ML News

Large Language Models (LLMs) have had a significant impact on the realm of Artificial Intelligence (AI). Yet, these models are not perfect and often struggle with mathematical reasoning, which is a crucial element of AI's cognitive abilities. Researchers are working hard to enhance these models' reasoning capabilities by focusing on Chain-of-Thought (CoT) prompts and optimizing…

Artificial intelligence (AI)'s potential to have unprecedented capabilities has raised concerns about the possible threats it could pose to cybersecurity, privacy, and human autonomy. Understanding these risks is essential for mitigating them. This is usually achieved by evaluating AI systems' performance in various domains but often requires a deeper understanding of their possible dangers. To…

The increasing urgency and complexity of materials discovery and characterization have made understanding and modeling crystal structures an intense field of research. Periodic patterns and the infinite nature of these structures present a challenge in predicting material properties, highlighting the need for new computational and experimental methods. Recent advancements such as Matformer and PotNet models…

The understanding and modelling of crystal structures is a critical area of material science research due to their inherent complexity. Recent advances have included models designed to process and analyze these structures, improving prediction accuracy for material properties. However, challenges remain, particularly in dealing with the periodic patterns of crystalline materials and maintaining predictive accuracy.…

Amazon AI engineers have developed a revolutionary machine learning framework known as DATALORE, designed to enhance the process of data management, traceability and reproducibility. The DATALORE system aims to reduce complications surrounding data tracing, necessary for creating effectively documented machine learning (ML) pipelines. To do this, DATALORE employs Large Language Models (LLMs), which simplify the…

Google's team of researchers has introduced a new methodology called Parameter-Efficient Reinforcement Learning (PERL) that enhances the efficiency and applicability of Reinforcement Learning from Human Feedback (RLHF) models with Large Language Models (LLMs). The current RLHF process is computationally intense and requires vast resources, thus restricting its broad usage. PERL provides a solution to this…

The refinement of large language models (LLMs) is an essential challenge in the field of artificial intelligence. The major difficulty lies in ensuring that these digital repositories of knowledge stay current and accurate. Traditional ways of updating LLMs, such as retraining or fine-tuning, demand considerable resources and carry the associated risk of catastrophic forgetting, whereby…

This article details a recent Google study whose goal is to train Large Language Models (LLMs) to better process information represented in graph form. LLMs are typically trained on text, but graphs provide a more efficient way of organising information due to their visual representation of relationships between entities (nodes) as connected by links (edges).…

Time series analysis is crucial in various sectors, including finance, healthcare, and environmental monitoring. However, the diversity of time series data presents a significant challenge due to its varying lengths, dimensions, and tasks such as forecasting and classification. Traditionally, individual task-specific models for each different type of analysis were used. However, this approach is resource-intensive…

In the rapidly evolving field of artificial intelligence, managing the efficient operation of large language models (LLMs) on consumer-grade hardware is a substantial technical challenge. This arises from the intrinsic struggle between a model's size and computational efficiency. Some compression methods like direct and multi-codebook quantization (MCQ) have offered partial solutions for reducing memory requirements…

Machine learning (ML) workflows are crucial for enabling data-driven innovations. Yet as they continue to grow in complexity and scale, they become increasingly resource-intensive and time-consuming, raising operational costs. These workflows also require management across a range of unique workflow engines, each with its own Application Programming Interface (API), complicating optimization efforts across different platforms.…