Academics and practitioners alike believe that continual learning (CL) is a fundamental step towards artificial intelligence. Continual learning is the ability of a model to learn continually from a stream of data. In practice, this means supporting the ability of a model to autonomously learn and adapt in production as new data comes in. Some may know it as auto-adaptive learning, or continual AutoML. The idea of CL is to mimic humans ability to continually acquire, fine-tune, and transfer knowledge and skills throughout their lifespan.

Deepfakes are video manipulations that can make people say seemingly strange things. Barack Obama and Nicolas Cage have been featured in these videos. "The Boy Who Cried Wolf" has long been a staple on nursery room shelves for a reason: It teaches kids that joking too much about a possible threat may turn people ignorant when the threat becomes an actual danger. President Donald Trump has been warning about "fake news" throughout his entire political career. And now the real wolf might be just around the corner.

Today's social media platforms are ruled over by opaque algorithms that decide what we see, guiding us towards content they believe we have the greatest chance of engaging with and creating new content in response to. We have no right to see inside these algorithms or control them in any way. Their decisions are made with their profit-minded creators in mind, not our intellectual and emotional best interests. Could our personal digital assistants of the future wade through the vast social wasteland on our behalf, tracking down content and curating a specialized feed that represents what we want to see, not what social platforms want us to see and even shield us from hate? The algorithms that run today's social media platforms are designed to mindlessly drive us towards the highest-engagement content.

Looking to get started with AI but don't know how? NVIDIA has just published a new self-paced Deep Learning Institute course that uses the newly released Jetson Nano Developer Kit to get up and running fast. In the course, students will learn to collect image data and use it to train, optimize, and deploy AI models for custom tasks like recognizing hand gestures, and image regression for locating a key point in an image. The Jetson Nano Developer Kit is a powerful, easy-to-use, mini AI computer that lets users run multiple neural networks in parallel. The device is perfectly suited for image classification, object detection, segmentation, speech processing, and more. Students will use Jupyter notebooks on the Jetson Nano to build a deep learning classification project with computer vision models.

If it seems like Amazon is listening in, it's because they are. Here's how to stop it. Every few months, I take a hard look at Amazon Echo and catch up on the latest skills. If you're not familiar with skills, these programs give Alexa new tricks. What's interesting is that individuals produce many Alexa skills, and Amazon shares skill revenues with developers.

The episode talks about how AI has been around for some time, has evolved over the years, needs to make a transition from the "consumer" to the "industrial" and leaves you with an ACTION (watch to learn more:-))

Until a decade or so ago, artificial intelligence was -- for most Americans -- something that existed only in the movies. Now, Alexa and Google Home have moved into numerous households and live only to please. According to many reports, 2019 will be the year of AI. A recent McKinsey Global survey found that 47 percent of companies have implemented at least one AI capability into their business processes, more than doubling the 20 percent that reported using AI the previous year. Artificial intelligence can be the difference maker in an entrepreneur's business, providing everything from customer support to automated supervision of assembly lines.

Machine learning can be a fantastic tool for creators, but integrating AI into your workflow is a challenge for those who can't code. A new program called Runway ML aims to make this process easier by providing artists, designers, filmmakers, and others with an "app store" of machine learning applications that can be activated with a few clicks. Say you're an animator on a budget who wants to turn a video of a human actor into a 3D model. Instead of hiring expensive motion capture equipment, you could use Runway to apply a neural network called "PosetNet" to your footage, creating wireframe models of your actor that can then be exported for animation. Or say you need to remove a coffee cup that was accidentally left in a shot on your high-budget fantasy TV drama.

Last week, a video went viral on social media around the world. It shows a robotic arm picking up a bowling ball, spinning its arm around, and hurling the ball down the lane at speed, sending all the bowling pins flying. It soon emerged that the very real-looking video was in fact fake, the work of a motion graphics designer who had hash-tagged it with words such as animation, rendering and CGI to indicate that it was computer-generated, before sharing on social media. Nonetheless, from the reactions it was clear that not everyone picked up the clues; many if not most people thought it was real. The impossibility of distinguishing between fake and real in images and videos is an everyday occurrence now, something we just have to live with.

The secrets behind traditional folk music from across the globe and dating back thousands of years are likely to be revealed by using artificial intelligence, according to a leading academic expert. Speaking at Folk Music Analysis UK, a three-day event showcasing cutting edge technological research methods and findings using ethnomusicology and computational analysis at Birmingham City University, Dr. Islah Ali-MacLachlan highlighted advances in the field which now allow for greater understanding of the cultural data embedded in folk music. The senior lecturer in audio engineering and acoustics said, "In this era of streaming, music discovery, digitization and algorithms, one of the last bastions of music to be extensively and accurately mapped as well as understood is traditional folk music from across the globe. Sounds that have taken hundreds, if not thousands, of years to evolve, are full of human, cultural, religious, societal and geographical intricacies that we will benefit from understanding. The Folk Music Analysis workshop is a chance to discuss worldwide folk music traditions and the tools we use to extract information about them."