Researchers from the OATML group at the University of Oxford have developed a statistical method to improve the reliability of large language models (LLMs) such as ChatGPT and Gemini. This method looks to mitigate the issues of "hallucinations," wherein the model generates false or unsupported information, and "confabulations," where the model provides arbitrary or incorrect…
Language Learning Models (LLMs) such as ChatGPT and Gemini have shown the capability of answering complex queries, but they often produce false or unsupported information, a situation aptly titled "hallucinations". This gets in the way of their reliability, with potential repercussions in critical fields like law and medicine. A specific subset of these hallucinations, known…
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…
Decision-making is crucial for organizations, often requiring data analysis and selection processes to determine the best alternative to meet specific objectives. For instance, pharmaceutical distribution networks often have to confront daunting decisions such as choosing the appropriate plants to run, deciding on the number of employees to employ, and optimizing production costs while ensuring prompt…
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…