News/Open-source
Linux Lite adds subtle AI desktop chat feature for better developer support
Linux Lite 7.2 introduces subtle AI integration: The latest release of Linux Lite, a lightweight distribution designed to revive older desktops, has incorporated an AI feature in a more nuanced way than expected. The distribution now includes a website called "Linux Lite AI Chat" that serves as a direct support option for users, answering Linux Lite-specific questions and addressing issues. This approach to AI integration is less intrusive than full desktop integration, which would be challenging to implement across various Linux components and development teams. Key features of Linux Lite 7.2: The new version builds upon its reputation for being...
read Nov 5, 2024PyCharm integrates Hugging Face AI tools for developers
AI-powered development simplification: PyCharm's new Hugging Face integration brings state-of-the-art machine learning capabilities directly into the IDE, making it easier for developers to incorporate advanced AI models into their projects. The integration allows developers to seamlessly insert Hugging Face models into their code through a simple right-click menu option, streamlining the process of implementing complex AI functionalities. Users can browse and select from a wide range of models, including those capable of image-text-to-text tasks, directly within the PyCharm interface. The integration provides instant access to model cards, offering developers crucial information about a model's origin, intended uses, and performance characteristics....
read Nov 5, 2024Argilla’s new feature streamlines data prep and import from the Hugging Face Hub
Argilla 2.4 introduces no-code dataset preparation: Argilla, an open-source data-centric tool for AI developers and domain experts, has released a new feature allowing users to easily import and prepare datasets from the Hugging Face Hub without coding. The update enables users to import any of the 230,000+ datasets available on the Hugging Face Hub directly into Argilla's user interface. Users can define questions and collect human feedback on the imported datasets, streamlining the process of building high-quality datasets for AI projects. This feature is particularly beneficial for domain experts who may lack coding experience but possess valuable knowledge in their...
read Nov 2, 2024Hugging Face launches powerful small AI models to run on smartphones
Breakthrough in compact AI models: Hugging Face has released SmolLM2, a new family of small but powerful language models designed to run efficiently on smartphones and edge devices. Key features and capabilities: SmolLM2 comes in three sizes – 135M, 360M, and 1.7B parameters – offering impressive performance while requiring significantly fewer computational resources than larger models. The 1.7B parameter version outperforms Meta's Llama 1B model on several key benchmarks. SmolLM2 shows significant improvements over its predecessor in instruction following, knowledge, reasoning, and mathematics. The largest variant was trained on 11 trillion tokens using a diverse dataset combination, including FineWeb-Edu and...
read Nov 1, 2024Chinese researchers develop military AI with Meta’s Llama model
Chinese military's AI advancement: China has reportedly developed a military intelligence tool called ChatBIT using Meta's Llama 13B AI model, raising concerns about the potential misuse of open-source AI technology for military purposes. Two Chinese institutions with military ties were involved in creating ChatBIT, which is designed to gather and process military intelligence data. The AI tool was trained on a relatively small dataset of approximately 100,000 military records, suggesting it may be in early stages of development or intended for specific, focused tasks. ChatBIT's potential future applications include military training and analysis, though its current capabilities and deployment status...
read Nov 1, 2024Meta just made its MobileLLM model and weights open to researchers
Breakthrough in mobile AI: Meta AI has open-sourced MobileLLM, a set of language models optimized for mobile devices, marking a significant advancement in efficient, on-device AI technology. The full weights and code for MobileLLM are now available on Hugging Face, allowing researchers to access and build upon this innovative technology. The release is currently under a Creative Commons 4.0 non-commercial license, limiting its use to research purposes and prohibiting commercial applications. Technical innovations: MobileLLM introduces several key advancements to maximize AI performance on resource-constrained devices. The models employ deep, thin architectures instead of traditional wide designs, focusing on depth to...
read Oct 31, 2024Meta is releasing new research to advance embodied AI and robotics
Advancing embodied AI: Meta FAIR is making significant strides in robotics research, aiming to develop AI systems that can perceive and interact with their surroundings as effectively as humans. Meta is releasing several new research artifacts that focus on touch perception, robot dexterity, and human-robot interaction, which are crucial for achieving advanced machine intelligence (AMI). The company is partnering with GelSight Inc and Wonik Robotics to develop and commercialize tactile sensing innovations, fostering an open ecosystem for AI research. Breakthrough in touch perception: Meta Sparsh, a new general-purpose touch representation, is being released to enable AI to perceive what's inaccessible...
read Oct 31, 2024Microsoft’s agentic AI tool OmniParser surges in open source popularity
Revolutionizing AI-GUI Interaction: Microsoft's OmniParser, an open-source generative AI model, has quickly risen to prominence as a groundbreaking tool for enabling large language models (LLMs) to better understand and interact with graphical user interfaces (GUIs). OmniParser has become the top trending model on Hugging Face, a popular AI code repository, marking the first time an agent-related model has achieved this distinction. The tool is designed to convert screenshots into structured data that vision-enabled LLMs like GPT-4V can easily interpret and act upon. This breakthrough addresses a critical need for AI to seamlessly operate across various GUIs as LLMs become increasingly...
read Oct 31, 2024Linux creator Linus Torvalds dismisses AI hype
Linux creator's skepticism on AI hype: Linus Torvalds, the creator of Linux, has expressed strong reservations about the current state of artificial intelligence, describing it as "90 percent marketing and ten percent reality." Torvalds made these comments during an interview at the Open Source Summit in Vienna, highlighting his disillusionment with the AI industry's hype cycle. While acknowledging AI's potential to change the world, Torvalds stated his intention to "basically ignore it" due to what he perceives as the tech industry's poor positioning in the AI space. The Linux creator's skepticism is particularly noteworthy given the widespread use of Linux...
read Oct 29, 2024Open-source AI training data must be disclosed under new OSI rules
AI openness redefined: New standards challenge tech giants: The Open Source Initiative (OSI) has released its official definition of "open" artificial intelligence, setting new criteria that could reshape the landscape of AI development and accessibility. OSI's definition requires AI systems to provide access to training data details, complete code for building and running the AI, and the settings and weights from the training process. This new standard directly challenges some widely promoted open-source AI models, including Meta's Llama, which falls short of meeting these criteria. The definition aims to bring transparency and reproducibility to AI systems, aligning them with long-standing...
read Oct 28, 2024Why open-source development is crucial for the future of AI
Open Source AI: Driving Innovation Beyond the Headlines: The open-source movement is quietly revolutionizing the AI landscape, providing accessible tools and technologies for individuals and smaller organizations outside the realm of big tech companies. Open-source software is free to use, modify, and share, encouraging collaboration and continuous improvement without restrictions on usage. The concept dates back to the 1950s and has been instrumental in developing critical technologies like the Internet and World Wide Web. Stable Diffusion: A Prime Example of Open-Source AI Success: Since its launch in 2022, Stable Diffusion has become a cornerstone of open-source AI image generation technology....
read Oct 26, 2024Meta releases ‘quantized models’ to efficiently run AI on mobile devices
Quantized Llama models: A leap forward in mobile AI: Meta has released lightweight quantized versions of their Llama 3.2 1B and 3B language models, designed to run efficiently on popular mobile devices while maintaining high performance and accuracy. Key advancements in model efficiency: The quantized models achieve a 2-4x speedup compared to their original counterparts. Model size has been reduced by an average of 56%. Memory usage has decreased by an average of 41%. These improvements enable on-device AI capabilities with enhanced privacy and speed. Quantization techniques employed: Quantization-Aware Training with LoRA adaptors (QLoRA): This method prioritizes accuracy by simulating...
read Oct 25, 2024Meta beats Apple and Google in the race to put powerful AI on mobile devices
AI comes to your pocket: Meta's breakthrough in mobile AI technology: Meta Platforms has developed compressed versions of its Llama artificial intelligence models that can run efficiently on smartphones and tablets, potentially revolutionizing how we interact with AI in our daily lives. Technological innovation driving mobile AI: Meta's achievement in compressing large language models for mobile devices represents a significant leap forward in AI accessibility and functionality. The company has created smaller versions of its Llama 3.2 1B and 3B models that run up to four times faster while using less than half the memory of earlier versions. These compressed...
read Oct 24, 2024Google DeepMind open-sources AI text watermarking technology
Google DeepMind advances AI text watermarking: Google has open-sourced SynthID, a tool designed to identify AI-generated text, as part of its broader effort to promote responsible AI development. Key features of SynthID: Adds an invisible watermark directly into AI-generated content Works by altering the probability of token generation in large language models Can detect watermarked text by comparing probability scores with unwatermarked text Deployed in Google's Gemini products and available on Hugging Face Performance and limitations: Extensive testing showed no compromise in quality, accuracy, creativity, or speed of generated text Resistant to light editing and cropping but less reliable with...
read Oct 22, 2024Stability just launched Stable Diffusion 3.5 in big move for open-source AI art
A new era for text-to-image AI: Stability AI has launched Stable Diffusion 3.5, a significant update to its open-source text-to-image generative AI technology, aiming to reclaim leadership in the competitive field. The release introduces three new model variants: Stable Diffusion 3.5 Large (8 billion parameters), Large Turbo (a faster version), and Medium (2.6 billion parameters for edge computing). All models are available under the Stability AI Community License, allowing free non-commercial use and commercial use for entities with annual revenue under $1 million. Enterprise licenses are available for larger deployments, with models accessible via Stability AI's API and Hugging Face....
read Oct 20, 2024Why some insiders believe OSI’s new open-source definition falls short
Open Source Initiative's controversial AI definition sparks debate: The Open Source Initiative (OSI) has proposed a new definition for Open Source AI, triggering significant criticism and concerns within the open source community. Key points of contention: The proposed definition includes access to training datasets as part of the "source" for AI models, which some argue goes beyond traditional open source principles. Critics, including Bruce Perens, the original author of the Open Source Definition (OSD), have voiced strong opposition to the OSI's approach. The OSI has been accused of censorship and authoritarianism in managing the discussion around the new definition. Community...
read Oct 20, 20245 AI predictions Lex Fridman got right for 2024
AI's rapid evolution in 2024: Lex Fridman's predictions from early in the year have largely come to fruition, showcasing the accelerating pace of artificial intelligence development and its impact across various sectors. Personalized LLMs and edge computing: The concept of running large language models on individual devices has gained significant traction, marking a shift away from cloud-based processing. Advances in hardware and neural network design have made it possible to operate decent LLMs on standard endpoint devices. This trend reverses the decade-long move towards centralized data processing, potentially revolutionizing how individuals interact with AI in their daily lives. The ability...
read Oct 19, 2024Meta just released Spirit LM, an open-source multimodal AI model
Introducing Meta Spirit LM: Meta has unveiled a groundbreaking open-source multimodal language model that seamlessly integrates text and speech inputs and outputs, challenging competitors like OpenAI's GPT-4o and Hume's EVI 2. Developed by Meta's Fundamental AI Research (FAIR) team, Spirit LM aims to address limitations in existing AI voice experiences by offering more expressive and natural-sounding speech generation. The model is capable of learning tasks across modalities, including automatic speech recognition (ASR), text-to-speech (TTS), and speech classification. Currently, Spirit LM is only available for non-commercial usage under Meta's FAIR Noncommercial Research License. Advanced approach to text and speech processing: Spirit...
read Oct 18, 2024Meta shares massive AI materials database to accelerate research
Meta's game-changing contribution to materials science AI: Meta has released a massive open-source data set and AI models called Open Materials 2024 (OMat24) to accelerate the discovery of new materials using artificial intelligence. The big picture: The OMat24 release addresses a critical bottleneck in materials discovery by providing researchers with an extensive, high-quality data set and AI models that were previously unavailable or proprietary. Meta's decision to make OMat24 freely available and open-source stands in contrast to other industry players like Google and Microsoft, who have kept their competitive models and data sets secret. The data set contains approximately 110...
read Oct 17, 2024Meta unveils open-source AI hardware strategy
The evolution of Meta's AI infrastructure: Meta's journey in scaling its AI capabilities has led to significant advancements in hardware design and infrastructure optimization to support increasingly complex AI models and workloads. Meta has been integrating AI into its core products for years, including features like Feed and its advertising system. The company's latest AI model, Llama 3.1 405B, boasts 405 billion parameters and required training across more than 16,000 NVIDIA H100 GPUs. Meta's AI training clusters have rapidly scaled from 128 GPUs to two 24,000-GPU clusters in just over a year, with expectations for continued growth. Networking challenges and...
read Oct 17, 2024These AI models outperform open-source peers but lag behind humans
AI's struggle with visual reasoning puzzles: Recent research from the USC Viterbi School of Engineering Information Sciences Institute (ISI) tested the ability of multi-modal large language models (MLLMs) to solve abstract visual puzzles similar to those found on human IQ tests, revealing significant limitations in AI's cognitive abilities. The study, presented at the Conference on Language Modeling (COLM 2024) in Philadelphia, focused on evaluating the nonverbal abstract reasoning abilities of both open-source and closed-source MLLMs. Researchers used puzzles developed from Raven's Progressive Matrices, a standard type of abstract reasoning test, to challenge the AI models' visual perception and logical reasoning...
read Oct 17, 2024‘Arch-Function’ AI models are purpose-built for lightning fast agentic AI
Revolutionizing enterprise AI with Arch-Function LLMs: Katanemo's open-source release of Arch-Function large language models (LLMs) promises to significantly accelerate agentic AI applications for complex enterprise workflows. The big picture: Arch-Function LLMs, built on Qwen 2.5 with 3B and 7B parameters, offer ultra-fast speeds for function-calling tasks critical to agentic workflows, potentially outperforming industry leaders like OpenAI's GPT-4 and Anthropic's models. Katanemo claims Arch-Function models are nearly 12 times faster than GPT-4 while delivering significant cost savings. The open-source release aims to enable super-responsive agents capable of handling domain-specific use cases without excessive costs for businesses. Gartner predicts that by 2028,...
read Oct 15, 2024What to know about OpenAI’s new Swarm AI agent framework
Introducing OpenAI's Swarm framework: OpenAI has launched an experimental tool called Swarm, designed to orchestrate networks of AI agents, offering a unique blend of simplicity, flexibility, and control in multi-agent collaboration. Key features and design philosophy: Swarm emphasizes simplicity and transparency in agent interactions, utilizing a stateless design through the Chat Completions API. The framework focuses on ease of understanding and implementation, making it accessible to developers new to multi-agent systems. Swarm's stateless model means agents do not retain memory between interactions, which contributes to its simplicity but limits its use for complex, context-dependent tasks. Developers have more granular control...
read Oct 14, 2024AI model DeepSeek uses synthetic data to prove complex theorems
Breakthrough in AI-driven theorem proving: DeepSeek-Prover, a new large language model (LLM), has achieved significant advancements in formal theorem proving, outperforming previous models and demonstrating the potential of synthetic data in enhancing mathematical reasoning capabilities. Key innovation - Synthetic data generation: The researchers addressed the lack of training data for theorem proving by developing a novel approach to generate extensive Lean 4 proof data. The synthetic data is derived from high-school and undergraduate-level mathematical competition problems. The process involves translating natural language problems into formal statements, filtering out low-quality content, and generating proofs. This approach resulted in a dataset of...
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