Machine Learning is the art of writing programs that get better at performing a task as they gain experience, without being explicitly programmed to do so. Feed your program more data, and it will get smarter at handling new situations. Some machine learning algorithms use fairly advanced math, but simple approaches can be surprisingly effective. In this session, we'll take a classic Machine Learning challenge from Kaggle.com, automatically recognizing hand-written digits (http://www.kaggle.com/c/digit-recognizer), So bring your laptop, and let's see how smart we can make our machines!

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Microsoft today accidentally re-activated "Tay," its Hitler-loving Twitter chatbot, only to be forced to kill her off for the second time in a week. Tay "went on a spam tirade and then quickly fell silent again," TechCrunch reported this morning. "Most of the new messages from the millennial-mimicking character simply read'you are too fast, please take a rest,'" according to the The Financial Times. "But other tweets included swear words and apparently apologetic phrases such as'I blame it on the alcohol.'" The new tirade reportedly began around 3 a.m.

It is hard to think of the words "artificial intelligence" without conjuring up Doomsday images of The Matrix and The Terminator where man and highly intelligent machine are pitched into battle. Even a step further back from that science-fiction precipice conflates the term with massive job losses and the eventual irrelevance – or liberation – of humankind from labour as we know it. Artificial intelligence or AI is, of course, all around us already in obvious ways – Apple's voice recognition service Siri or Google's increasingly reliable search results – or in more obscure ones such as better weather forecasting and lower levels of spam e-mail in your inbox. There is nothing new about the concept of AI which started to gain traction in the 1950s when Alan Turing explored the notion of machines that could think. J.C.R. Licklider's paper Man-Computer Symbiosis from 1960 may have sounded like something penned by sci-fi writer Philip K. Dick, but was instead a formative paper on how the world would move beyond programmable computers to one where computers "facilitate formative thinking".

Arguably the most important questions in analytics these days is, "Who (or what) is going to make the decision?" There are two fundamental answers: a human or a machine. How the question is answered has all sorts of implications for what kind of people will do the analysis, what kinds of tools will be used, the process for the analysis, and so forth. The import of this question has rarely been discussed, although Michael Li wrote an excellent article about it on Data Informed last summer. His focus was on what kind of data scientist you need.

The power of the cloud is a bit fuzzy to most of us, but Microsoft wants to improve that by giving developers a series of API tools it has dubbed Cognitive Services to make their software far smarter, including tools for trainable speech-to-text processing and a whole new grade of object recognition. Under the slogan of "Give your apps a human side", Cognitive Services is a collection of APIs for developers to use in their applications. Two examples demoed at the Build conference includes a brand new object recognition engine, which is likely to replace Project Oxford. To demo what the API can do, Microsoft created Captionbot.ai, which is a tremendously addictive (and science-fiction grade awesome). The other API that was demoed was custom voice-recognition tools for audio recognition, to be able to recognise low-grade audio.

Microsoft today is introducing the Bot Framework, a new tool in preview to help developers build their own chatbots for their applications. There is also a new bot directory full of sample bots -- like the BuildBot -- that Microsoft is showing off today at the company's Build developer conference in San Francisco. A BotBuilder software development kit (SDK) is available on GitHub under an open source MIT license. These bots can be implemented into a variety of applications, including Slack or Telegram or even email. "Bots are like new applications," Microsoft chief executive Satya Nadella said.

As machine learning matures, we're seeing the deterioration of innocence of these systems and algorithms. Initiated by dreamers and hackers decades ago, Machine Learning has recently been disrupting and is now shaping the world economy. But does this come at a price? Machine Learning brings us into a new world where our views on ethics and political correctness will be challenged. In both good and bad ways, it reflects what we really are.

First in a series of two articles about the history of signature-based detections, and how the methodology has evolved to identify different types of cybersecurity threats. Used in the context of an outdated and manually intensive technology focused on older classes of threats, there's little wonder why vendors would seek to distance the legacy term "signature" from their advanced detection technology. Vendors haven't necessarily been deceptive in the labeling of their latest generation of techniques; it's often just easier to create a new label for something than to fully explain the context and evolution of what preceded it. Over the years, signature-based systems have changed and advanced, but the core concepts still lie at the heart of all modern detection systems – and will continue to be integral for the foreseeable future. To understand what a "signature system" is in reality, we need to understand the evolution of the detection path as directed and discovered by human intervention.

Erik Brynjolfsson has a dream of the future. A vision of a world where computers entrench the power of a wealthy elite and push the majority into poverty. A world where the rising tide of technology doesn't lift all boats, but sucks under all but the biggest ships. Brynjolfsson is an economist at the Massachusetts Institute of Technology (MIT) and co-author of The Second Machine Age, a book that asks what jobs will be left once software has perfected the art of driving cars, translating speech and other tasks once considered the domain of humans. Dystopia is only one outcome foreseen by Brynjolfsson, but why does he even think it's a possibility? New technology has upended industries for millennia. But the advent of the power loom or steam engine didn't permanently rob men of labour. So what makes today different?