News from the AI & ML world
DeeperML - #opensourceai
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Chris McKay@Maginative
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OpenAI has secured a massive $40 billion funding round, led by SoftBank, catapulting its valuation to an unprecedented $300 billion. This landmark investment makes OpenAI the world's second-most valuable private company alongside TikTok parent ByteDance Ltd, trailing only Elon Musk's SpaceX Corp. This deal marks one of the largest capital infusions in the tech industry and signifies a major milestone for the company, underscoring the escalating significance of AI.
The fresh infusion of capital is expected to fuel several key initiatives at OpenAI. The funding will support expanded research and development, and upgrades to computational infrastructure. This includes the upcoming release of a new open-weight language model with enhanced reasoning capabilities. OpenAI said the funding round would allow the company to “push the frontiers of AI research even further” and “pave the way” towards AGI, or artificial general intelligence. Recommended read:
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Michael Nuñez@venturebeat.com
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OpenAI, the company behind ChatGPT, has announced a significant strategic shift by planning to release its first open-weight AI model since 2019. This move comes amidst mounting economic pressures from competitors like DeepSeek and Meta, whose open-source models are increasingly gaining traction. CEO Sam Altman revealed the plans on X, stating that the new model will have reasoning capabilities and allow developers to run it on their own hardware, departing from OpenAI's cloud-based subscription model.
This decision marks a notable change for OpenAI, which has historically defended closed, proprietary models. The company is now looking to gather developer feedback to make the new model as useful as possible, planning events in San Francisco, Europe and Asia-Pacific. As models improve, startups and developers increasingly want more tunable latency, and want to use on-prem deplouments requiring full data control, according to OpenAI. The shift comes alongside a monumental $40 billion funding round led by SoftBank, which has catapulted OpenAI's valuation to $300 billion. SoftBank will initially invest $10 billion, with the remaining $30 billion contingent on OpenAI transitioning to a for-profit structure by the end of the year. This funding will help OpenAI continue building AI systems that drive scientific discovery, enable personalized education, enhance human creativity, and pave the way toward artificial general intelligence. The release of the open-weight model is expected to help OpenAI compete with the growing number of efficient open-source alternatives and counter the criticisms that have come from remaining a closed model. Recommended read:
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Ryan Daws@AI News
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DeepSeek V3-0324 has emerged as a leading AI model, topping benchmarks for non-reasoning AI in an open-source breakthrough. This milestone signifies a significant advancement in the field, as it marks the first time an open weights model has achieved the top position among non-reasoning models. The model's performance surpasses proprietary counterparts and edges it closer to proprietary reasoning models, highlighting the growing viability of open-source solutions for latency-sensitive applications. DeepSeek V3-0324 represents a new era for open-source AI, offering a powerful and adaptable tool for developers and enterprises.
DeepSeek-V3 now runs at 20 tokens per second on Apple’s Mac Studio, presenting a challenge to OpenAI’s cloud-dependent business model. The 685-billion-parameter model, DeepSeek-V3-0324, is freely available for commercial use under the MIT license. This achievement, coupled with its cost efficiency and performance, signals a shift in the AI sector, where open-source frameworks increasingly compete with closed systems. Early testers report significant improvements over previous versions, positioning DeepSeek's new model above Claude Sonnet 3.5 from Anthropic. Recommended read:
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Ryan Daws@AI News
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DeepSeek V3-0324, the latest large language model from Chinese AI startup DeepSeek, is making waves in the artificial intelligence industry. The model, quietly released with an MIT license for commercial use, has quickly become the highest-scoring non-reasoning model on the Artificial Analysis Intelligence Index. This marks a significant milestone for open-source AI, surpassing proprietary counterparts like Google’s Gemini 2.0 Pro, Anthropic’s Claude 3.7 Sonnet, and Meta’s Llama 3.3 70B.
DeepSeek V3-0324's efficiency is particularly notable. Early reports indicate that it can run directly on consumer-grade hardware, specifically Apple’s Mac Studio with an M3 Ultra chip, achieving speeds of over 20 tokens per second. This capability is a major departure from the typical data center requirements associated with state-of-the-art AI. The updated version demonstrates substantial improvements in reasoning and benchmark performance, as well as enhanced Chinese writing proficiency and optimized translation quality. Recommended read:
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Ryan Daws@AI News
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DeepSeek, a Chinese AI company, has released DeepSeek V3-0324, an updated AI model that demonstrates impressive performance. The model is now running at 20 tokens per second on a Mac Studio. This model is said to contain 685 billion parameters and its cost-effectiveness challenges the dominance of American AI models, signaling that China continues to innovate in AI despite chip restrictions. Reports from early testers show improvements over previous versions and the model tops non-reasoning AI models in open-source first.
This new model runs on consumer-grade hardware, specifically Apple's Mac Studio with the M3 Ultra chip, diverging from the typical data center requirements for AI. It is freely available for commercial use under the MIT license. According to AI researcher Awni Hannun, the model runs at over 20 tokens per second on a 512GB M3 Ultra. The company has made no formal announcement, just an empty README file and the model weights themselves. This stands in contrast to the carefully orchestrated product launches by Western AI companies. Recommended read:
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Dashveenjit Kaur@AI News
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Chinese AI startup DeepSeek is shaking up the global technology landscape with its latest large language model, DeepSeek-V3-0324. This new model has been lauded for matching the performance of American AI models, while boasting significantly lower development costs. According to Lee Kai-fu, CEO of Chinese startup 01.AI, the gap between Chinese and American AI capabilities has narrowed dramatically, with China even ahead in some specific areas.
DeepSeek-V3-0324 features enhanced reasoning capabilities and improved performance in multiple benchmarks, particularly in mathematics. The model scored 59.4 on the American Invitational Mathematics Examination (AIME), a significant improvement over its predecessor. Häme University lecturer Kuittinen Petri noted DeepSeek's achievements were realized with just a fraction of the resources available to competitors like OpenAI. This breakthrough has been attributed to DeepSeek’s focus on algorithmic efficiency and novel approaches to model architecture, allowing them to overcome restrictions on access to the latest silicon. This disruption is not going unnoticed, when DeepSeek launched its R1 model in January, America’s Nasdaq plunged 3.1%, while the S&P 500 fell 1.5%. While DeepSeek claimed a $5.6 million training cost, this represented only the marginal cost of the final training run. SemiAnalysis estimates DeepSeek's actual hardware investment at closer to $1.6 billion, with hundreds of millions in operating costs. The developments present opportunities and challenges for the. Recommended read:
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Ryan Daws@AI News
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NVIDIA has launched Dynamo, an open-source inference software, designed to accelerate and scale reasoning models within AI factories. Dynamo succeeds the NVIDIA Triton Inference Server, representing a new generation of AI inference software specifically engineered to maximize token revenue generation for AI factories deploying reasoning AI models. The software orchestrates and accelerates inference communication across thousands of GPUs, utilizing disaggregated serving.
Dynamo optimizes AI factories by dynamically managing GPU resources in real-time to adapt to request volumes. Dynamo’s intelligent inference optimizations have shown to boost the number of tokens generated by over 30 times per GPU and has demonstrated the ability to double the performance and revenue of AI factories serving Llama models on NVIDIA’s current Hopper platform. Recommended read:
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Alex Knapp,@Alex Knapp
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Meta
, Alex Knapp
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Meta's open-source large language model (LLM), Llama, has achieved a significant milestone, surpassing one billion downloads since its release in 2023. This achievement underscores the growing influence of Llama in the AI community, attracting both researchers and enterprises seeking to integrate it into various applications. The model's popularity has surged, with companies like Spotify, AT&T, and DoorDash adopting Llama-based models for production environments.
Meta views open sourcing AI models as crucial, with each download of Llama moving closer to this goal. However, Llama's widespread use hasn't been without its challenges, including copyright lawsuits alleging training on copyrighted books without permission. The company plans to introduce multimodal models and improved reasoning capabilities. Additionally, Meta has been working to incorporate innovations from competing models to enhance Llama's performance. Recommended read:
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Jason Corso,@AI News | VentureBeat
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cset.georgetown.edu
, AI News | VentureBeat
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The open-source AI landscape is currently facing challenges related to transparency, maintainability, and evaluation. Selective transparency is raising concerns, as truly open-source AI should allow for inspection, experimentation, and understanding of all contributing elements. In tandem, open-source maintainers report being overwhelmed by a surge in junk bug reports generated by AI systems. These reports, often low-quality and hallucinated, require time and effort to refute, increasing the workload for maintainers.
Efforts are underway to improve the red-teaming of AI systems to enhance understanding and governance. A recent workshop highlighted challenges and offered recommendations for better AI evaluations. While the policy landscape has shifted towards prioritizing AI innovation, evaluations like red-teaming remain critical for identifying safety and security risks. This involves emulating attacker tactics to "break" AI models and identifying unwanted outputs. Recommended read:
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Jason Corso,@AI News | VentureBeat
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AI News | VentureBeat
, Windows Copilot News
The increasing use of AI in software development and security analysis is presenting new challenges for open-source projects. While open-source AI tools are gaining traction due to faster development and innovation, maintainers are now facing a surge of low-quality bug reports generated by AI systems. These reports, often described as "spammy" and "hallucinated," appear legitimate at first but waste valuable time as maintainers must investigate and refute them.
The Computer History Museum, in collaboration with Google, has recently released the original 2012 source code for AlexNet, a revolutionary neural network. This release is a significant milestone for AI enthusiasts, enabling deeper understanding and further innovation. However, the flood of AI-generated junk bug reports raises concerns about the impact of AI on the open-source ecosystem, with developers like Seth Larson suggesting such low-quality reports should be treated as potentially malicious. Recommended read:
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Muhammad Zulhusni@AI News
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AI News
, GZERO Media
Several major US artificial intelligence companies have expressed fears that America is losing its edge in AI development. In submissions to the US government in March 2025, these companies warned that Chinese AI models, like DeepSeek R1, are becoming increasingly sophisticated and competitive. The submissions, prompted by a request for input on an AI Action Plan, underscore the growing challenge posed by China in terms of technological capabilities and pricing within the AI sector.
China's growing AI capabilities are exemplified by DeepSeek R1, a state-supported model that has garnered attention from US developers. OpenAI noted that DeepSeek demonstrates a narrowing technological gap between the US and China, expressing concerns about the model's potential to influence global AI development, particularly given its "state-subsidized, state-controlled, and freely available" nature. Competition from China also includes Ernie X1 and Ernie 4.5, released by Baidu, which are designed to compete with Western systems. Recommended read:
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Harsh@Composio
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Composio
, Windows Copilot News
The integration of Artificial Intelligence (AI) into coding and software development is rapidly evolving, sparking both excitement and ethical considerations. GitHub's COO, Kyle Daigle, recently discussed the impact of AI-assisted coding, highlighting tools like GitHub Copilot and the potential of ambient AI to seamlessly integrate into developer workflows. The discussion included licensing concerns and the importance of developers understanding and navigating the ethical complexities that arise with AI-driven development. This comes as developers are exploring AI Agents SDK, a framework that simplifies the creation of multi-agent systems.
The appeal of AI in development is further underscored by the comparison of Agents SDK with alternatives like LangGraph, Autogen, and CrewAI. Each framework offers unique strengths, with Agents SDK focusing on simplicity and production readiness, while LangGraph excels in complex workflows. However, amidst the enthusiasm, ethical considerations are surfacing, most notably the controversy surrounding OpenAI employees questioning the company's military deal with startup Anduril, raising concerns about the potential misuse of AI and its impact on OpenAI's reputation. This highlights the ongoing debate about the ethical boundaries of AI development and deployment, particularly in sensitive areas like defense and healthcare. Recommended read:
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Ryan Daws@AI News
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AI News
The open-source AI movement is gaining momentum, with several significant developments highlighting its growing influence. Hugging Face is actively advocating for an open-source approach in the US government's upcoming AI Action Plan, emphasizing that innovation thrives with diverse contributors and accessible infrastructure. They propose focusing on strengthening open-source AI ecosystems, promoting efficient AI adoption, and establishing robust security standards.
The All Things Open AI conference saw unexpected success, reflecting the increasing interest in the field. Attendance exceeded expectations, indicating the strong demand for collaborative learning and knowledge sharing within the open-source AI community. This event, a partnership between All Things Open and The Artificially Intelligent Enterprise, featured training sessions and presentations, drawing a large crowd of participants. In a landmark event for AI history, the Computer History Museum, in collaboration with Google, has released the original source code for AlexNet, the groundbreaking neural network that revolutionized AI in 2012. This opens up new avenues for research and understanding of the foundations of modern AI, enabling developers and researchers to delve into the intricacies of AlexNet's architecture and algorithms. This is considered a monumental moment for AI enthusiasts. Recommended read:
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