Microsoft's AI Models Trained on Unlicensed Web Data, Contradicting Clean Data Claims
In brief
- Microsoft has revealed that its new MAI models were trained using unlicensed web data like Common Crawl, despite earlier claims of exclusively using "clean and commercially licensed" datasets.
- This practice aligns with many other AI companies, which rely on fair use and depend on website owners to block their crawlers if they object.
- This admission raises questions about the transparency and accuracy of Microsoft's marketing around its AI products.
- While the company emphasizes the quality of its data, critics argue that using unlicensed material undermines claims of enterprise-grade cleanliness and licensing.
- Looking ahead, this could spark broader discussions about data sourcing practices in the AI industry and how companies communicate their methods to users and developers.
Terms in this brief
- MAI
- Microsoft's AI (MAI) refers to Microsoft's suite of artificial intelligence models and services. The term is used within Microsoft to denote their AI initiatives and products.
- Common Crawl
- A large-scale dataset created by crawling the web, providing a vast resource for training AI models. It is often used despite concerns over licensing and data ownership.
Read full story at The Decoder →
More briefs
SK Hynix Sees 1242 Percent Net Profit Boost
SK hynix said its second-quarter net profit soared 1242 percent year-on-year. This was driven by demand for its memory chips from the artificial intelligence industry. The company's quarterly net profit was 94 trillion won, an all-time high. Operating profit jumped 557 percent from last year to 60 trillion won. Revenue stood at 79 trillion won. SK hynix will invest around 40 trillion won this year. The company expects demand for its memory chips to persist as tech companies increase their AI infrastructure investments. SK hynix will continue to grow with the evolving AI technology.
UK Introduces AI-Enabled Smart Lamp-Posts
The UK has introduced AI-enabled smart lamp-posts that can recognize number plates and faces. These lamp-posts can power themselves through solar panels and have the potential to fight crime and track down missing persons. They can also incorporate new AI capability such as gait analysis and suspicious behaviors. The use of these lamp-posts has raised concerns about surveillance and data ownership, with 50,000 of them set to be installed in Nigeria. The technology will continue to develop and expand in the future.
AI Companies Destroying Millions of Books for Training Data
AI companies are destroying millions of physical books to use for training data. They scan the books and then discard them. This matters because the books are a valuable source of human-authored text. The AI companies need this text to improve their models. One company, Anthropic, was sued for copyright infringement and paid a $1.5 billion settlement. The AI companies are now hiring middlemen to buy the books for them. They want to keep their involvement a secret because they know it is not popular. The demand for human-authored text will continue to drive the destruction of physical books.
AI Sees Through Leaves to Help Farmers
Scientists created a new AI technology that can see through leaves to identify and measure hidden fruit. This technology can help farmers by creating a complete 3D model of each plant, including what is behind the leaves. It can open the door to automating tasks like crop forecasting. Current methods can produce inaccuracies of up to 23%, but the new technology has achieved fruit counts within 2% to 3% of the correct figure. The technology can save large growers millions of dollars and reduce waste. It will help farmers understand how much fruit they will produce and the size of the fruit. Farmers will be able to plan better with this new technology.
Microsoft's New Cybersecurity Model Delivers Big Efficiency Gains
Microsoft has unveiled MAI-Cyber-1-Flash, a compact cybersecurity model that achieves an impressive 96 percent score on the CyberGym benchmark when integrated into its MDASH multi-agent system. This new tool significantly reduces costs by cutting down reliance on expensive pure frontier models-costs are expected to drop by 50 percent since only the most challenging cases will be handled by GPT-5.4. While MAI-Cyber-1-Flash excels in efficiency, Microsoft still turns to OpenAI for complex reasoning tasks. This hybrid approach allows Microsoft to leverage its own model where it shines brightest while relying on OpenAI's expertise for tougher problems. Looking ahead, this cost-effective solution could expand cybersecurity capabilities for businesses, making advanced protection more accessible. The integration with MDASH suggests a broader push toward multi-agent systems in security, potentially leading to even smarter and more coordinated defenses in the future.