# Jul 16, 2024

## Daily

### Run CUDA, unmodified, on AMD GPUs

- **SCALE** is a **GPGPU programming toolkit** designed to compile CUDA applications for AMD GPUs without requiring modifications to the original CUDA program or its build system, aiming to broaden CUDA's hardware compatibility.

### Codestral Mamba

- **Codestral Mamba**, a new AI model by Mistral AI, offers **linear time inference** and can handle **sequences of infinite length**, diverging from traditional Transformer models to enhance code productivity. [Read more about Mamba models](https://arxiv.org/abs/2312.00752)

### Guide to Machine Learning with Geometric, Topological, and Algebraic Structures

- **Modern machine learning** is evolving to **embrace non-Euclidean data**, characterized by complex geometric, topological, and algebraic structures, diverging from the traditional Euclidean geometry foundation.

### XLSTMTime: Long-Term Time Series Forecasting with xLSTM

- **xLSTMTime**, an adaptation of the extended LSTM (xLSTM) architecture for long-term time series forecasting (LTSF), **outperforms current models**, including those based on transformers and LTSF-Linear, by incorporating **exponential gating and a revised memory structure**.

### Transformer Layers as Painters

- **Empirical studies** on frozen pretrained transformers reveal **distinct behaviors** between lower, middle, and final layers, with **middle layers exhibiting uniformity**.

### \\[R\\] Protein language models expose viral mimicry and immune escape

- **Protein Language Models (PLMs) achieve a 99.7% accuracy** in distinguishing viral proteins from human ones, highlighting their potential in identifying viral mimicry strategies and immune escape mechanisms.

### \\[R\\] Discussion on the paper: Transcendence: Generative Models Can Outperform The Experts That Train Them

- The paper, **"Transcendence: Generative Models Can Outperform The Experts That Train Them,"** has sparked significant interest for its claim that **generative models can surpass the expertise** of their training datasets' contributors. [Read the paper](https://arxiv.org/abs/2406.11741)

### \\[R\\] Any-Property-Conditional Molecule Generation with Self-Criticism using Spanning Trees

- **Spanning Tree-based Graph Generation (STGG)** has been enhanced to **STGG+**, incorporating a **Transformer architecture** and **random masking** to generate molecules conditional on desired properties, significantly improving upon traditional SMILES and graph diffusion models.

### 🤗SmolLM - blazingly fast and remarkably powerful

- **SmolLM introduces a new benchmark in small language models** with sizes of 135M, 360M, and 1.7B parameters, trained on a meticulously curated high-quality dataset, SmolLM-Corpus, which includes Cosmopedia v2, Python-Edu, and FineWeb-Edu, totaling over 252B tokens.

### TwoMinutePapers - New AI: This Is A Gaming Revolution!

- **Embodied avatars** in gaming now allow players to **control their virtual characters with their own body movements**, rather than traditional controllers, promising a more immersive experience.

### \\[D\\] Best open source LLM for graph based questions answering

- **Open source Large Language Models (LLMs)** are sought for **two distinct tasks**: creating knowledge graphs from unstructured text and answering questions based on these graphs.

### \\[N\\] Found a helpful tool for scaling LLM on a large amount of unstructured data

- **Roe AI** simplifies the process of **scaling LLMs** for extracting features from **unstructured data** such as PDFs and images/videos, bypassing the need for complex coding or inadequate platform support.

### Foundational Autoraters: Taming Large Language Models for Better Automatic Evaluation

- **FLAMe**, a **Foundational Large Autorater Model**, leverages a **diverse collection of 5M+ human judgments** across **100+ quality assessment tasks**, outshining LLMs like **GPT-4** and **Claude-3** in generalization to various tasks. [Read more](http://arxiv.org/abs/2407.10817v1)

### RUBICON: Evaluating conversations between humans and AI systems

- **RUBICON**, an automated assessment technique introduced by Microsoft Research, **transforms minimal datasets into extensive domain-specific rubrics**, enhancing the evaluation of human-AI interactions in specialized settings like software development.

### Data-driven model improves accuracy in predicting EV battery degradation

- Microsoft Research and Nissan Motor Corporation have developed a **machine learning method** that predicts EV battery degradation with a **remarkable average error rate of 0.94%**, enhancing the accuracy of battery recycling efforts significantly.