Large language models (LLMs) are being extensively used in multiple applications. However, they have a significant limitation: they struggle to process long-context tasks due to the constraints of transformer-based architectures. Researchers have explored various approaches to boost LLMs' capabilities in processing extended contexts, including improving softmax attention, reducing computational costs and refining positional encodings. Techniques…
To counter unsafe responses from chatbots, companies often use a process called red-teaming, in which human testers write prompts designed to elicit such responses so the artificial intelligence (AI) can be trained to avoid them. However, since it is impossible for human testers to cover every potential toxic prompt, MIT researchers developed a technique utilizing…
Medical imaging is a critical tool in diagnosing and monitoring disease. However, interpreting these images is not always straightforward, leading to potential disagreement amongst clinicians. To address this issue, researchers at MIT, in collaboration with the Broad Institute of MIT and Harvard, and Massachusetts General Hospital (MGH), have developed an artificial intelligence (AI) tool, named…
An AI model utilizes 500 million years of evolution simulation to develop a new fluorescent protein.
Researchers have developed an AI system called ESM3 that is capable of simulating hundreds of millions of years of protein evolution to create a new fluorescent protein unlike any found in nature. The system, designed by a team led by Alexander Rives at EvolutionaryScale, can process and generate data about protein sequences, structures, and functions.…
Microsoft researchers have recently introduced a new technique for evaluating conversational AI assistants: RUBICON. This technique was specifically designed to assess domain-specific Human-AI conversations by generating and assessing candidate rubrics. Tested on 100 conversations between developers and a chat-based assistant specifically designed for C# debugging, RUBICON outperformed all other alternative rubric sets, demonstrating its high…
Large language models powering AI chatbots possess the potential for generating harmful content due to their exposure to countless websites, putting users at risk if the AI generates illegal activities description, illicit instructions, or personal information leakage. To mitigate such threats, AI-developing companies use a procedure known as red-teaming, where human testers compose prompts aimed…
In biomedical science, the process of annotating pixels from crucial elements within a medical image, such as a cell or organ, is called segmentation. This task can be aided by artificial intelligence (AI), which highlights pixels that might indicate the existence of a certain disease or anomaly. However, segmentation is seldom clear-cut, as a group…
The world of machine learning has been based on Euclidean geometry, where data resides in flat spaces characterized by straight lines. However, traditional machine learning methods fall short with non-Euclidean data, commonly found in the fields such as neuroscience, computer vision, and advanced physics. This paper brings to light these shortcomings, and emphasizes the need…
Machine Learning (ML) significantly contributes to the augmentation of Augmented Reality (AR) across a variety of educational fields, promoting superior object visualizations and interactive capabilities. This analysis reviews the intersection of ML and AR, detailing the widespread applications from kindergarten education to university learning. It investigates ML frameworks including support vector machines, Deep Learning Convolutional…
Artificial intelligence (AI) chatbots like ChatGPT, capable of generating computer code, summarizing articles, and potentially even providing instructions for dangerous or illegal activities, pose unique safety challenges. To mitigate this risk, companies use a safeguarding process known as red-teaming, where human testers attempt to prompt inappropriate or unsafe responses from AI models. This process is…
Artificial Intelligence (AI) models are increasingly being employed in the field of biomedicine to assist clinicians with image segmentation, a process that annotates pixels from important structures in a medical image, such as an organ or cell. However, these AI models often offer a singular answer, while image segmentation in the medical field is usually…
Assessing the effectiveness of Large Language Model (LLM) compression techniques is a vital challenge in AI. Traditional compression methods like quantization look to optimize LLM efficiency by reducing computational overhead and latency. But, the conventional accuracy metrics used in evaluations often overlook subtle changes in model behavior, including the occurrence of "flips" where right answers…