Artificial Intelligence (AI) is transforming a multitude of industries at an exponential rate. In particular, AI agents designed to streamline and automate various aspects of business operations are emerging as some of the most innovative recent developments. These agents broadly fall into three categories: Planning Agents, Workflow Agents, and Matrix Agents. Each type of agent…
Improving Text Embeddings in Compact Language Models: A Comparative Refinement Method using MiniCPM.
Researchers from Tsinghua University have developed an approach to improve the performance of smaller language models such as MiniCPM, Phi-2, and Gemma by enhancing their text embeddings. By applying contrastive fine-tuning using the NLI dataset, the researchers significantly improved the text embedding quality across various benchmarks. In particular, MiniCPM showed a significant 56.33% performance improvement,…
Despite their crucial function in our digital lives, many search engines still struggle to deliver relevant and accurate results, leading to user frustration. Often, these issues stem from limitations in the underlying technologies used by these search engines. Several have tried to address these problems by integrating advanced algorithms and machine learning models into their…
OWLSAM2 is an innovative project that combines the strengths of OWLv2 and SAM2, two advanced models in the field of computer vision, to create a text-promptable model for zero-shot object detection and mask generation. OWLv2 stands out for its zero-shot object detection abilities that enable it to identify objects based on textual descriptions alone, without…
Introducing OWLSAM2: An unparalleled project that merges the sophisticated zero-shot object recognition attributes of OWLv2, renowned for its ability to identify objects in images without needing specific dataset training, and the highly advanced mask generation proficiencies of SAM2 (Segment Anything Model 2). This novel integration consequently leads to the creation of a text-prompted model that…
Researchers at MIT and the University of Washington have developed a method to model the behavior of agents, either human or artificial, accounting for potential unknown computational constraints that could affect their problem-solving abilities. This model can predict an agent’s future behavior based on a few instances of their past actions - what they term…
MIT and MIT-IBM Watson AI Lab researchers have invented a machine-learning chip resistant to the most common forms of cyberattacks. The technology caters to increasing demand for secure health-monitoring apps for individuals with chronic diseases or fitness goals, as well as for hardware-heavy uses such as autonomous vehicles and virtual reality. Health records and other…
Tornadoes are one of nature's most destructive and unpredictable forces, causing billions of dollars in damages and claiming lives every year. Though they are difficult to predict, a new open-source dataset created by researchers from the MIT Lincoln Laboratory, known as TorNet, offers hope in improving our detection and prediction abilities. Composed of radar images…
Large Language Models (LLMs) have drastically changed machine learning, pushing the field from traditional end-to-end training towards the use of pretrained models with carefully crafted prompts. This move has created a compelling question for researchers: Can a pretrained LLM function similar to a neural network, parameterized by its natural language prompt?
LLMs have been used for…
To construct AI systems that can effectively collaborate with humans, a comprehensive model of human behavior is pivotal, however, humans often exhibit suboptimal decision-making. Linking this irrationality to computational limitations, researchers from the Massachusetts Institute of Technology (MIT) and the University of Washington have presented an innovative technique to model the behavior of a human…