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@www.businessinsider.com - 39d
OpenAI has announced plans to transition its for-profit arm into a Public Benefit Corporation (PBC) in Delaware, a move aimed at ensuring its long-term sustainability while maintaining its mission. This structural change is designed to balance profit generation with the company's broader goals, particularly in healthcare, education, and science, which will be pursued by its non-profit arm. The PBC structure will allow OpenAI to raise necessary capital while also maintaining a public benefit interest in its decision making. The company has also indicated the need to become an enduring company as it moves into 2025.
This transition comes with a clarified definition of Artificial General Intelligence (AGI), defining it as a system capable of generating over $100 billion in profits. This definition, agreed upon with Microsoft, is important as it triggers a clause in their agreement, granting them access to advanced models only before AGI is reached. There are reports the company may be trying to remove this clause as well. The move comes after a year in which OpenAI has experienced large losses, with the company reportedly not expected to turn a profit until 2029. Recommended read:
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@www.marktechpost.com - 26d
AMD researchers, in collaboration with Johns Hopkins University, have unveiled Agent Laboratory, an innovative autonomous framework powered by large language models (LLMs). This tool is designed to automate the entire scientific research process, significantly reducing the time and costs associated with traditional methods. Agent Laboratory handles tasks such as literature review, experimentation, and report writing, with the option for human feedback at each stage. The framework uses specialized agents, such as "PhD" agents for literature reviews, "ML Engineer" agents for experimentation, and "Professor" agents for compiling research reports.
The Agent Laboratory's workflow is structured around three main components: Literature Review, Experimentation, and Report Writing. The system retrieves and curates research papers, generates and tests machine learning code, and compiles findings into comprehensive reports. AMD has reported that using the o1-preview LLM within the framework produces the most optimal research results, which can assist researchers by allowing them to focus on creative and conceptual aspects of their work while automating more repetitive tasks. The tool aims to streamline research, reduce costs, and improve the quality of scientific outcomes, with a reported 84% reduction in research expenses compared to previous autonomous models. Recommended read:
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@the-decoder.com - 40d
DeepSeek has unveiled its v3 large language model (LLM), a significant advancement in AI. This new model was trained on an impressive 14.8 trillion tokens using 2,788,000 H800 GPU hours at a cost of approximately $5.576 million, a figure remarkably lower than other models of similar capability. DeepSeek v3's training involved both supervised fine-tuning and reinforcement learning, enabling it to achieve performance benchmarks comparable to Claude 3.5 Sonnet, showcasing its strong capabilities. The model is a Mixture-of-Experts (MoE) model with 671 billion parameters, with 37 billion activated for each token.
The release of DeepSeek v3 also includes API access, with highly competitive pricing compared to others in the market. Input is priced at $0.27 per million tokens (or $0.07 with cache hits), and output at $1.10 per million tokens. For comparison, Claude 3.5 Sonnet charges $3 per million tokens for input and $15 for output. These prices, along with its strong performance, indicate DeepSeek v3 is set to disrupt the market in terms of model quality and affordability. The model was also released as fully open-source with all associated papers and training frameworks provided to the research community. Recommended read:
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@the-decoder.com - 22d
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pub.towardsai.net
, THE DECODER
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AI research is rapidly advancing, with new tools and techniques emerging regularly. Johns Hopkins University and AMD have introduced 'Agent Laboratory', an open-source framework designed to accelerate scientific research by enabling AI agents to collaborate in a virtual lab setting. These agents can automate tasks from literature review to report generation, allowing researchers to focus more on creative ideation. The system uses specialized tools, including mle-solver and paper-solver, to streamline the research process. This approach aims to make research more efficient by pairing human researchers with AI-powered workflows.
Carnegie Mellon University and Meta have unveiled a new method called Content-Adaptive Tokenization (CAT) for image processing. This technique dynamically adjusts token count based on image complexity, offering flexible compression levels like 8x, 16x, or 32x. CAT aims to address the limitations of static compression ratios, which can lead to information loss in complex images or wasted computational resources in simpler ones. By analyzing content complexity, CAT enables large language models to adaptively represent images, leading to better performance in downstream tasks. Recommended read:
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