They are currently in the NYC Data Science Academy 12 week full time Data Science Bootcamp program taking place between January 11th to April 1st, 2016. This post is based on their fourth class project - Machine learning(due on the 8th week of the program). The Higgs Boson is a landmark discovery that will help us to understand the basic nature of the universe. It was discovered first by the ATLAS experiment at the Large Hadron Collider, CERN in 2012. The Higg's Boson decays into two tau particles giving rise to a small signal buried in background noise. The goal of the Higgs Boson Machine Learning Challenge was to classify the characterizing events detected by ATLAS into "tau tau decay of a Higgs boson" versus "background."

When Google unveiled its Tensor Processing Unit (TPU) during this year's Google I/O conference in Mountain View, California, it finally ticked for this editor in particular that machine learning is the future of computing hardware. Of course, the TPU is only a part of the firm's mission to push machine learning – the practice that powers chat bots, Siri and the like – forward. Google also has TensorFlow, its open source library of machine intelligence software. And sure, the chips that we find in our laptops and smartphones will continue to get faster and more versatile. But, it seems as if we've already seen the extent of the computing experiences that these processors can provide, if only limited by the devices they power.

I will discuss the need for a fundamental reorientation of the field of AI towards provably beneficial systems. This need has been disputed by some, and I will consider their arguments. I will also discuss the technical challenges involved and some promising initial results.

Microsoft has acquired Genee, a startup that provided a virtual assistant for scheduling appointments. The deal, announced Tuesday, will bring the Genee team into the Microsoft fold and put them to work on bringing intelligence into Office 365. Genee's service lets users loop in an artificial intelligence assistant that could interpret sentences like "set up lunch at some point this week" and turn them into calendar appointments. Acquiring Genee is part of Microsoft's ongoing crusade to build intelligent productivity software and services using AI. The company has focused on that area under the tenure of CEO Satya Nadella, with features like Office Delve, which finds documents shared within an organization that are relevant to a user, and Focused Inbox in Outlook, which filters unimportant email into a separate tab so it's not cluttering a user's experience. The Genee service will be shutting down on Sept. 1 as part of the deal.

Bots are officially everywhere -- they're available to assist with almost every part of our lives, from shopping and gift-giving to news-tracking and political decision-making. They can even tell us what to make for dinner. The thing that seems odd to me, though, is that the entire bot conversation has been focused on A.I., machine learning (ML), and natural language processing (NLP). This makes me question whether we're looking at bots the wrong way. Having been in the business of building software for quite some time, and learned about workflows, business logic and other riveting concepts, I can't help but feel there's a misunderstanding about what constitutes a bot and what is just an access layer to the bot.

This article is the first of a series dedicated to amazing ChatScript, an open-source natural language processing and dialog flow scripting platform. First of all I explain some general concepts and platform architectures. I introduce the beginner chatbots authors to practice with ChatScript programming (in this article, just starting with standalone platform installation on a personal computer). I precede this sort of installation guide for dummies, with an overview on software platforms architectures for conversational engines. The reader with a basic programming skill will be able to install and run the engine on his personal computer enjoying to chatbots scripts development!

This book is for anyone who intends to work with large and complex data sets. Familiarity with basic Python and machine learning concepts is recommended. Working knowledge in statistics and computational mathematics would also be helpful. Large Python machine learning projects involve new problems associated with specialized machine learning architectures and designs that many data scientists have yet to tackle. But finding algorithms and designing and building platforms that deal with large sets of data is a growing need.

You can get a pretty good idea of a country's wealth by seeing how much it shines at night – just compare the intense brightness of China and South Korea to the dark mass of North Korea that's sandwiched between them. But nighttime lights don't tell you which neighborhoods or villages within a large region are merely poor and which are home to people living in abject poverty. That's the level of detail policymakers need when they decide where to deploy their economic development programs. You could get that detail by sending legions of survey-takers into crowded slums and sparsely populated rural areas. But that would be hugely time-consuming and cost tens of millions of dollars or more.

Feels like a home away from home. Microsoft acquires AI-powered scheduling service Genee, to be shut down and merged with Office ... 'Software is eating the world': How robots, drones and artificial intelligence will change everything

Microsoft acquires AI-powered scheduling service Genee, to be shut down and merged with Office ... 'Software is eating the world': How robots, drones and artificial intelligence will change everything