Peter Ma was bored in his high school computer science class. So he decided to teach himself something new: how to use artificial intelligence to find alien life. That's how he eventually became the lead author of a groundbreaking study published in Nature Astronomy. The study reveals how Ma and his co-authors used AI to analyze a massive dataset of radio signals collected by the SETI Breakthrough Listen project. They found eight signals that might just be technosignatures or signs of alien technology.

Surfers, swimmers and beachgoers face a hidden danger in the ocean: rip currents. These narrow channels of water can flow away from the shore at speeds up to 2.5 meters per second, making them one of the biggest safety risks for those enjoying the ocean. To help keep beachgoers safe, Christo Rautenbach, a coastal and estuarine physical processes scientist, has teamed up with the National Institute of Water and Atmospheric Research in New Zealand to develop a real-time rip current identification tool using deep learning. On this episode of the NVIDIA AI Podcast, host Noah Kravitz interviews Rautenbach about how AI can be used to identify rip currents and the potential for the tool to be used globally to help reduce the number of fatalities caused by rip currents. Developed in collaboration with Surf Lifesaving New Zealand, the rip current identification tool has achieved a detection rate of roughly 90% in trials.

Training, testing and validating autonomous vehicles requires a continuous pipeline -- or data factory -- to introduce new scenarios and refine deep neural networks. A key component of this process is simulation. AV developers can test a virtually limitless number of scenarios, repeatably and at scale, with high-fidelity, physically based simulation. And like much of the technology related to AI, simulation is constantly evolving and improving, getting ever nearer to closing the gap between the real and virtual worlds. NVIDIA DRIVE Sim, built on Omniverse, provides a virtual proving ground for AV testing and validation.

Entrepreneur Jason Mars calls conversation our "first technology." Before humans invented the wheel, crafted a spear or tamed fire, we mastered the superpower of talking to one another. That makes conversation an incredibly important tool. But if you've dealt with the automated chatbots deployed by the customer service arms of just about any big organization lately -- whether banks or airlines -- you also know how hard it can be to get it right. Deep learning AI and new techniques such as zero-shot learning promise to change that.

YouTube is a great platform for learners and has some best channels for learning data science. That's why I thought to share with you the 15 Best YouTube Channels to Learn Data Science. So if you are planning to learn data science, then these data science YouTube channels will help you to understand the fundamentals of data science. Now without any further ado, let's start finding the best youtube channels to learn data science- Math is essential for data science and machine learning to understand how machine learning algorithms work. So if you want to learn math concepts, then you should check this YouTube channel.