Researchers from the Improbable AI Lab at MIT and the MIT-IBM Watson AI Lab have developed a new technique to improve "red-teaming," a process of safeguarding large language models, such as AI chatbot, through the use of machine learning. The new approach focuses on the automatic generation of diverse prompts that result in undesirable responses…
Researchers from MIT, in collaboration with the Broad Institute of MIT and Harvard and Massachusetts General Hospital, have introduced a new artificial intelligence (AI) tool known as Tyche, which can provide multiple, plausible image segmentation possibilities for a given medical image. Unlike conventional AI tools, which typically offer a single definitive interpretation, Tyche generates a…
Neural networks have been of immense benefit in the design of robot controllers, boosting the adaptive and effectiveness abilities of these machines. However, their complex nature makes it challenging to confirm their safe execution of assigned tasks. Traditionally, the verification of safety and stability are done using Lyapunov functions. If a Lyapunov function that consistently…
Biomedicine often requires the annotation of pixels in a medical image to identify critical structures such as organs or cells, a process known as segmentation. In this context, artificial intelligence (AI) models can be useful to clinicians by highlighting pixels indicating potential disease or anomalies. However, decision-making in medical image segmentation is frequently complex, with…
Researchers from the Massachusetts Institute of Technology's (MIT) Computer Science and Artificial Intelligence Laboratory (CSAIL) have developed an algorithm to mitigate the risks associated with using neural networks in robots. The complexity of neural network applications, while offering greater capability, also makes them unpredictable. Current safety and stability verification techniques, called Lyapunov functions, do not…
Mistral AI has unveiled the new Mathstral model, an innovation designed specifically for mathematical reasoning and scientific discovery. The model, named Mathstral as an homage to Archimedes on the occasion of his 2311th anniversary, comprises a vast 7 billion parameters and a 32,000-token context window, and is made available under the Apache 2.0 license.
The Mathstral…
The immigration process in the United States is known for being notoriously difficult, requiring extensive paperwork, high costs, and complicated lottery systems. Often, immigration lawyers and legal writers must invest significant time and resources to research the most complex jobs, gather evidence, and draft a persuasive petition for various types of visas. This results in…
Telecommunication, the transmission of information over distances, is fundamental in our modern world, enabling the channeling of voice, data, and video via technologies including radio, television, satellite and the internet to support global connectivity and data exchange. But while innovations in the field continue to improve the speed, reliability, and efficiency of communication systems, existing…
Telecommunications is a field involving the transmission of information over distances to facilitate communication. It uses various technologies such as radio, television, satellite, and the internet for voice, data, and video transmission and plays a fundamental role in societal and economic functions.
However, Large Language Models (LLMs) that are typically used in the field lack specialised…
Creating comprehensive and detailed outlines for long-form articles such as those found on Wikipedia is a considerable challenge due to issues in capturing the full depth of the topic, thus leading to shallow or poorly structured articles. This pivotal problem originates from systems' inability to ask the correct queries and source information from a variety…
Researchers are grappling with how to identify cause and effect in diverse time-series data, where a single model can't account for various causal mechanisms. Most traditional methods used for casual discovery from this type of data typically presume a uniform causal structure across the entire dataset. However, real-world data is often highly complex and multi-modal,…