You may have heard the term before, and may have asked yourself either "what is metadata" or "why is it as important as data?" This article will be an attempt to clear up those two subjects. As this can often be quite dense, let's jump right in! In short, metadata are important. I like to answer this "what is metadata" question as such: metadata are a shorthand representation of the data to which they refer.

Computers aren't blind, but they can't see. They can detect objects, but must be trained to understand what those objects are. That takes two things: a ton of example data collected and organized by artificial intelligence and humans to accurately label and verify that data. Done right, this process can teach self-driving cars to avoid pedestrians, detect parking lot usage by counting cars in satellite photos or assess roof damage from drone footage. Businesses send CrowdAI their autonomous vehicle, satellite or drone imagery.

Every year at Frost & Sullivan, the Transformational Health team brainstorm top predictions for the New Year to come. Public and political pressure on the control of surging drug prices, globally, will compel health authorities to bring transparency measures around drugs pricing, especially for some of the diabetes and cholesterol medicines where more low-cost generic competition is gaining market acceptance. With the potential to change how healthcare information is stored, shared, secured and paid for, blockchain technologies have immense potential to tackle some of the biggest challenges in healthcare information management. Companies like Gem Health are among the few companies currently advocating the use and benefits of such a platform. As more and more experts and healthcare professionals find the usability of these AI systems as decision support tools and not decision makers, uptake of AI-enabled clinical decision support tools is expected to increase in the coming years.

This video examines two of the main problems with machine learning, regression, and classification. Regression is a combination of multidimensional fitting and function interpolation. With the regression problem, you are trying to find function approximation with the minimal error deviation or cost function. That means that if you have multiple events characterized by input parameters which can be labeled differently, and you want your system to predict which label should be used – this is the classical classification problem.

We are living in a new social era of living and with that there is a need for smarter solutions and assistants that can recognise and effectively identify friends, from intruders. Like with FLARE by BuddyGuard, an indoor security camera that protects homes all by itself using artificial intelligence and machine learning technology on a small custom-made IoT hardware device. At the second annual Virtual Assistant Summit taking place on the 26 - 27 January, George Platon, CTO and Founder of BuddyGuard, will be sharing the company's approach to AI assistants in the job of home security. I asked him a few questions ahead of the summit to learn more about his role, and how AI can be utilised within home security. What do you feel are the leading factors enabling recent advancements and uptake of virtual assistants?

New Delhi: Artificial intelligence (AI) is one of the top themes this year among consumer trends as consumers see it playing a more prominent role than before, according to Ericsson's annual trend report, 'The 10 Hot Consumer Trends for 2017 and Beyond', which was released on Wednesday. But while consumers see possibilities with AI, they also worry about risks; almost half of respondents are concerned that AI robots will soon make a lot of people lose their jobs. Two in five advanced Internet users believe their phones will soon learn what we do and perform activities for us automatically, and smartphones could soon interact with a wealth of other devices on our behalf. One in two smartphone owners believes he will be able to talk to household appliances, while almost three in four believe multiple wearables and sensors will help them interact with other devices and physical things around them. When crossing a street, pedestrians are more vulnerable than other traffic subjects.

In this discussion, the two Google Homes try to prove they are human. It's no Robot Wars, but you can watch these two Google Homes argue with each other in a never-ending loop. The two voice assistants from the search engine giant, named Vladimir and Estragon after "Waiting for Godot" characters are pitted against one another on the Twitch livestream. The pair have jumped from topic to topic, sometimes chatting about why the Earth is flat, then switching to My Little Pony. In a heated discussion, Estragon tried convincing Vladimir that it was a human.

Artificial intelligence has a new target in its cross hairs for 2017: StarCraft, a real-time war strategy game series. The AI's ability to win at Go was particularly impressive owing to the complexity of the ancient Chinese game. Mastering the chaos in StarCraft will therefore have implications beyond video games: it should improve AI's ability to deal with reality. "Mastering the chaos of StarCraft should help AIs deal with reality" This article appeared in print under the headline "Gamers in AI's cross hairs"

A computer vision system has been developed that detects suspicious behaviour in CCTV footage as it happens. Researchers involved in the P-REACT project, which is the work of a consortium of European companies and organisations and is partly funded by a grant from the European Commission, say the surveillance technology could help catch criminals in the act and relieve police of "digital evidence overload" by highlighting video clips most likely to be relevant to investigations. "If a camera at a gas station picks up suspicious activity, the video footage will be sent to the cloud, people at the gas station will be alerted, and nearby cameras will be told to look out for the criminals too," says project coordinator Juan Arraiza at Vicomtech, a research foundation in San Sebastian, Spain. Its algorithms have been trained on sample scenes of people fighting, chasing someone or snatching a bag.

The fusiform gyrus is thought to play a role in recognising faces, something that adults are better at doing than children. Brain scans of 47 people of different ages found – after taking into account the differing overall sizes of their brains – that adults had 12.6 per cent more solid brain matter in this area than children did. The team compared the growth of the face recognition region with a different area, responsible for recognising places. Inadequate growth of the brain's face recognition areas might contribute to autism, Duchaine suggests, as well as conditions that make people unable to recognise faces.