Before the development of PILOT (PIecewise Linear Organic Tree), linear model trees were slow to fit and susceptible to overfitting, notably with large datasets. The traditional regression trees faced challenges capturing linear relationships efficiently. Linear model trees also encountered problems with interpretability when integrating linear models in leaf nodes. The research points out the need…
Multi-target multi-camera tracking (MTMCT) has become indispensable in intelligent transportation systems, yet real-world applications are complex due to a shortage of publicly available data and laborious manual annotation. MTMCT involves tracking vehicles across multiple camera lenses, detecting objects, carrying out multi-object tracking, and finally clustering trajectories to generate a comprehensive image of vehicle movement. MTMCT…
In the domain of visual question answering (VQA), the Multi-Image Visual Question Answering (MIQA) remains a major hurdle. It entails generating pertinent and grounded responses to natural language prompts founded on a vast assortment of images. While large multimodal models (LMMs) have proven competent in single-image VQA, they falter when dealing with queries involving an…
Language Learning Models (LLMs) are sophisticated pieces of software used to build Artificial Intelligence models. While they are incredibly valuable, their intrinsic randomness means their development requires continuous monitoring, systematic testing, and fast iteration of fundamental logic. Unfortunately, current solutions are vertical, causing a divide between stages of the development process, and slowing down developers.
Enter…
In an effort to improve AI systems and their ability to collaborate with humans, scientists are trying to better understand human decision-making, including its suboptimal aspects, and model it in AI. A model for human or AI agent behaviour, developed by researchers at MIT and the University of Washington, takes into account an agent’s unknown…
Researchers at the Massachusetts Institute of Technology (MIT) and the MIT-IBM Watson AI Lab have developed a machine learning accelerator chip that is resistant to the most common types of cyberattacks, ensuring data privacy while supporting efficient AI model operations on devices. The chip can be used in demanding AI applications like augmented and virtual…
Machine learning (ML) models are increasingly used by organizations to allocate scarce resources or opportunities, such as for job screening or determining priority for kidney transplant patients. To avoid bias in a model's predictions, users may adjust the data features or calibrate the model's scores to ensure fairness. However, researchers at MIT and Northeastern University…
The political sector is undergoing a major transformation and appears ready to adopt more future-focused strategies. A significant factor driving this change is the growing influence of Artificial Intelligence (AI). This technology is redefining how political campaigns are planned and implemented, as well as enhancing politicians' connection with voters. AI tools such as Robotic Marketer…
