LLMs like ChatGPT and Gemini demonstrate impressive reasoning and answering capabilities but often produce “hallucinations,” meaning they generate false or unsupported information. This problem hampers their reliability in critical fields, from law to medicine, where inaccuracies can have severe consequences. Efforts to reduce these errors through supervision or reinforcement have seen limited success. A subset… →

Large Language Models (LLMs) have revolutionized natural language processing, demonstrating exceptional performance on various benchmarks and finding real-world applications. However, the autoregressive training paradigm underlying these models presents significant challenges. Notably, the sequential nature of autoregressive token generation results in slow processing speeds, limiting the models’ efficiency in high-throughput scenarios. Also, this approach can lead… →

Roboflow’s Supervision tool is a robust and versatile resource that caters to various computer vision needs. From loading datasets to drawing detections and counting items within a zone, Supervision provides essential functionalities to streamline and enhance these processes. Let’s delve into Supervision’s comprehensive features, installation methods, and practical applications, emphasizing its utility in modern computer… →

In decision-making, habitual behavior has always been seen as separate from goal-directed behavior. Habitual behaviors are automatic responses, deeply ingrained through experience. Like riding a bike or reaching for your coffee cup in the morning, they required little to no conscious thought. In contrast, goal-directed behavior requires deliberate planning and action to achieve a specific… →

Decision-making is critical for organizations, involving data analysis and selecting the most suitable alternative to achieve specific goals. In business scenarios like pharmaceutical distribution networks, companies face complex decisions such as determining which plants to operate, how many employees to hire, and optimizing production costs while ensuring timely delivery. The decision-making task traditionally requires three… →

The integration of  AI in clinical pathology faces challenges due to data constraints and concerns over model transparency and interoperability. AI and ML algorithms have shown significant advancements in tasks such as cell segmentation, image classification, and prognosis prediction in digital pathology. Despite outperforming pathologists in specific functions like predicting colorectal carcinoma microsatellite instability, regulatory… →

BigCode, a leading entity in developing large language models (LLMs), has announced the release of BigCodeBench, a novel benchmark designed to rigorously evaluate LLMs’ programming capabilities on practical and challenging tasks.  Addressing Limitations in Current Benchmarks Existing benchmarks like HumanEval have been pivotal in evaluating LLMs on code generation tasks, but they face criticism for… →

In the developing field of Artificial Intelligence (AI), the ability to think quickly has become increasingly significant. The necessity of communicating with AI models efficiently becomes critical as these models get more complex. In this article we will explain a number of sophisticated prompt engineering strategies, simplifying these difficult ideas through straightforward human metaphors. The… →

Biomedical natural language processing (NLP) focuses on developing machine learning models to interpret and analyze medical texts. These models assist with diagnostics, treatment recommendations, and extracting medical information, significantly improving healthcare delivery and clinical decision-making. By processing vast amounts of biomedical literature and patient records, these models help identify patterns and insights that can lead… →