Bhagat Singh, Marie Curie, Charles Darwin, and other historical figures were momentarily'brought back to life' via Deep Nostalgia – an AI tool released by the genealogy website, MyHeritage. Kind of surreal to take a photo of the singularly inspiring Bhagat Singh -- a revolutionary voice in 1920s India, who was hung by the British in 1931, at the age of 24 -- run it through the Heritage AI algorithm, and see him reanimated. When Ken Burns meets Deep Fake: MyHeritage is offering a tool dubbed #DeepNostalgia, meant to animate old family pictures. Holy Darwin this #deepfake is so scary, Mr. Darwin!!#DeepNostalgia pic.twitter.com/vxWP5LnO9L Deep Nostalgia created quite a furore of late, with animated pictures of historical figures running rife in social media.
Whereas many AI models are trained on carefully labelled datasets, Facebook said SEER learned how to identify objects in photos by analyzing random, unlabeled and uncurated Instagram images. This AI technique is known as self-supervised learning. "The future of AI is in creating systems that can learn directly from whatever information they're given -- whether it's text, images, or another type of data -- without relying on carefully curated and labeled data sets to teach them how to recognize objects in a photo, interpret a block of text, or perform any of the countless other tasks that we ask it to," Facebook's researchers wrote in a blog post. "SEER's performance demonstrates that self-supervised learning can excel at computer vision tasks in real-world settings," they added. "This is a breakthrough that ultimately clears the path for more flexible, accurate, and adaptable computer vision models in the future."
The year 2020 will go down in history for a variety of dubious reasons. But for some companies, they will look back on last year and triumphantly proclaim that 2020 was the year they finally adopted artificial intelligence (AI) to grow their business. Because AI takes traditional market segmentation several steps beyond data analysis into actionable journeys. Campaigns can have multiple pathways selected by the behavior of the customer on the website as viewed through click, interaction and download behavior. Each can be personalized at every step.
This week at Microsoft Ignite, a number of new developments to Azure were in focus. While there were dozens of updates to the world's second-largest public cloud, data was once again in the spotlight. The company made a series of announcements to enable users to extract more value from the exponential increase in data. Satya Nadella, in his Ignite keynote, provided a new visionary direction, or at least a new way of expressing the company's cloud endeavors. In short, the Microsoft cloud is evolving to further embrace edge, privacy, security, AI, and developers (both coders and no coders), and to serve as an engine of job creation. On the surface, this shift appears subtle.
It has 3 components: 1. #SUM: Understand ethical values that #Support, #Underwrite, and #Motivate a responsible data design and use ecosystem. SUM comprises #Respect, #Connect, #Care, and #Protect to provide a way to think about the moral scope of the societal and ethical impacts of a project and establish criteria to evaluate its ethical permissibility. FAST provides moral and practical tools to ensure a project is bias-mitigating, non-discriminatory, and fair. It also safeguards public trust in a project's capacity to deliver safe and reliable AI innovation. It sets up transparent processes of design and implementation to safeguard and justify both the AI project and its product.
Fox News Flash top headlines are here. Check out what's clicking on Foxnews.com. NASA is slated to announce what it says is a "series of firsts" in its Mars 2020 Perseverance rover mission. On Friday, mission team members from Southern California's Jet Propulsion Laboratory (JPL) will hold their first news conference since the expedition began and the rover touched down on the red planet's surface in February. The teleconference and visuals are set to stream live on NASA's JPL YouTube channel at 3:30 p.m. ET.
Facebook's researchers have unveiled a new AI model that can learn from any random group of unlabeled images on the internet. Facebook's researchers have unveiled a new AI model that can learn from any random group of unlabeled images on the internet, in a breakthrough that, although still in its early stages, the team expects to generate a "revolution" in computer vision. Dubbed SEER (SElf-SupERvised), the model was fed one billion publicly available Instagram images, which had not previously been manually curated. But even without the labels and annotations that typically go into algorithm training, SEER was able to autonomously work its way through the dataset, learning as it was going, and eventually achieving top levels of accuracy on tasks such as object detection. The method, aptly named self-supervised learning, is already well-established in the field of AI: it consists of creating systems that can learn directly from the information they are given, without having to rely on carefully labeled datasets to teach them how to perform a task such as recognizing an object in a photo or translating a block of text.
Artificial intelligence built by Facebook has learned to classify images from 1 billion Instagram photos. The AI used a different learning technique to many other similar algorithms, relying less on input from humans. The team behind it says the AI learns in a more common sense way. Conventionally, computer vision systems are trained to identify specific things, such as a cat or a dog. They achieve this by learning from a large collection of images that have been annotated to describe what is in them.
As impressively capable as AI systems are these days, teaching machines to perform various tasks, whether its translating speech in real time or accurately differentiating between chihuahuas and blueberry muffins. But that process still involves some amount of hand holding and data curation by the humans training them. However the emergence of self supervised learning (SSL) methods, which have already revolutionized natural language processing, could hold the key to imbuing AI with some much needed common sense. Facebook's AI research division (FAIR) has now, for the first time, applied SSL to computer vision training. "We've developed SEER (SElf-supERvised), a new billion-parameter self-supervised computer vision model that can learn from any random group of images on the internet, without the need for careful curation and labeling that goes into most computer vision training today," Facebook AI researchers wrote in a blog post Thursday.
Most artificial intelligence is still built on a foundation of human toil. Peer inside an AI algorithm and you'll find something constructed using data that was curated and labeled by an army of human workers. Now, Facebook has shown how some AI algorithms can learn to do useful work with far less human help. The company built an algorithm that learned to recognize objects in images with little help from labels. The Facebook algorithm, called Seer (for SElf-supERvised), fed on more than a billion images scraped from Instagram, deciding for itself which objects look alike.