News from the AI & ML world
DeeperML - #aicopilot
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@www.microsoft.com
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Microsoft is aggressively pursuing AI integration across a range of its products and research initiatives. One notable project is Aurora, an AI model developed by Microsoft Research to revolutionize environmental forecasting. Senior Researchers Megan Stanley and Wessel Bruinsma are leading the charge, and recently discussed how Aurora extends its capabilities to predict tropical cyclones and ocean waves. Their work, detailed in a Nature publication titled "A Foundation Model for the Earth System," highlights Aurora's potential to significantly improve weather prediction accuracy and environmental understanding.
Microsoft is also experimenting with "AI Developing AI" through Project Amelie. This experimental AI agent aims to automate the creation of machine learning pipelines from a single prompt. Microsoft reports that in early testing, Project Amelie outperforms current state-of-the-art benchmarks on MLE-Bench, a framework that evaluates how effectively machine learning agents handle real-world tasks. During a live demo at Microsoft Build 2025, Project Amelie was showcased and how it can assist data scientists by automating the process of data sourcing, analysis, and formatting. This addresses the typically manual and time-consuming tasks involved in preparing data for machine learning. However, Microsoft's AI endeavors are not without their challenges. Research by Alex Lu, a senior researcher at Microsoft, indicates that zero-shot foundation models in single-cell biology currently underperform compared to simpler methods. Lu's work suggests that more research is needed to rigorously evaluate and improve the efficacy of AI models in biological applications. These findings, highlighted in the paper "Assessing the Limits of Zero-Shot Foundation Models in Single-Cell Biology," emphasize the importance of continued research and development to fully realize the potential of AI in scientific domains. Recommended read:
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Thomas Claburn@The Register
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Microsoft is significantly advancing human-agent collaboration with the latest upgrades to its Microsoft 365 Copilot. The tech giant is rolling out new updates, including Researcher and Analyst agents designed to enhance workplace productivity by providing in-depth research and data analysis. These AI agents, powered by OpenAI's deep reasoning models, are envisioned as digital colleagues capable of performing complex workplace tasks, helping professionals tackle intricate challenges with advanced reasoning capabilities. This aligns with Microsoft’s broader AI-first strategy, aiming to scale digital labor and drive substantial productivity gains for companies adopting these technologies.
Microsoft is also unveiling a redesigned Microsoft 365 Copilot app featuring AI-powered search and an Agent Store, positioning it as the central hub for human-agent collaboration. The new AI-powered enterprise search tool, Copilot Search, organizes data across the enterprise, providing rich, context-aware answers from first-party and third-party apps like ServiceNow, Google Drive, and Jira. The Agent Store provides access to Microsoft’s Researcher and Analyst agents, initially introduced in March and available to those enrolled in Microsoft’s Frontier program. Moreover, Microsoft is adding new capabilities to its Control System feature to assist IT professionals in overseeing and measuring bot usage effectively. These updates are part of a broader effort to integrate AI across the Microsoft ecosystem, addressing the increasing demand for AI-powered solutions in the enterprise. According to Microsoft’s 2025 Work Trend Index report, a significant majority of companies are rethinking their strategies to leverage AI, signaling a decisive move toward full-scale AI transformation. Key features include AI-powered enterprise search, personalized memory capabilities, specialized reasoning agents like Researcher and Analyst, and the Agent Store. The Researcher agent helps with multi-step research tasks, while the Analyst agent provides data science capabilities. Recommended read:
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Megan Crouse@techrepublic.com
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Microsoft has unveiled BitNet b1.58 2B4T, a groundbreaking AI model designed for exceptional efficiency. Developed by Microsoft's General Artificial Intelligence group, this model utilizes one-bit neural network weights, representing each weight with only three discrete values (-1, 0, or +1). This approach, called ternary quantization, allows each weight to be stored in just 1.58 bits, drastically reducing memory usage. The result is an AI model that can operate on standard CPUs without the need for specialized, energy-intensive GPUs.
Unlike conventional AI models that rely on 16- or 32-bit floating-point numbers, BitNet's unique architecture allows it to run smoothly on hardware like Apple's M2 chip, requiring only 400MB of memory. To compensate for its low-precision weights, BitNet b1.58 2B4T was trained on a massive dataset of four trillion tokens, the equivalent of approximately 33 million books. This extensive training enables the model to perform on par with, and in some cases even better than, other leading models of similar size, such as Meta's Llama 3.2 1B, Google's Gemma 3 1B, and Alibaba's Qwen 2.5 1.5B. To facilitate the deployment and adoption of this innovative model, Microsoft has released a custom software framework called bitnet.cpp, optimized to take full advantage of BitNet's ternary weights. This framework is available for both GPU and CPU execution, including a lightweight C++ version. The model has demonstrated strong performance across a variety of tasks including math and common sense reasoning in benchmark tests. Microsoft plans to expand BitNet to support longer texts, additional languages, and multimodal inputs like images, while also working on the Phi series, another family of efficient AI models. Recommended read:
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Megan Crouse@techrepublic.com
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Microsoft has unveiled BitNet b1.58, a groundbreaking language model designed for ultra-efficient operation. Unlike traditional language models that rely on 16- or 32-bit floating-point numbers, BitNet utilizes a mere 1.58 bits per weight. This innovative approach significantly reduces memory requirements and energy consumption, enabling the deployment of powerful AI on devices with limited resources. The model is based on the standard transformer architecture, but incorporates modifications aimed at efficiency, such as BitLinear layers and 8-bit activation functions.
The BitNet b1.58 2B4T model contains two billion parameters and was trained on a massive dataset of four trillion tokens, roughly equivalent to the contents of 33 million books. Despite its reduced precision, BitNet reportedly performs comparably to models that are two to three times larger. In benchmark tests, it outperformed other compact models and performed competitively with significantly larger and less efficient systems. Its memory footprint is just 400MB, making it suitable for deployment on laptops or in cloud environments. Microsoft has released dedicated inference tools for both GPU and CPU execution, including a lightweight C++ version, to facilitate adoption. The model is available on Hugging Face. Future development plans include expanding the model to support longer texts, additional languages, and multimodal inputs such as images. Microsoft is also working on another efficient model family under the Phi series. The company demonstrated that this model can run on a Apple M2 chip. Recommended read:
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@the-decoder.com
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Microsoft is pushing the boundaries of AI in both security and gaming, showcasing new advancements in both fields. On the security front, Microsoft is hosting a Tech Accelerator event on April 22, 2025, aimed at guiding developers and cloud architects on effectively integrating security best practices within Azure and AI projects. The event will provide essential guidance and resources for secure planning, building, management, and optimization of Azure deployments and AI applications. Participants will gain insights from Microsoft experts, learn how to identify security risks in Azure environments, protect infrastructure from threats, and design secure AI environments and applications.
Microsoft is also making waves in the gaming world with its real-time AI-generated playable demo of Quake II. The company's WHAMM (World and Human Action MaskGIT Model) is a generative AI model capable of supporting real-time gameplay. This model represents a significant upgrade from its predecessor, WHAM-1.6B, offering faster visual output, improved resolution, and smarter gameplay response. WHAMM can generate images at over 10 frames per second with a resolution of 640x360, a notable enhancement compared to WHAM-1.6B. The WHAMM model was trained on Quake II using intentional data curation, focusing on high-quality data collected from professional game testers. This allowed the model to efficiently learn in-game behavior with significantly less data than previous iterations. Players can interact with the game using controllers or keyboards, with the model dynamically updating the environment to respond appropriately. While WHAMM is still in its early stages and has limitations, such as occasional input lag and challenges with accurately storing stats, its accelerated rate of advancement suggests a future where entirely AI-generated video games could become a reality. Recommended read:
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Ken Yeung@Ken Yeung
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Microsoft is enhancing its Copilot Studio platform with new 'deep reasoning' capabilities, allowing AI agents to solve complex problems more effectively. This upgrade also includes 'agent flows' which blend AI's flexibility with structured business automation. The new Researcher and Analyst agents for Microsoft 365 Copilot represent a significant step forward in AI agent evolution, enabling them to handle sophisticated tasks requiring detailed analysis and methodical thinking.
Microsoft's Security Copilot service is also getting a boost with a set of AI agents designed to automate repetitive tasks, freeing up security professionals to focus on more critical threats. These AI agents are designed to assist with critical tasks such as phishing, data security, and identity management. These agents showcase the breadth of what can be created when combining enterprise business data, access to advanced reasoning models, and structured workflows. Recommended read:
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mpesce@Windows Copilot News
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Microsoft is actively enhancing its Copilot service with regular updates, introducing new features and improvements. Recent updates, as of March 19, 2025, include Copilot Vision which is now available on Android devices for Pro subscribers in the United States, allowing users to leverage their phone's camera for assistance. Copilot Vision access in Edge has expanded to all free users in the U.S., enabling the AI to scan, analyze, and provide insights from webpages. Additionally, the "Think Deeper" reasoning model has been upgraded to OpenAI's o3-mini (high), leading to faster and more flexible responses to complex questions.
Microsoft is also making strides in AI research and security. Microsoft Research has developed a more efficient method for incorporating external knowledge into language models, called KBLaM, which uses vector pairs woven directly into the model's architecture. This system offers faster and more efficient responses by processing knowledge directly within the model. Furthermore, Microsoft is expanding its Security Copilot service with new AI agents designed to help IT teams manage increasing security threats, with 11 agents being added in an April preview, designed to improve security outcomes at machine speed and scale. Recommended read:
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stclarke@Source
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Source Asia
, The Microsoft Cloud Blog
Microsoft is focusing on integrating AI to enhance business productivity, showcasing real-world transformations across industries. Estée Lauder is partnering with Microsoft to reimagine trend forecasting and consumer marketing using AI. They are building a generative AI ecosystem with Copilot Studio, Azure OpenAI Service, and Azure AI Search. This will enable them to gather data, identify trends, build marketing assets, and inform research more efficiently.
Estée Lauder, with almost 80 years of consumer data, will use ConsumerIQ, an agent built in Copilot Studio, to put consumer data at employees' fingertips. This enables the company to rapidly respond to consumer tastes. A marketing director can use natural language prompts to ask ConsumerIQ to find needed information, saving time. Estée Lauder aims to become leaner, faster, and more agile with AI. Generative AI could generate $9 billion to $10 billion in the beauty industry worldwide. Recommended read:
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