News from the AI & ML world
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@ComputerWeekly.com
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Meta and the UK Government have joined forces to launch a £1 million ‘Open Source AI Fellowship’ program. The goal is to embed some of the UK’s most promising AI experts within Whitehall, the UK government's administrative center, to develop advanced AI tools. These tools will aim to improve government agility and contribute to the delivery of the Plan for Change. The Alan Turing Institute is also backing the fellowship.
The program intends to harness the power of open source AI models, including Meta's Llama models. These models have shown great potential for scientific and medical breakthroughs and could transform public service delivery. Fellows will work within government departments, potentially contributing to high-security use cases like AI-powered language translation for national security, or speeding up the approval process for house building by leveraging construction planning data. The fellowship is a practical response to the growing demand for generative AI talent. It will provide engineers a chance to address high-impact public sector challenges, which aims to create transparent, sovereign AI infrastructure that can scale across departments while reducing costs and enhancing productivity. Technology Secretary Peter Kyle emphasizes the aim is to create open, practical AI tools "built for public good," focusing on delivery rather than just ideas and developing sovereign capabilities in areas like national security and critical infrastructure. Recommended read:
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@colab.research.google.com
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Google's Magenta project has unveiled Magenta RealTime (Magenta RT), an open-weights live music model designed for interactive music creation, control, and performance. This innovative model builds upon Google DeepMind's research in real-time generative music, providing opportunities for unprecedented live music exploration. Magenta RT is a significant advancement in AI-driven music technology, offering capabilities for both skill-gap accessibility and enhancement of existing musical practices. As an open-weights model, Magenta RT is targeted towards eventually running locally on consumer hardware, showcasing Google's commitment to democratizing AI music creation tools.
Magenta RT, an 800 million parameter autoregressive transformer model, was trained on approximately 190,000 hours of instrumental stock music. It leverages SpectroStream for high-fidelity audio (48kHz stereo) and a newly developed MusicCoCa embedding model, inspired by MuLan and CoCa. This combination allows users to dynamically shape and morph music styles in real-time by manipulating style embeddings, effectively blending various musical styles, instruments, and attributes. The model code is available on Github and the weights are available on Google Cloud Storage and Hugging Face under permissive licenses with some additional bespoke terms. Magenta RT operates by generating music in sequential chunks, conditioned on both previous audio output and style embeddings. This approach enables the creation of interactive soundscapes for performances and virtual spaces. Impressively, the model achieves a real-time factor of 1.6 on a Colab free-tier TPU (v2-8 TPU), generating two seconds of audio in just 1.25 seconds. This technology unlocks the potential to explore entirely new musical landscapes, experiment with never-before-heard instrument combinations, and craft unique sonic textures, ultimately fostering innovative forms of musical expression and performance. Recommended read:
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@www.marktechpost.com
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Mistral AI has released Mistral Small 3.2, an updated version of its open-source model, Mistral-Small-3.2-24B-Instruct-2506, building upon the earlier Mistral-Small-3.1-24B-Instruct-2503. This update focuses on enhancing the model’s overall reliability and efficiency, particularly in handling complex instructions, minimizing repetitive outputs, and maintaining stability during function-calling scenarios. The improvements aim to refine specific behaviors such as instruction following, output stability, and function calling robustness without altering the core architecture.
A significant enhancement in Mistral Small 3.2 is its improved accuracy in executing precise instructions. Benchmark scores reflect this improvement, with the model achieving 65.33% accuracy on the Wildbench v2 instruction test, up from 55.6% for its predecessor. Performance on the challenging Arena Hard v2 test nearly doubled, increasing from 19.56% to 43.1%, demonstrating an enhanced ability to understand and execute intricate commands accurately. Internally, Mistral’s accuracy rose from 82.75% in Small 3.1 to 84.78% in Small 3.2. Mistral Small 3.2 also addresses the issue of repetitive errors by significantly reducing instances of infinite or repetitive output, a common problem in extended conversational scenarios. Internal evaluations show a decrease in infinite generation errors by nearly half, from 2.11% in Small 3.1 to 1.29%. The updated model also demonstrates enhanced capability in calling functions, making it more suitable for automation tasks. Additionally, Mistral AI emphasizes its compliance with EU regulations like GDPR and the EU AI Act, making it an appealing choice for developers in the region. Recommended read:
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Thomas Macaulay@The Next Web
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The Next Web
Europe is setting its sights on becoming a leader in Artificial Intelligence applications, even if it can't compete with the US in AI hardware. Tech leaders at the TNW Conference emphasized that Europe needs to capitalize on its existing strengths in application development. The focus should be on building innovative AI applications on top of the AI infrastructure being established by US companies. This strategy allows Europe to leverage the massive investments being made in datacenters, networking, and cloud services by major players like Meta, Amazon, Alphabet, and Microsoft.
The advantage the US has in AI infrastructure could become a launchpad for European software. Europe already boasts successful app companies like Spotify, Grammarly, Revolut, and Klarna, and this presents an opportunity for a new wave of AI-driven applications to emerge from the region. Experts call for financial changes, with a greater risk appetite from investors, less red tape around public funding, and more local procurement. Innovation-friendly regulation is also needed. The EU is also taking steps to achieve digital sovereignty, acknowledging its dependence on foreign technologies. To reduce reliance on Big Tech, the EU needs real investment in public digital infrastructure. Open Source AI has been identified as a key factor in achieving this, focusing on power, trust, and sovereignty. It emphasizes European culture and values. The European Parliament recognizes that Europe cannot base its digital economy on infrastructures it doesn't control, and must ensure a secure, trustworthy, and innovation-driven digital ecosystem. Recommended read:
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Carl Franzen@AI News | VentureBeat
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Mistral AI has launched its first reasoning model, Magistral, signaling a commitment to open-source AI development. The Magistral family features two models: Magistral Small, a 24-billion parameter model available with open weights under the Apache 2.0 license, and Magistral Medium, a proprietary model accessible through an API. This dual release strategy aims to cater to both enterprise clients seeking advanced reasoning capabilities and the broader AI community interested in open-source innovation.
Mistral's decision to release Magistral Small under the permissive Apache 2.0 license marks a significant return to its open-source roots. The license allows for the free use, modification, and distribution of the model's source code, even for commercial purposes. This empowers startups and established companies to build and deploy their own applications on top of Mistral’s latest reasoning architecture, without the burdens of licensing fees or vendor lock-in. The release serves as a powerful counter-narrative, reaffirming Mistral’s dedication to arming the open community with cutting-edge tools. Magistral Medium demonstrates competitive performance in the reasoning arena, according to internal benchmarks released by Mistral. The model was tested against its predecessor, Mistral-Medium 3, and models from Deepseek. Furthermore, Mistral's Agents API's Handoffs feature facilitates smart, multi-agent workflows, allowing different agents to collaborate on complex tasks. This enables modular and efficient problem-solving, as demonstrated in systems where agents collaborate to answer inflation-related questions. Recommended read:
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@siliconangle.com
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Hugging Face, primarily known as a platform for machine learning and AI development, is making a significant push into the robotics field with the introduction of two open-source robot designs: HopeJR and Reachy Mini. HopeJR is a full-sized humanoid robot boasting 66 degrees of freedom, while Reachy Mini is a desktop unit. The company aims to democratize robotics by offering accessible and open-source platforms for development and experimentation. These robots are intended to serve as tools for AI developers, similar to a Raspberry Pi, facilitating the testing and integration of AI applications in robotic systems. Hugging Face anticipates shipping initial units by the end of the year.
HopeJR, co-designed with French robotics company The Robot Studio, is capable of walking and manipulating objects. According to Hugging Face Principal Research Scientist Remi Cadene, it can perform 66 movements including walking. Priced around $3,000, HopeJR is positioned to compete with offerings like Unitree's G1. CEO Clem Delangue emphasized the importance of the robots being open source. He stated that this enables anyone to assemble, rebuild, and understand how they work and remain affordable, ensuring that robotics isn’t dominated by a few large corporations with black-box systems. This approach lowers the barrier to entry for researchers and developers, fostering innovation and collaboration in the robotics community. Reachy Mini is a desktop robot designed for AI application testing. Resembling a "Wall-E-esque statue bust" according to reports, Reachy Mini features a retractable neck that allows it to follow the user with its head and auditory interaction. Priced between $250 and $300, Reachy Mini is intended to be used to test AI applications before deploying them to production. Hugging Face's expansion into robotics includes the acquisition of Pollen Robotics, a company specializing in humanoid robot technology, and the release of AI models specifically designed for robots, as well as the SO-101, a 3D-printable robotic arm. Recommended read:
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@www.marktechpost.com
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DeepSeek, a Chinese AI startup, has launched an updated version of its R1 reasoning AI model, named DeepSeek-R1-0528. This new iteration brings the open-source model near parity with proprietary paid models like OpenAI’s o3 and Google’s Gemini 2.5 Pro in terms of reasoning capabilities. The model is released under the permissive MIT License, enabling commercial use and customization, marking a commitment to open-source AI development. The model's weights and documentation are available on Hugging Face, facilitating local deployment and API integration.
The DeepSeek-R1-0528 update introduces substantial enhancements in the model's ability to handle complex reasoning tasks across various domains, including mathematics, science, business, and programming. DeepSeek attributes these improvements to leveraging increased computational resources and applying algorithmic optimizations in post-training. Notably, the accuracy on the AIME 2025 test has surged from 70% to 87.5%, demonstrating deeper reasoning processes with an average of 23,000 tokens per question, compared to the previous version's 12,000 tokens. Alongside enhanced reasoning, the updated R1 model boasts a reduced hallucination rate, which contributes to more reliable and consistent output. Code generation performance has also seen a boost, positioning it as a strong contender in the open-source AI landscape. DeepSeek provides instructions on its GitHub repository for those interested in running the model locally and encourages community feedback and questions. The company aims to provide accessible AI solutions, underscored by the availability of a distilled version of R1-0528, DeepSeek-R1-0528-Qwen3-8B, designed for efficient single-GPU operation. Recommended read:
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@www.marktechpost.com
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DeepSeek has released a major update to its R1 reasoning model, dubbed DeepSeek-R1-0528, marking a significant step forward in open-source AI. The update boasts enhanced performance in complex reasoning, mathematics, and coding, positioning it as a strong competitor to leading commercial models like OpenAI's o3 and Google's Gemini 2.5 Pro. The model's weights, training recipes, and comprehensive documentation are openly available under the MIT license, fostering transparency and community-driven innovation. This release allows researchers, developers, and businesses to access cutting-edge AI capabilities without the constraints of closed ecosystems or expensive subscriptions.
The DeepSeek-R1-0528 update brings several core improvements. The model's parameter count has increased from 671 billion to 685 billion, enabling it to process and store more intricate patterns. Enhanced chain-of-thought layers deepen the model's reasoning capabilities, making it more reliable in handling multi-step logic problems. Post-training optimizations have also been applied to reduce hallucinations and improve output stability. In practical terms, the update introduces JSON outputs, native function calling, and simplified system prompts, all designed to streamline real-world deployment and enhance the developer experience. Specifically, DeepSeek R1-0528 demonstrates a remarkable leap in mathematical reasoning. On the AIME 2025 test, its accuracy improved from 70% to an impressive 87.5%, rivaling OpenAI's o3. This improvement is attributed to "enhanced thinking depth," with the model now utilizing significantly more tokens per question, indicating more thorough and systematic logical analysis. The open-source nature of DeepSeek-R1-0528 empowers users to fine-tune and adapt the model to their specific needs, fostering further innovation and advancements within the AI community. Recommended read:
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@www.marktechpost.com
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AI News | VentureBeat
, www.foo.be
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Multi-agent AI systems are rapidly advancing, shifting the focus from single, powerful AI models to collaborative networks of specialized AI agents. These agents, each possessing unique skills, can work together to tackle complex tasks, mimicking the dynamics of a team of expert colleagues. Successfully orchestrating these systems requires careful architectural design, shared knowledge management, and robust failure planning, as highlighted by industry discussions and enabled by modern platforms like Microsoft AutoGen and LangGraph. The challenge lies in coordinating these independent agents, ensuring seamless communication, shared understanding, and consistent operation in the face of potential failures.
Architectural frameworks play a crucial role in managing agent interactions. Solid architectural blueprints are essential for reliability and scale, addressing the challenges of independent agents, complex communication, shared state management, and inevitable failures. Tools like Microsoft AutoGen streamline the development of multi-agent workflows, allowing developers to focus on defining agent expertise and system prompts rather than intricate plumbing. AutoGen facilitates the creation of cohesive "DeepDive" tools by orchestrating specialist assistants such as Researchers, FactCheckers, Critics, Summarizers, and Editors. The long-term sustainability of open-source projects is also critical. When selecting open-source projects, the presence of a Contributor License Agreement (CLA) can be a strong indicator of potential risks. CLAs can be misused to lock in contributions, allowing the original creator to relicense the work under different terms. Conversely, a Developer Certificate of Origin (DCO) is typically a positive sign, indicating respect for contributors and a focus on building a healthy, sustainable community. Examining whether a project merges pull requests from external contributors is another important indicator of its commitment to open collaboration and long-term viability. Recommended read:
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@venturebeat.com
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Nvidia has launched Parakeet-TDT-0.6B-V2, a fully open-source transcription AI model, on Hugging Face. This represents a new standard for Automatic Speech Recognition (ASR). The model, boasting 600 million parameters, has quickly topped the Hugging Face Open ASR Leaderboard with a word error rate of just 6.05%. This level of accuracy positions it near proprietary transcription models, such as OpenAI’s GPT-4o-transcribe and ElevenLabs Scribe, making it a significant advancement in open-source speech AI. Parakeet operates under a commercially permissive CC-BY-4.0 license.
The speed of Parakeet-TDT-0.6B-V2 is a standout feature. According to Hugging Face’s Vaibhav Srivastav, it can "transcribe 60 minutes of audio in 1 second." Nvidia reports this is achieved with a real-time factor of 3386, meaning it processes audio 3386 times faster than real-time when running on Nvidia's GPU-accelerated hardware. This speed is attributed to its transformer-based architecture, fine-tuned with high-quality transcription data and optimized for inference on NVIDIA hardware using TensorRT and FP8 quantization. The model also supports punctuation, capitalization, and detailed word-level timestamping. Parakeet-TDT-0.6B-V2 is aimed at developers, researchers, and industry teams building various applications. This includes transcription services, voice assistants, subtitle generators, and conversational AI platforms. Its accessibility and performance make it an attractive option for commercial enterprises and indie developers looking to build speech recognition and transcription services into their applications. With its release on May 1, 2025, Parakeet is set to make a considerable impact on the field of speech AI. Recommended read:
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Alexey Shabanov@TestingCatalog
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Alibaba Cloud has unveiled Qwen 3, a new generation of large language models (LLMs) boasting 235 billion parameters, poised to challenge the dominance of US-based models. This open-weight family of models includes both dense and Mixture-of-Experts (MoE) architectures, offering developers a range of choices to suit their specific application needs and hardware constraints. The flagship model, Qwen3-235B-A22B, achieves competitive results in benchmark evaluations of coding, math, and general knowledge, positioning it as one of the most powerful publicly available models.
Qwen 3 introduces a unique "thinking mode" that can be toggled for step-by-step reasoning or rapid direct answers. This hybrid reasoning approach, similar to OpenAI's "o" series, allows users to engage a more intensive process for complex queries in fields like science, math, and engineering. The models are trained on a massive dataset of 36 trillion tokens spanning 119 languages, twice the corpus of Qwen 2.5 and enriched with synthetic math and code data. This extensive training equips Qwen 3 with enhanced reasoning, multilingual proficiency, and computational efficiency. The release of Qwen 3 includes two MoE models and six dense variants, all licensed under Apache-2.0 and downloadable from platforms like Hugging Face, ModelScope, and Kaggle. Deployment guidance points to vLLM and SGLang for servers and to Ollama or llama.cpp for local setups, signaling support for both cloud and edge developers. Community feedback has been positive, with analysts noting that earlier Qwen announcements briefly lifted Alibaba shares, underscoring the strategic weight the company places on open models. Recommended read:
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