Machine learning plays a pivotal role in the development of autonomous vehicles and robots. In the realm of mechanical engineering, these technologies rely on machine learning algorithms for navigation, object recognition, and decision-making. This convergence is revolutionizing transportation and manufacturing industries.

In recent years, scientists have found that machine learning–based weather models can make weather predictions more quickly using less energy than traditional models. However, many of those models are unable to accurately predict the weather more than 15 days into the future and begin to simulate unrealistic weather by day 60.

Chinmay Govind and Nihar Ballamudi leveraged AI for their project that may help improve protections for whale populations worldwide

Google DeepMind, a London-based AI research lab, has been in the business of machine learning-based weather forecasting for several years, but back in June announced a new experimental AI model specific to tropical storms and hurricanes that would be evaluated in real-time this season by forecasters at the National Hurricane Center.

Environmental scientists are increasingly using enormous artificial intelligence models to make predictions about changes in weather and climate, but a new study by MIT researchers shows that bigger models are not always better.

His pioneering machine-learning model is capable of modifying textbook diagrams and maps for colour-blind students and has achieved a 99.7% accuracy.

Apurva Pathak is a prominent machine learning expert and software engineer currently based in Newark, California. With a strong academic foundation, holding a Master of Science in Computer Science from UC San Diego and a Bachelor of Technology from NIT Rourkela,

CMS employed machine learning to probe rare Higgs decays into charm quarks. The search produced the most stringent limits so far. The Higgs boson, first observed at the Large Hadron Collider (LHC) in 2012,