Long-Context Language Models (LCLMs) have emerged as a new frontier in artificial intelligence with the potential to handle complex tasks and applications without needing intricate pipelines that were traditionally used due to the limitations of context length. Unfortunately, their evaluation and development have been fraught with challenges. Most evaluations rely on synthetic tasks with fixed-length…
Advancements in vision-language models (VLMs) have enabled the possibility of developing a fully autonomous Artificial Intelligence (AI) assistant that can perform daily computer tasks through natural language. However, just having the reasoning and common-sense abilities doesn't always lead to intelligent assistant behavior. Thus, a method to translate pre-training abilities into practical AI agents is crucial.…
Large Language Models (LLMs) have revolutionized natural language processing, with considerable performance across various benchmarks and practical applications. However, these models also have their own sets of challenges, primarily due to the autoregressive training paradigm which they rely upon. The sequential nature of autoregressive token generation can drastically slow down processing speeds, limiting their practicality…
Machine learning has made significant strides, especially in the field of generative models such as diffusion models. These models are tailored to handle complex, high-dimensional data like images and audio which have versatile uses in various sectors such as art creation and medical imaging. Nevertheless, perfect alignment with human preferences remains a challenge, which can…
Neural networks, despite being theoretically capable of fitting as many data samples as they have parameters, often fall short in reality due to limitations in training procedures. This creates a gap between their potential and their practical performance, which can be an obstacle for applications that require precise data fitting, such as medical diagnoses, autonomous…
Google DeepMind is set to make significant strides in the field of artificial intelligence with its innovative Video-to-Audio (V2A) technology. This technology will revolutionize the synthesis of audiovisual content by addressing the common issue in current video generation models, which often produce silent films.
V2A's potential to transform artificial intelligence-driven media creation is tremendous, providing…
Biomedical Natural Language Processing (NLP) uses machine learning to interpret medical texts, aiding with diagnoses, treatment recommendations, and medical information extraction. However, ensuring the accuracy of these models is a challenge due to diverse and context-specific medical terminologies.
To address this issue, researchers from MIT, Harvard, and Mass General Brigham, among other institutions, developed RABBITS (Robust…
Artificial Intelligence (AI) models are becoming more sophisticated, and efficient communication with these models is crucial. Various prompt engineering strategies have been developed to facilitate this communication, utilizing concepts and structures similar to human problem-solving methods. These strategies can be categorized into different types: chaining methods, decomposition-based methods, path aggregation methods, reasoning-based methods, and external…
BigCode, a leading developer of large language models (LLMs), has launched BigCodeBench, a new benchmark for comprehensively assessing the programming capabilities of LLMs. This concurrent approach addresses the limitations of existing benchmarks like HumanEval, which has been criticized for its simplicity and scant real-world relevance. BigCodeBench comprises 1,140 function-level tasks which require the LLMs to…
The integration of artificial intelligence (AI) in clinical pathology represents an exciting frontier in healthcare, but key challenges include data constraints, model transparency, and interoperability. These issues prevent AI and machine learning (ML) algorithms from being widely adopted in clinical settings, despite their proven effectiveness in tasks such as cell segmentation, image classification, and prognosis…
Historically, thinking around decision-making has dichotomized habitual and goal-oriented behavior, treating them as independent activities controlled by distinct neural systems. Habitual behaviors, being automatic, are fast and model-free while goal-oriented behaviors, requiring deliberate action, are slower, model-based but demanding computationally. Microsoft researchers, however, have proposed an innovative Bayesian behavior framework that attempts to synergize these…
Roboflow’s Supervision is a reusable tool crafted to simplify numerous tasks relating to computer vision. The tool is quite adaptable and provides functionalities to load datasets from different sources, draw detections on images or videos, and count the number of detections within specified zones. One of the significant features of Supervision is its ability to…