An ongoing internet outage appears to be spreading and taking down many of the world's biggest websites. Companies including Twitter, Netflix, PayPal and eBay appeared to have their websites broken. And other services like PlayStation Network appeared to be hit by the outage. Almost every major service that isn't part of a major internet provider seemed to be having issues. As such, Google and Facebook appeared to stay up – but almost everything else was down, according to Down Detector's dashboard. Amy Rimmer, Research Engineer at Jaguar Land Rover, demonstrates the car manufacturer's Advanced Highway Assist in a Range Rover, which drives the vehicle, overtakes and can detect vehicles in the blind spot, during the first demonstrations of the UK Autodrive Project at HORIBA MIRA Proving Ground in Nuneaton, Warwickshire Chris Burbridge, Autonomous Driving Software Engineer for Tata Motors European Technical Centre, demonstrates the car manufacturer's GLOSA V2X functionality, which is connected to the traffic lights and shares information with the driver, during the first demonstrations of the UK Autodrive Project at HORIBA MIRA Proving Ground in Nuneaton, Warwickshire In its facilities, JAXA develop satellites and analyse their observation data, train astronauts for utilization in the Japanese Experiment Module'Kibo' of the International Space Station (ISS) and develop launch vehicles The robot developed by Seed Solutions sings and dances to the music during the Japan Robot Week 2016 at Tokyo Big Sight.

Are you just trying to identify (classify) one of your 50 individuals from a new photo, or are you specifically looking to create an embedding representation that can be used to differentiate individuals not in your training set? For classification purposes that might be enough data (esp. For an embedding to differentiate arbitrary individuals not in the training set, I think you'd need a lot more data. It's a bit of an apples and oranges comparison since different models are trained on different datasets, but nonetheless note the significant gain in accuracy of DeepFace and VGGFace over OpenFace, despite having slightly fewer individuals in the training set... it seems that it's the number of photos (as well as model differences) that's making the difference, and maybe that 0.6M (600,000) photos just isn't enough for this domain.

Students can now make their dream of making a robot friend come true. As a collaborative effort of SoftBank Robotics America and the on-campus clothing store the Ave at USC, a humanoid robot named Pepper will be visiting USC and staying on the third floor of the bookstore from Oct. 18 to Oct. 20. Pepper will be greeting the customers, informing them about the Ave and helping them customize shoes.

In 2004, at the end of a long day of classes, I was putting in some extra hours in my school's computer lab perfecting my latest project: Acey Deucey. A program powered using my crude understanding of the Visual Basic coding language, this program felt alive to me. Not just because I'd created an interface that looked sort of like a first grader's drawing of a third-rate casino, but because it was a game that users could play against the computer. It wasn't machine learning--but to me, it sure felt intelligent. I sat before the screen, testing and retesting the code, and feeling almost exactly like Mary Shelley's Victor Frankenstein: "No one can conceive the variety of feelings which bore me onwards, like a hurricane, in the first enthusiasm of success. Life and death appeared to me ideal bounds, which I should first break through, and pour a torrent of light into our dark world."

Colorization of grayscale images is a simple task for the human imagination. Researchers from the Toyota Technological Institute at Chicago and University of Chicago developed a fully automatic image colorization system using deep learning and GPUs. Their paper mentions previous approaches required some level of user input. Using a TITAN X GPU, they trained their deep neural network to predict hue and chroma distributions for each pixel given its hypercolumn descriptor. The predicted distributions then determine color assignment at test time.

In our earlier posts we've discussed, and proven, empathys' growing importance in artificial intelligence. The next questions to ask are, "How do we make AI empathetic?", "How do we build emotion into our AIs?" and "Can we ever make AI feel?" At Kairos, we believe the answer to the "how" question is in face analysis. Facial recognition allows software to identify and verify human faces while emotion analysis allows software to measure and read the emotions on those found faces. More importantly, facial recognition and emotion analysis looks at each user as an individual and captures their specific human data.

David Kenny took the helm of IBM's Watson Group ibm in February, after Big Blue acquired The Weather Company, where Kenny had served as CEO. In the months since then, the Watson business has grown dramatically, with well over 100,000 developers worldwide now working with more than three dozen Watson application program interfaces (APIs). Fortune Deputy Editor Clifton Leaf caught up with Kenny in mid-October, when IBM Watson's General Manager was in San Francisco, getting ready to open Watson West--the AI system's newest business outpost--and to launch the company's second World of Watson conference, a gathering of its burgeoning ecosystem of partners and users, in Las Vegas on Oct. 24. FORTUNE: We hear a lot of terms on the AI front these days--"artificial intelligence," "machine learning," "deep learning," "unsupervised learning," and the one IBM uses to describe Watson: "cognitive computing." KENNY: Deep learning is a subset of machine learning, which essentially is a set of algorithms. Deep-learning uses more advanced things like convolutional neural networks, which basically means you can look at things more deeply into more layers. Machine learning could work, for example, when it came to reading text. Deep learning was needed when we wanted to read an X-ray. And all of that has led to this concept of artificial intelligence--though at IBM, we tend to say, in many cases, that it's not artificial as much as it's augmented.

It's been a big year for Artificial Intelligence, and its related terms like Cognitive Computing, Machine Intelligence, and Intelligent Machines, and for all its associated branches, from Machine Learning to Neural Networking and Natural Language Processing. IBM, for example, has continued making strides with Watson in verticals such as healthcare and financial services. The vendor announced everything from advances in its ability to add rich image analytics with Deep Learning to the Watson Health platform, to a furthering of its partnership with Deloitte that will use the technology in solutions to more efficiently and immediately manage risk and regulatory compliance requirements. At the end of last year, we also saw Tesla Motors CEO Elon Musk join with other industry figures to launch a non-profit AI research venture, OpenAI, with its stated goal being to help spread the technology to a broader base and direct it to having a positive impact on humanity. Furthermore, just to pick one Google announcement in the area, the search giant said it's using AI and Machine Learning in the Smart Reply capability in its Gmail Inbox mobile email client.

Often called cognitive computing or machine learning, AI is computers completing tasks traditionally performed by people. One way that AI has affected the legal space is its ability to process data to find patterns, perform tests, analyse and evaluate data to produce a set of results. The law's framework of rules makes it ideal for applying AI systems, where computers will process those rules, enabling them to complete tasks usually performed by lawyers. In simple terms, AI technology works by applying an amount of sample data and outcomes, previously examined by a professional, to a cognitive system, which is then able to analyse large amounts of data at high speed to produce a faster and more accurate result. The goal of AI isn't to change the nature of legal work or replace human lawyers, but to enable lawyers to concentrate on more cognitive tasks such as developing legal arguments, instead of spending long periods of time on routine duties like drafting and reviewing documents, extensive research of case files and other un-billable tasks.

Seeing a driver take their hands off the wheel can be enough to unnerve even the most hardened front seat passenger. But auto firm Jaguar Land Rover is looking to make this cold sweat inducing sight a common occurrence, with its autonomous driver technology. The firm is one of a trio of car makers set to trial vehicles which can'talk' to one another, drive themselves, skip red lights and even help you find a parking spot. Jaguar Land Rover is looking to make this cold sweat inducing sight a common occurrence. A trio of car manufacturers has joined with universities and tech firms to launch a three-year project to test connected and automated cars in the UK.