The writer William Gibson once said that the future is here, just not evenly distributed. That was the case with the World Wide Web 20 years ago, when some business models – notably e-commerce and new media – took off faster than others. Now a similar trend is happening with artificial intelligence, or AI. The promise of AI has seemingly been just on the horizon for years, with little evidence of change in the lives of most consumers. A few years ago, buzzwords like "big data" hinted at the potential, but ending up generating little actual impact.

Google's Perspective API, created in conjunction with Alphabet incubee Jigsaw, is supposed to provide an automated way to detect "toxic" language in social media. "Through different experiments, we show that an adversary can deceive the system by misspelling the abusive words or by adding punctuations between the letters," the four academics wrote in their recently published paper, "Deceiving Google's Perspective API Built for Detecting Toxic Comments." The API is intended to allow digital publishers to assess the sentiment expressed in online posts in real time. Words get sent to a server for analysis and scores get returned, ideally allowing publishers to detect trolls as they type and to take the necessary steps to keep online interaction civil. In practice, just like other automated content filtering, malware detection, and spam detection systems, Perspective can be fooled.

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.