During Microsoft's Build Conference earlier this year, CEO Satya Nadella delivered the three-hour keynote address, in which he highlighted his belief that the future of technology lies in human language. In this new wave of technology, conversation is the new interface, and "bots are the new apps." While not as flashy as virtual reality nor as immediately practical as 3D printing, chatbots are nevertheless gaining major traction this year, with support coming from across the entire tech industry. The big tech enterprises are all entering the chatbot space, and many startups are too. The question is, why is so much attention being given to chatbots recently, and should your business be paying attention too?

From Google search results to Netflix recommendations and investment strategies, Bayes Theorem (also often called Bayes Rule or Bayes Formula) is used across countless industries to help calculate and assess probability. Bayesian statistics is taught in most first-year statistics classes across the nation, but there is one major problem that many students (and others who are interested in the theorem) face. The theorem is not intuitive for most people, and understanding how it works can be a challenge, especially because it is often taught without visual aids. In this guide, we unpack the various components of the theorem and provide a basic overview of how it works – and with illustrations to help. Three scenarios – the flu, breathalyzer tests, and peacekeeping – are used throughout the booklet to teach how problems involving Bayes Theorem can be approached and solved.

What is an evolutionary algorithm?

Be the change that you want to see in the world.

The popularity of the ASOIAF series and Game of Thrones has resulted in a large community of fans.

Human Rights Watch issued a report Friday urging a ban on the development of fully autonomous weapons. The 49-page report entitled "Making the Case: The Dangers of Killer Robots and the Need for a Preemptive Ban" detailed the various dangers of creating weapons that could think for themselves, including the concern that self-operating defense technology would remove the human element from warfare upon which most clauses of military law are bound. The organization argued machines would not be able to responsibly make the same complex tactical decisions involved in warfare and that existing laws did not adequately cover their use in combat. Human Rights Watch also contended that removing the human element of warfare raised serious moral issues, saying lack of empathy would exacerbate unlawful and unnecessary violence. The organization warned that such technology could be used by authoritarian leaders as a means of controlling and subjugating populations without fear of revolt.

DeepMind, Google's artificial intelligence outfit, wants to streamline health care by using machine learning to provide medics with intelligent notifications. But not everyone is happy with the piles of data being shared with the company. The project will provide medics across a number of London hospitals with alerts about patients via an app called Streams. The app is meant to provide easy access to patient histories and test results for nurses and doctors. But its system will also learn to track patterns in patients' blood test data and flag cases that show early signs of kidney injury to the appropriate doctors.

There's been some stunning progress in artificial intelligence of late, but it's been surprisingly flat. Now AI researchers are moving beyond two-dimensional images and pixels. The work could have a big impact on robotics and self-driving cars, helping to make machines that can learn how to act more intelligently in the real world. "An exciting and important trend is the move in learning-based vision systems from just doing things with images to doing things with three-dimensional objects," says Josh Tenenbaum, a professor in MIT's Department of Brain and Cognitive Sciences. "That includes seeing objects in depth and modeling whole solid objects--not just recognizing that this pattern of pixels is a dog or a chair or table." Tenenbaum and colleagues used a popular machine-learning technique known as generative adversarial modeling to have a computer learn about the properties of three-dimensional space from examples.

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One of the most defining aspects of this century is the mass of data that is available everywhere. Using this data competently and analysing it is one of the many things that deep learning (DL) does. From fighting cancer by studying its pathology and patterns to warding off asteroid attacks, deep learning has put the world on a whole different trajectory. In deep learning the computer solves a problem independently by going through multiple levels of learning, which makes a lot of simple tasks much more efficient and accurate. This intricate level of computing requires GPU-accelerated computing to speed up tasks such as image, handwriting and voice identification.