experimenting with generative AI, LinkedIn’s approach is a great reminder: It’s not just about building AI models but ensuring they integrate seamlessly with business operations and user experiences. Thoughtful architecture and a focus on scalability make all the difference when you’re aiming for meaningful, reliable outcomes.

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Stanford Unveils Global AI Power Rankings Tool

Stanford’s Institute for Human-Centered AI (HAI) has launched the Global Vibrancy Tool 2024, a groundbreaking framework that evaluates the AI ecosystems of 36 countries. Using 42 AI-specific indicators, the tool offers insights into the global landscape of AI capabilities, covering areas like research, investment, and infrastructure.

• The U.S. Dominates: The United States leads by a wide margin, excelling in AI research output, private investment, and the development of state-of-the-art machine learning models. It remains the hub for AI innovation, backed by its robust academic institutions, thriving private sector, and world-class talent pool.

• China in Second Place: China follows but lags behind the U.S. significantly in critical areas like private AI investment and the creation of notable machine learning models. However, it remains a global force, particularly in government-driven initiatives and the deployment of AI at scale.

• The United Kingdom Holds Strong in Third: The UK secures the third spot, reflecting its strong AI research community, government policies, and growing private sector engagement.

• Emerging Players: Countries like South Korea, the UAE, and Singapore are making strides, leveraging strategic investments and public-private collaborations to close the gap with leading nations.

Read Findings. Or Explore the Tracking Tool.

The New Scaling Law

At Microsoft Ignite, CEO Satya Nadella introduced a new AI scaling law emphasizing “test-time” or “inference-time” compute. This approach allocates more computational resources during inference, allowing AI models additional time to process and generate responses. Nadella’s remarks suggest a shift from traditional scaling methods, which primarily emphasized increasing model size and training data, to enhancing performance through extended computational efforts during inference.

In his post, Marcus argues that while increasing inference-time computation can yield improvements, it is not a universal solution and may lead to diminishing returns, especially in open-ended tasks where generating reliable synthetic data is challenging. He emphasizes the need for innovative approaches beyond scaling existing models, advocating for AI systems that integrate reasoning and factual understanding rather than solely relying on extended computational processing.

The AI community is exploring new scaling methodologies, such as enhancing inference-time computation, to advance model performance. While these approaches offer potential benefits, they also underscore the necessity for fresh innovations that address the inherent limitations of current AI models.

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Blog Spotlight: The Role of AI in Data Governance

As data governance becomes essential to evolving business needs, organizations are searching for ways to manage it more efficiently, securely, and ethically. Data governance has become more than a compliance checkbox—it’s a strategic imperative.

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