News from the AI & ML world
DeeperML - #aiglobal
|
@the-decoder.com
//
OpenAI is making strides in AI customization and application development with the release of Reinforcement Fine-Tuning (RFT) on its o4-mini reasoning model and the appointment of Fidji Simo as the CEO of Applications. The RFT release allows organizations to tailor their versions of the o4-mini model to specific tasks using custom objectives and reward functions, marking a significant advancement in model optimization. This approach utilizes reinforcement learning principles, where developers provide a task-specific grader that evaluates and scores model outputs based on custom criteria, enabling the model to optimize against a reward signal and align with desired behaviors.
Reinforcement Fine-Tuning is particularly valuable for complex or subjective tasks where ground truth is difficult to define. By using RFT on o4-mini, a compact reasoning model optimized for text and image inputs, developers can fine-tune for high-stakes, domain-specific reasoning tasks while maintaining computational efficiency. Early adopters have demonstrated the practical potential of RFT. This capability allows developers to tweak the model to better fit their needs using OpenAI's platform dashboard, deploy it through OpenAI's API, and connect it to internal systems. In a move to scale its AI products, OpenAI has appointed Fidji Simo, formerly CEO of Instacart, as the CEO of Applications. Simo will oversee the scaling of AI products, leveraging her extensive experience in consumer tech to drive revenue generation from OpenAI's research and development efforts. Previously serving on OpenAI's board of directors, Simo's background in leading development at Facebook suggests a focus on end-users rather than businesses, potentially paving the way for new subscription services and products aimed at a broader audience. OpenAI is also rolling out a new GitHub connector for ChatGPT's deep research agent, allowing users with Plus, Pro, or Team subscriptions to connect their repositories and ask questions about their code. Recommended read:
References :
@the-decoder.com
//
OpenAI is expanding its global reach through strategic partnerships with governments and the introduction of advanced model customization tools. The organization has launched the "OpenAI for Countries" program, an initiative designed to collaborate with governments worldwide on building robust AI infrastructure. This program aims to assist nations in setting up data centers and adapting OpenAI's products to meet local language and specific needs. OpenAI envisions this initiative as part of a broader global strategy to foster cooperation and advance AI capabilities on an international scale.
This expansion also includes technological advancements, with OpenAI releasing Reinforcement Fine-Tuning (RFT) for its o4-mini reasoning model. RFT enables enterprises to fine-tune their own versions of the model using reinforcement learning, tailoring it to their unique data and operational requirements. This allows developers to customize the model to better fit their needs using OpenAI’s platform dashboard, tweaking it for internal terminology, goals, processes and more. Once deployed, if an employee or leader at the company wants to use it through a custom internal chatbot orcustom OpenAI GPTto pull up private, proprietary company knowledge, answer specific questions about company products and policies, or generate new communications and collateral in the company’s voice, they can do so more easily with their RFT version of the model. The "OpenAI for Countries" program is slated to begin with ten international projects, supported by funding from both OpenAI and participating governments. Chris Lehane, OpenAI's vice president of global policy, indicated that the program was inspired by the AI Action Summit in Paris, where several countries expressed interest in establishing their own "Stargate"-style projects. Moreover, the release of RFT on o4-mini signifies a major step forward in custom model optimization, offering developers a powerful new technique for tailoring foundation models to specialized tasks. This allows for fine-grained control over how models improve, by defining custom objectives and reward functions. Recommended read:
References :
Dashveenjit Kaur@AI News
//
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:
References :
Ryan Daws@AI News
//
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:
References :
Ryan Daws@AI News
//
References:
venturebeat.com
, AI News
,
DeepSeek, a Chinese AI startup, is making waves in the artificial intelligence industry with its DeepSeek-V3 model. This model is demonstrating performance that rivals Western AI models like those from OpenAI and Anthropic, but at significantly lower development costs. The release of DeepSeek-V3 is seen as jumpstarting AI development across China, with other startups and established companies releasing their own advanced models, further fueling competition. This has narrowed the technology gap between China and the United States as China has adapted to and overcome international restrictions through creative approaches to AI development.
One particularly notable aspect of DeepSeek-V3 is its ability to run efficiently on consumer-grade hardware, such as the Mac Studio with an M3 Ultra chip. Reports indicate that the model achieves speeds of over 20 tokens per second on this platform, making it a potential "nightmare for OpenAI". This contrasts sharply with the data center requirements typically associated with state-of-the-art AI models. The company's focus on algorithmic efficiency has allowed them to achieve notable gains despite restricted access to the latest silicon, showcasing that Chinese AI innovation has flourished by focusing on algorithmic efficiency and novel approaches to model architecture. Recommended read:
References :
|
BenchmarksBlogsResearch Tools |