Since the discovery and emergence of computers, we know that they lack thought capacities. That is something where human mind surpasses computing technologies. Computers might not possess cognitive abilities, but they are capable of executing operations which completely rely on human perceptions. Whether it is business security, critical processes or customer experiences, it is possible to utilize the power of automation. From handwriting recognition, face identification and behavioral pattern determination to any task requiring cognitive skills, computers are capable of delivering the right solutions.

Artificial intelligence has had its share of ups and downs recently. In what was widely seen as a key milestone for artificial intelligence (AI) researchers, one system beat a former world champion at a mind-bendingly intricate board game. But then, just a week later, a "chatbot" that was designed to learn from its interactions with humans on Twitter had a highly public racist meltdown on the social networking site. How did this happen, and what does it mean for the dynamic field of AI? In early March, a Google-made artificial intelligence system beat former world champ Lee Sedol four matches to one at an ancient Chinese game, called Go, that is considered more complex than chess, which was previously used as a benchmark to assess progress in machine intelligence.

Does predictability provide an overriding concept and perhaps a metric for evaluating when LAWS are acceptable or when they might be unacceptable under international humanitarian law? Arguably, if the behavior of an autonomous weapon is predictable, deploying it might be considered no different from, for example, launching a ballistic missile. This, of course, presumes that we can know how predictable the behavior of a specific autonomous weapon will be. Over the past two years, this body has focused upon ethical and legal challenges to LAWS. In addition, there are contentions that autonomous weaponry will fail to perform as expected, will behave unpredictably on occasion, and are therefore inherently risky and liable to commit acts that violate IHL, even when this is not the intention of those who deploy the systems.

Kaggle is a Data Science community where thousands of Data Scientists compete to solve complex data problems. In this article we are going to see how to go through a Kaggle competition step by step. The contest explored here is the San Francisco Crime Classification contest. The goal is to classify a crime occurrence knowing the time and place it happened. Here, the objectives are fixed by Kaggle.

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Artificial intelligence "chat-bots" could be the future of online interaction, Facebook has said. The social media giant is building the ability into its Messenger app for third-party software to engage in lifelike text exchanges with users. The move was announced at the firm's annual F8 developers' conference in San Francisco, with founder Mark Zuckerberg predicting that bots could eventually be better than real people at natural language text conversations. He said: "We think you should be able to text message a business like you would a friend, and get a quick response. "Our goal with artificial intelligence is to build systems that are better than people at perception seeing, hearing, language and so on."

As part of the auto show 2015 Tokyo Motor Show was shown a lot of amazing new products. However, the most striking is the machine invented by engineers Yamaha. Yamaha has decided to all hit, unveiled its new development – a humanoid robot motorcycle racer Motobot. This electro-mechanical humanoid creature, able to control the bike on the track. It can easily manipulate the controls like knobs accelerator, brake lever and shift.

It is a federal crime to shoot down aircraft, and this week, the FAA confirm that that includes drones. This is great news for anyone who has a drone, and for anyone who doesn't want errant bullets falling from the sky, and it's bad news for anyone eager to pump a quadcopter full of lead. From Forbes' John Goglia, who confirmed this with the FAA: According to the FAA "regardless of the situation, shooting at any aircraft -- including unmanned aircraft -- poses a significant safety hazard. An unmanned aircraft hit by gunfire could crash, causing damage to persons or property on the ground, or it could collide with other objects in the air. To reach this justification, the FAA turned to 18 U.S.C. 32, a law that in part expands "United States jurisdiction over aircraft sabotage to include destruction of any aircraft in the special aircraft jurisdiction of the United States."

Let's say you're scrolling through your favorite social media app and you come across a series of pictures of a man in a tuxedo and a woman in a long white dress. An automated image captioning system might describe that scene as "a picture of a man and a woman," or maybe even "a bride and a groom." But a person might look at the pictures and think, "Wow, my friends got married! As image captioning tools get increasingly good at correctly recognizing the objects in an image, a group of researchers is taking the technology one step further. They are working on a system that can automatically describe a series of images in the same kind of way that a human would, by focusing not just on the items in the picture but also what's happening and how it might make a person feel. "Captioning is about taking concrete objects and putting them together in a literal description," said Margaret Mitchell, a Microsoft researcher who is leading the research project. "What I've been calling visual ...

We are already aware of machine learning codes disrupting the fashion retail market such as inventory management, virtual reality systems for apparel fitting and most commonly, recommendation systems based on consumer preferences, buying behavior, etc. I myself captured some of these in the post on ML applications in the e-commerce ecosystem, written a few months ago. Today, on a separate note, we'll look at some of the initiatives where machine learning is disrupting the consumer fashion market in some really novel and unique ways. In a way, these models are planning for a future where machines will be able to interpret art the way we do. Determining "Fashionability" Researchers at University of Toronto are working at building a machine learning model to help you improve your "fashionability".