This article combines theory with hands-on implementation in Flutter to make learning data structures engaging and practical.
The Institute of Foundation Models at MBZUAI focuses on advancing research in Generative AI, developing foundation models for various data types, and driving innovation in healthcare, climate change, and sustainability.
Biomedical researchers face significant challenges due to the complexity of topics and the need for trans-disciplinary approaches. The AI Co-Scientist system, powered by Gemini 2.0, aims to accelerate scientific discovery by generating, debating, and evolving hypotheses. It integrates specialized agents to interact with scientists, manage tasks, and allocate resources effectively.
The AI Co-Scientist integrates four key components:
- Natural Language Interface: Allows scientists to interact with the system.
- Asynchronous Task Framework: Implements a multi-agent system for continuous execution.
- Supervisor Agent: Manages the task queue and assigns specialized agents.
- Persistent Context Memory: Stores and retrieves agent and system states.
The system includes various specialized agents:
- Generation Agent: Initiates research and creates hypotheses.
- Reflection Agent: Reviews hypothesis quality and correctness.
- Ranking Agent: Prioritizes hypotheses using an Elo-based tournament system.
- Proximity Agent: Computes similarity graphs for hypothesis clustering.
- Evolution Agent: Refines top-ranked hypotheses.
- Meta-review Agent: Synthesizes insights from reviews and debates.
Learn how to get started with Podman, a daemon-less and secure containerization tool that's a great alternative to Docker.
Bruce Sterling shares his collection of Ibizan books, discussing the island's history, culture, and his personal experiences living and tinkering there.
The article by Jurriaan Rexwinkel explores the intersection of maker education and various research areas, highlighting how hands-on, project-based learning fosters skills like creativity, collaboration, and problem-solving. It discusses the role of technology, self-regulation, collaboration, academic performance, and intrinsic motivation in Maker Education, referencing key theories and publications such as Self-Determination Theory and 'Invent to Learn'. The article emphasizes the shift in educational roles towards facilitation and the importance of mindset in educational innovation.
This tutorial guides readers on how to fine-tune the Mistral 7B large language model using QLoRA with the Axolotl library, focusing on managing limited GPU resources for efficient training. It covers environment setup, dataset creation, configuration of QLoRA hyperparameters, the fine-tuning process, and testing the fine-tuned model.
Boxy is a Boxer-inspired box editor that provides various functionalities for managing and manipulating boxes. It supports key bindings, modules, and allows running in a browser. The editor includes features such as mouse and keyboard interactions, saving and restoring boxes, markdown visualization, and LLM inference.
- "Deep Research" is a new trend in AI-driven research using large language models for multi-step investigations.
- The article compares Deep Research systems, highlighting capabilities and limitations like generating tangential content and handling nonsensical queries.
- Includes systems such as Gemini Advanced 1.5 Pro, OpenAI’s Deep Research, Perplexity’s Deep Research Mode, and You.com’s Research Feature.
Dan Weinreb's thesis details the development of ZWEI, a real-time display-oriented editor for the Lisp Machine. It emphasizes ZWEI's design, implementation using Lisp, and integration with the Lisp environment. Key aspects include the use of buffer pointers (bps), intervals, and Lisp macros, as well as the impact of the Lisp Machine's architecture on the editor's functionality.
