# Apr 27, 2024

- **Tiny rubber spheres used to make a programmable fluid**  
  Researchers at **Harvard University** have developed a **metafluid** that can change its properties under pressure, using tiny rubber spheres that buckle, offering a new approach to **soft robotics**. [Adel Djellouli/Harvard SEAS](https://seas.harvard.edu/news/2024/04/intelligent-liquid)

- **Searchformer: Beyond A* – Better planning with transformers via search dynamics**  
  **Facebook Research's Searchformer** introduces a novel approach to planning with transformers, enhancing algorithmic efficiency by leveraging search dynamics bootstrapping, as detailed in their paper [_Beyond A*: Better Planning with Transformers via Search Dynamics Bootstrapping_](https://arxiv.org/abs/2402.14083).

- **LLMs: Why does in-context learning work? What exactly is happening from a technical perspective?**  
  **In-context learning** in Large Language Models (LLMs) operates not through cognitive processes but by **statistically weighting tokens** similar to provided examples, enhancing output relevance and accuracy.

- **What can LLMs never do?**  
  **LLMs struggle with tasks requiring iterative reasoning or memory of previous states**, such as playing Wordle or predicting cellular automata, despite excelling in other complex areas like chess or coding.

- **Removing Reflections from RAW Photos**  
  **A new system** removes **real-world reflections from RAW photos**, utilizing an optional contextual photo from the opposite direction to enhance clarity.

- **The Landscape of Machine Learning on ArXiv**  
  The **ArXiv Machine Learning Landscape** presents a **visual data map** showcasing the distribution and relationships of machine learning papers.

- **Show HN: Cognita – open-source RAG framework for modular applications**  
  **Cognita is an open-source framework designed to transition RAG (Retrieval-Augmented Generation) systems from prototyping in Jupyter notebooks to scalable, modular production environments**, utilizing Langchain/LlamaIndex for experimentation and offering a structured codebase with API-driven, extendable components.

- **Llama-3 based OpenBioLLM-70B & 8B: Outperforms GPT-4, Gemini, Meditron-70B, Med-PaLM-1 & Med-PaLM-2 in Medical-domain**  
  **OpenBioLLM-Llama3-70B & 8B models** set a new benchmark in the **biomedical domain**, outperforming major models like **GPT-4, Gemini, and Med-PaLM series**, marking them as the most advanced open-source medical-domain LLMs currently available.

- **Direct-Style Effects Explained**  
  **Direct-style effects, also known as algebraic effects and effect handlers, aim to simplify coding** by allowing natural style code without monads while retaining benefits of reasoning and composition, as seen in languages like [Unison](https://www.unison-lang.org/docs/fundamentals/abilities/) and [OCaml](https://github.com/ocaml-multicore/ocaml-effects-tutorial), and discussions for Scala implementation.

- **Relational Graph Convolutional Networks for Sentiment Analysis**  
  **Relational Graph Convolutional Networks (RGCNs)** enhance sentiment analysis by **capturing complex relationships** between entities, offering a significant improvement over traditional and deep learning models.

- **Einsum for Tensor Manipulation**  
  **Einsum offers a versatile approach for tensor manipulation in ML**, enabling operations like matrix multiplication and dot products with explicit documentation of tensor dimensions and implicit reordering.

- **WebSim, WorldSim and the Summer of Simulative AI**  
  **Simulative AI** is emerging as a pivotal frontier in generative AI, with **Joscha Bach of Liquid AI, Karan Malhotra of Nous Research, and Rob Haisfield of WebSim.ai** offering insights into its potential to explore adjacent multiverses and create immersive, interactive simulations.

- **Let's Think Dot by Dot: Hidden Computation in Transformer Language Models**  
  **Transformers can solve complex algorithmic tasks** by using **meaningless filler tokens** instead of detailed chain-of-thought responses, challenging the necessity of human-like task decomposition.

- **Large language models as research assistants**  
  **Large language models like GPT-4** are increasingly being used as research assistants, capable of outperforming humans in complex tasks such as the bar exam and matching the abilities of math Olympians, indicating a shift towards AI-driven academic research.

- **How do I convince my superior to do data preprocessing?**  
  **Data preprocessing** is advocated by an AI Engineer as crucial for enhancing chatbot performance, contrasting with their superior's preference for prompt engineering without preprocessing.