Skip to content Skip to sidebar Skip to footer

Staff

Transforming Healthcare: OpenEvidence Debuts Medical AI API for Improved Clinical Solutions

Artificial Intelligence (AI) is reshaping numerous aspects of life in the modern world, and the medical field is no exception. A remarkable breakthrough in this area has been achieved by OpenEvidence, a medical AI created under the Mayo Clinic Platform Accelerate. The AI has set a benchmark by scoring an impressive 90% on the United…

Read More

MathVerse: A Comprehensive Visual Math Benchmark Crafted for Fair, Thorough Assessment of Multi-modal Extensive Language Models (MLLMs)

The ability of large Multimodal Language Models (MLLMs) to tackle visual math problems is currently the subject of intense interest. While MLLMs have performed remarkably well in visual scenarios, the extent to which they can fully understand and solve visual math problems remains unclear. To address these challenges, frameworks such as GeoQA and MathVista have…

Read More

Scientists from GSK AI and Imperial College have launched RAmBLA, a machine learning tool created to assess the dependability of LLMs as auxiliary aids in the biomedical field.

The increased adoption and integration of large Language Models (LLMs) in the biomedical sector for interpretation, summary and decision-making support has led to the development of an innovative reliability assessment framework known as Reliability AssessMent for Biomedical LLM Assistants (RAmBLA). This research, led by Imperial College London and GSK.ai, puts a spotlight on the critical…

Read More

This AI article presents SafeEdit: An innovative standard for exploring the purification of LLMs through knowledge modification.

As the advancements in Large Language Models (LLMs) such as ChatGPT, LLaMA, and Mistral continue, there are growing concerns about their vulnerability to harmful queries. This has caused an immediate need to implement robust safeguards. Techniques such as supervised fine-tuning (SFT), reinforcement learning from human feedback (RLHF), and direct preference optimization (DPO) have been useful…

Read More

Improving User Control in Generative Language Models: Algorithmic Solution for Filtering Toxicity

Generative Language Models (GLMs) are now ubiquitous in various sectors, including customer service and content creation. Consequently, handling potential harmful content while keeping linguistic diversity and inclusivity has become important. Toxicity scoring systems aim to filter offensive or hurtful language, but often misidentify harmless language as harmful, especially from marginalized communities. This restricts access to…

Read More

Reforming High-Dimensional Optimization: The Dimension-Free Convergence of the Krylov Subspace Cubic Regularized Newton Method.

Optimizing efficiency in complex systems is a significant challenge for researchers, particularly in high-dimensional spaces commonly found in machine learning. Second-order methods like the cubic regularized Newton (CRN) method demonstrate rapid convergence; however, their application in high-dimensional problems has been limited due to substantial memory and computational requirements. To counter these challenges, scientists from UT…

Read More

Introducing Claude-Investor: The Maiden Claude 3 Investment Analysis Agent Repository.

In today's ever-evolving financial universe, investors often feel inundated by the sheer volume of data and information that needs to be analyzed while examining investment prospects. Without the right tools and guidance, investors often struggle to make sound financial decisions. Traditional approaches or financial advisor services, although resourceful, can often turn out to be time-consuming…

Read More

Researchers from EPFL have developed DenseFormer: A Tool for Boosting Transformer Efficiency using Depth-Weighted Averages to Improve Language Modeling Performance and Speed.

In recent years, natural language processing (NLP) has seen significant advancements due to the transformer architecture. However, as these models grow in size, so do their computational costs and memory requirements, limiting their practical use to a select few corporations. Increasing model depths also present challenges, as deeper models need larger datasets for training, which…

Read More

EPFL Researchers’ DenseFormer: Improving Transformer Efficiency through Depth-Weighted Averages for Optimal Language Modeling Speed and Performance.

Transformer architecture has greatly enhanced natural language processing (NLP); however, issues such as increased computational cost and memory usage have limited their utility, especially for larger models. Researchers from the University of Geneva and École polytechnique fédérale de Lausanne (EPFL) have addressed this challenge by developing DenseFormer, a modification to the standard transformer architecture, which…

Read More

Microsoft’s AI presents a new Machine Learning method named CoT-Influx, that enhances the limitation of Few-Shot Chain-of-Thoughts (CoT) Learning for better mathematical reasoning in Language Learning Models (LLM).

Large Language Models (LLMs) have proven to be game-changers in the field of Artificial Intelligence (AI), thanks to their vast exposure to information and versatile application scope. However, despite their many capabilities, LLMs still face hurdles, especially in mathematical reasoning, a critical aspect of AI’s cognitive skills. To address this problem, extensive research is being…

Read More

Microsoft AI introduces CoT-Influx, an innovative machine learning method that extends the limits of Few-Shot Chain-of-Thoughts (CoT) Learning to enhance mathematical reasoning in Language Learning Models (LLM).

Large Language Models (LLMs) have transformed the landscape of Artificial Intelligence. However, their true potential, especially in mathematic reasoning, remains untapped and underexplored. A group of researchers from the University of Hong Kong and Microsoft have proposed an innovative approach named 'CoT-Influx' to bridge this gap. This approach is aimed at enhancing the mathematical reasoning…

Read More

Eric Evans is set to resign from his position as the director of MIT Lincoln Laboratory.

Eric Evans, the director of MIT Lincoln Laboratory, will be stepping down from his position on July 1, 2024, after an 18-year tenure. Following this, he will be assuming the role of fellow in the director's office at Lincoln Laboratory, as well as hold an appointment as a senior fellow in the Security Studies Program…

Read More