LASR: A Novel Machine Learning Approach to Symbolic Regression Using Large Language Models

Graph and geometric deep learning models have been successful in machine learning for drug discovery, specifically in modeling atomistic interactions, 3D/4D situations, activity and property prediction, and molecular production. However, the lack of large labeled datasets…

Introduction to Arabic Stable LM 1.6B Large language models (LLMs) have greatly impacted natural language processing (NLP), especially in text generation and understanding. However, the Arabic language is often overlooked due to its complexity and cultural…

Researchers have developed DeepMB, a deep-learning framework that enables real-time, high-quality optoacoustic imaging in medical applications. By training the system on synthesized optoacoustic signals, DeepMB achieves accurate image reconstruction in just 31 milliseconds per image, making…

A Multi-Strategy AI with Deep Reinforcement Learning has achieved victory over GPT3.5 in a Chess Match. For more details, please visit Towards Data Science.

Practical Solutions for Video Analysis Challenges in Video Analysis Language Foundation Models (LFMs) and Large Language Models (LLMs) have inspired the development of Image Foundation Models (IFMs) in computer vision. However, applying these techniques to video…

EPFL and Apple researchers developed PaSS, a method enhancing language model efficiency by generating multiple tokens in parallel using one model. The approach speeds up generation by up to 30%, maintains model quality, and optimizes token…

OpenAI’s GPT-4 Turbo has received mixed reactions since its launch. While OpenAI claims it is an improvement over its predecessor, user experiences suggest otherwise. An independent benchmark test showed a drop in performance from GPT-4 to…

Practical Solutions and Value Extending Language Models’ Context Windows Large language models (LLMs) face limitations in processing extensive contexts due to their Transformer-based architectures. These constraints hinder their ability to incorporate domain-specific, private, or up-to-date information…

The rise in demand for data-centric local intelligence has highlighted the need for autonomous data analysis at the edge. Edge-AI devices, such as wearables and smartphones, represent the next phase of growth in the semiconductor industry.…

Researchers from Meta have introduced Retrieval-Augmented Dual Instruction Tuning (RA-DIT), a lightweight fine-tuning methodology to equip large language models (LLMs) with efficient retrieval capabilities. RA-DIT operates through two stages, optimizing the LLM’s use of retrieved information…

At the “Generative AI: Shaping the Future” symposium, keynote speaker Rodney Brooks highlighted the risk of overhyping AI’s capabilities, emphasizing the need for responsible development. The event at MIT included discussions on generative AI’s potential for…

The demand for bilingual digital assistants in the modern digital age is growing. Current large language models face challenges in understanding and interacting effectively in multiple languages. A new open-source model named ‘Yi’ is tailored for…

Researchers have proposed building an image dataset under a Creative Commons license to overcome obstacles in text-to-image generation. They have used transfer learning to generate captions for CC photos and created a dataset called CommonCatalog to…

Deep fakes are a growing concern, particularly in the context of elections. Recent incidents in Slovakia, the UK, and Sudan have highlighted the threat of AI-generated fake audio clips. These clips are harder to detect and…

Enhancing Text Embeddings in Small Language Models: A Contrastive Fine-Tuning Approach with MiniCPM Practical Solutions and Value Highlights: Smaller language models like MiniCPM offer better scalability but often need targeted optimization to perform. Contrastive fine-tuning significantly…

Retrieval Algorithms in Ad and Content Recommendation Systems Practical Solutions and Value Researchers from the University of Toronto explore advanced algorithms used in ad and content recommendation systems, highlighting their practical applications in driving user engagement…