|
Robert Krzaczyński@InfoQ - AI, ML & Data Engineering - 52d
Google has launched Gemini 2.0, a new top-ranked large language model with enhanced agent capabilities. It features improved performance on complex tasks such as coding, math, and reasoning. The model includes Gemini 2.0 Flash, designed for real-time multimodal interactions, and the Deep Research tool, an agentic system for in-depth online research. Also included are Project Astra, a visual assistant, Project Mariner, a Chrome extension, and Jules, a code debugging AI.
Recommended read:
References :
@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:
References :
@pub.towardsai.net - 36d
Recent developments in AI agent frameworks are paving the way for more efficient and scalable applications. The Jido framework, built in Elixir, is designed to run thousands of agents using minimal resources. Each agent requires only 25KB of memory at rest, enabling large-scale deployment without heavy infrastructure. This capability could significantly reduce the cost and complexity of running multiple parallel agents, a common challenge in current agent frameworks. Jido also allows agents to dynamically manage their own workflows and sub-agents utilizing Elixir's concurrency features and OTP architecture.
The core of Jido centers around four key concepts: Actions, Workflows, Agents, and Sensors. Actions represent small, reusable tasks, while workflows chain these actions together to achieve broader goals. Agents are stateful entities that can plan and execute these workflows. The focus is on creating a system where the agents can, to a degree, manage themselves without constant human intervention. Jido provides a practical approach to building autonomous, distributed systems through functional programming principles, and dynamic error handling. Recommended read:
References :
@the-decoder.com - 22d
References:
pub.towardsai.net
, THE DECODER
,
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:
References :
|
|