Researchers have discovered a number of security vulnerabilities in existing robots, which they say can be used by criminals to cause serious harm to their owners. IOActive tested robot operating systems and other software in order to identify nearly 50 flaws in robots from vendors including SoftBank Robotics, UBTECH Robotics, ROBOTIS, Universal Robots, Rethink Robotics, and Asratec Corp. Insecure communications, authentication issues, weak cryptography, memory corruption and privacy problems were just some of the issues named by the firm's senior security consultant Lucas Apa. The research paper says that criminals could exploit the flaws to gain control of robots in homes and workplaces, using them to spy on people and cause physical damage. The giant human-like robot bears a striking resemblance to the military robots starring in the movie'Avatar' and is claimed as a world first by its creators from a South Korean robotic company Waseda University's saxophonist robot WAS-5, ...

Gartner says 85% of customer interactions will be managed without a human by 2018. The prognostications of analysts are to be taken with a pinch of salt, but those working in the eCommerce field are hardly less enthusiastic. "Online retailers are scrambling to partner with or adopt new AI technologies to help facilitate customer interaction to try and match and even surpass the typical in-store experience" Let's have a look at two of the ways eCommerce merchants are already using AI to augment the experience of both retailers and their customers.

But first, let's start with some background. Two major tasks in computer vision are object classification and object detection. In the first case the system is supposed to correctly label the dominant object in an image. In the second case it should provide correct labels and locations for all objects in an image. Of course there are other interesting areas of computer vision, such as image segmentation, but today we're going to focus on detection.

Which authors of this paper are endorsers? Disable MathJax (What is MathJax?)

If you work in the tech sector or have interest in the tech scene, you've probably heard the term "deep learning" floating around quite a bit. It's the emerging area of computer science that is revolutionizing artificial intelligence, allowing us to build machines and systems of the future. Although deep learning is making our lives easier, understanding how it works can be hard. Having spent quite some time exploring the world of deep learning, mostly for computer vision applications, I learned a thing or two on what it's all about and therefore I'm here to share what I learned. Firstly, before you understand deep learning, it's important that you know what machine learning is.

Neural networks exists in all shapes and sizes, and are often characterized by their input and output data type. For instance, image classifiers are built with Convolutional Neural Networks. They take images as inputs, and output a probability distribution of the classes. Autoencoders (AE) are a family of neural networks for which the input is the same as the output*. They work by compressing the input into a latent-space representation, and then reconstructing the output from this representation. A really popular use for autoencoders is to apply them to images.

Scientists have developed a new artificial intelligence tool that can predict whether someone will attempt suicide as far off as two years into the future with up to 80 per cent accuracy. The team then combed through the electronic health records, which were anonymous, and identified more than 3,200 people who had attempted suicide. Using machine learning to examine all of those details, the algorithms were able to'learn' which combination of factors in the records could most accurately predict future suicide attempts. "The machine learns the optimal combination of risk factors. What really matters is how this algorithm and these variables interact with one another as a whole," said Jessica Ribeiro of Florida State University.

Azure Machine Learning is a cloud predictive analytics service that makes it possible to quickly create and deploy predictive models as analytics solutions. The Machine Learning service is cloud-based, provides compute resource and memory flexibility, and eliminates setup and installation concerns because you can work through your web browser on any Internet-connected PC. The Cortana Intelligence Gallery provides examples of how machine machine learning has been applied in various industries--from travel and health care to manufacturing and retail--to analyze, predict, and guide actionable insights. Microsoft Azure Machine Learning Studio is a collaborative visual development environment that enables you to build, test, and deploy predictive analytics solutions in the cloud. You upload or connect to your data, choose an algorithm from a ready-to-use library of algorithms, and build an end-to-end predictive workflow.

BARCELONA, Spain--Chatbots may be all the buzz for brands these days, but some tech execs anticipate it will be tough for brands to create them--much like they struggled to make branded apps. During a panel at Mobile World Congress in Barcelona, execs from PayPal, Google, Adobe, Sprint and GupShup talked about how messaging apps are evolving as platforms for content and commerce. While voice technology and artificial intelligence have been big buzzwords in the industry thanks to Facebook's moves to create bots and apps like Kik and Line gaining steam, not all marketers have the resources to realistically create and manage them, argued Harper Reed, PayPal's entrepreneur in residence of next-generation commerce. "Brands don't know how to adapt these things for the technology that we find everyday and I think there's a lot of hurdles between a normal consumer brand in the West, for instance, to figure out what is their mobile strategy let alone what is their chat app strategy," he said. Harper used his company's own technology to explain marketers' problem.

The IBM CEO has been at Big Blue for more than three decades, we wanted to know how she keeps engaged, about IBM's future with artificial intelligence and what career tips she might have for young women interested in technology. These are questions she answered via email, just prior to the opening of HIMSS17, where she delivered the keynote. Q: You've been at IBM since 1981. How do you keep it fresh? A: As IBM has done throughout its 105-year history, we remain dedicated to leading the world into a more prosperous and progressive future.