It's the first coronal mass ejection seen outside our sun. Breakthroughs, discoveries, and DIY tips sent every weekday. Skygazers can once again thank the sun for the latest round of Northern Lights that recently danced above much of the United States. Also known as the aurora borealis in the north, (or aurora australis in the Southern Hemisphere) these night sky events get their start on the sun's surface after coronal mass ejections (CMEs) spew ionized clouds of high energy particles towards Earth. The radiation then interacts with the planet's magnetosphere and generates the vivid colors in Earth's atmosphere-as well as the occasional electrical grid and satellite array headache .

Hundreds of millions of years of evolution have produced a variety of life-forms, each intelligent in its own fashion. Each species has evolved to develop innate skills, learning capacities, and a physical form that ensures survival in its environment. But despite being inspired by nature and evolution, the field of artificial intelligence has largely focused on creating the elements of intelligence separately and fusing them together after the development process. While this approach has yielded great results, it has also limited the flexibility of AI agents in some of the basic skills found in even the simplest life-forms. In a new paper published in the scientific journal Nature, AI researchers at Stanford University present a new technique that can help take steps toward overcoming some of these limits.

The European Commission recently announced its proposal for the regulation of artificial intelligence, looking to ban "unacceptable" uses of artificial intelligence. Up until now, the challenges for businesses getting AI'wrong' were bad press, reputation damage, loss of trust and market share, and most importantly for sensitive applications, harm to individuals. But with these new rules, two new consequences are arising: plain interdiction of certain AI systems, and GDPR-like fines. While for now this is only proposed for the EU, the definitions and principles set out may have wider-reaching implications, not only on how AI is perceived but also on how businesses should handle and work with AI. The new regulation sets four levels of risk: unacceptable, high, low, minimum, with HR AI systems sitting in the "High Risk" category.

We have an intuition that more observations is better. This is the same intuition behind the idea that if we collect more data, our sample of data will be more representative of the problem domain. There is a theorem in statistics and probability that supports this intuition that is a pillar of both of these fields and has important implications in applied machine learning. The name of this theorem is the law of large numbers. In this tutorial, you will discover the law of large numbers and why it is important in applied machine learning.