Intel’s Push for AI-ready Data Centers
Amazon’s Project PI detects defective products before shipping, Microsoft’s Aurora AI could transform weather forecasting.
Hello Engineering Leaders and AI Enthusiasts!
Welcome to the 289th edition of The AI Edge newsletter. This edition features “Intel’s Push for AI-ready Data Centers.”
And a huge shoutout to our amazing readers. We appreciate you😊
In today’s edition:
💻 Intel’s new data center chips handle demanding AI workloads
📦 Amazon’s Project PI detects defective products before shipping
☁️ Microsoft’s Aurora AI could transform weather forecasting
📚 Knowledge Nugget: 10 concepts to know when working with LLMs by
Let’s go!
Intel’s new data center chips handle demanding AI workloads
Intel has announced next-generation Xeon 6 server processors to regain the data center market share it had been losing to AMD. They come in two varieties. The larger, more powerful version is designed to run the computations necessary to generate responses from complex AI models and other tasks requiring increased horsepower. Intel plans to help companies modernize their aging data center systems with Xeon 6 chips so they can generate new digital capabilities.
Intel also revealed that its Gaudi 3 AI accelerator chips would be priced much lower than its rivals' products.
Why does it matter?
As more companies have started to deploy AI apps and models, the AI hardware space is getting heated with competition. Intel seems to be one of the only companies innovating across the full spectrum of the AI market opportunity– from semiconductor manufacturing to PCs and data center systems.
Amazon’s Project PI detects defective products before shipping
Amazon has launched Project PI, which uses AI to scan products for defects before shipping them to customers. This AI system combines computer vision to visually inspect items with generative AI models that can understand things like text on packages.
As products go through a scanning tunnel, the AI checks for damage, incorrect colors/sizes, or expired dates. If it finds a problem, that item is isolated to evaluate the defect. Project PI already operates in several of Amazon's warehouses across North America. The system catches millions of defective products daily before they reach customers.
Why does it matter?
Using innovative AI systems, retailers can avoid dealing with returns and reshipments, reducing costs and inefficiencies. By cutting down unnecessary shipping, retailers minimize environmental impact and carbon emissions, contributing to sustainability goals.
Microsoft’s Aurora AI could transform weather forecasting
Microsoft has developed a powerful new AI foundation model called Aurora that can make highly accurate weather predictions. It is trained on over a million diverse weather and climate data hours. This allows it to develop a comprehensive understanding of atmospheric dynamics and excel at forecasting various weather variables like temperature, wind speed, air pollution levels, and greenhouse gas concentrations.
What sets Aurora apart is its ability to capture intricate details at high spatial resolution (around 11km) while being much faster and more computationally efficient than traditional numerical weather prediction systems. Aurora's flexible architecture and training on heterogeneous datasets enable it to adapt to different forecasting tasks and resolutions.
Why does it matter?
This major advancement in AI-based weather forecasting could help communities prepare for extreme weather events like storms. AI will also play a bigger role in predicting the impacts of climate change. We may be nearing days when weatherman’s predictions will be 100% accurate.
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Knowledge Nugget: 10 concepts to know when working with LLMs
discusses 10 important concepts to understand when working with LLMs like LoRA (Low-Rank Adaptation), PEFT (Parameter-Efficient Fine-Tuning, RAG (Retrieval-Augmented Generation), MoE (Mixture of Experts), and so on.The article also discusses methods for qualitative evaluation of LLMs, including human review, human-in-the-loop feedback, and using larger LLMs to assess model behavior. Finally, it outlines a quick setup for fine-tuning an LLM using the Qwak platform, simplifying the deployment and monitoring of the machine learning model.
Why does it matter?
As LLMs become increasingly integrated into diverse industries, understanding these concepts enables developers and researchers to work more effectively with LLMs, optimize them for specific use cases, ensure their reliability, and leverage their capabilities efficiently.
What Else Is Happening❗
🚫 Hugging Face detects ‘unauthorized access’ to its AI model hosting platform
Last week, Hugging Face detected unauthorized access to Spaces, its platform for creating, sharing, and hosting AI models. It suspects some Spaces secrets (private pieces of info that act as keys to unlock protected resources like accounts, tools, and dev environments) have leaked. It has taken steps to remediate this. (Link)
🎓 High-quality education data key to AI performance: Research
Researchers created a high-quality dataset called FineWeb-Edu by filtering an existing web dataset for educational content. Language models trained on FineWeb-Edu significantly outperformed models trained on unfiltered datasets. The research shows that data quality and diversity are more important than dataset size for training effective AI models. (Link)
👎 LeCun criticizes Musk for mistreating scientists and spreading misinformation
LeCun has again rebuked Musk on X after they had a heated feud on X last week. This time, LeCun accused Musk of forcing researchers to work in secrecy instead of allowing them to publish their work, which slows scientific progress. He also accused Musk of falsely predicting AI and autonomous vehicles. (Link)
💰 Microsoft to invest $3.2 billion in Sweden to expand AI and cloud infrastructure
Microsoft will invest $3.2 billion over two years to expand its cloud and AI infrastructure in Sweden. Microsoft's biggest investment to date in Sweden includes a pledge to help train some 250,000 people with AI skills, corresponding to 2.4% of the population, which will help boost the Nordic country's competitiveness in generative AI. (Link)
🤖 Microsoft identifies few AI deep fakes in the EU election
As the European Union prepares for its elections in June 2024, the threat of AI-generated deepfakes has become a significant concern. Microsoft President Brad Smith highlighted this burning issue, emphasizing its potential impact on the democratic process and the steps Microsoft is taking to mitigate these risks. (Link)
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