A team from Columbia University and Princeton University have developed an algorithm to accurately analyse genetic ancestry across'tera' sized datasets – a potentially significant development in the development of personalised healthcare. Since the completion of the Human Genome Project, and the savings in both time and money that next generation sequencing enables, genetic datasets have grown exponentially whilst analysis has fought to keep pace. Now, a team of researchers have developed a machine learning algorithm they call TeraStructure, capable of analysing very large data sets. Machine learning is a computer analysis method which allows an artificial intelligence to literally teach itself, using statistical principles and the growing capability of computers to process data. Tech giants such as Google, Microsoft and Apple all have their own programs, but promising applications in medical science are still relatively few.

Machine learning couldn't be hotter, with several heavy hitters offering platforms aimed at seasoned data scientists and newcomers interested in working with neural networks. Among the more popular options is TensorFlow, a machine learning library that Google open-sourced a year ago. In my recent review of TensorFlow, I described the library and discussed its advantages, but only had about 300 words to devote to how to begin using Google's "secret sauce" for machine learning. That isn't enough to get you started. In this article, I'll give you a very quick gloss on machine learning, introduce you to the basics of TensorFlow, and walk you through a few TensorFlow models in the area of image classification.

When I go to Google Photos and search my photos for'skyline', it finds me this picture of the New York skyline I took in August, without me having labelled it! When I search for'cathedral', Google's neural networks find me pictures of cathedrals & churches I've seen. But of course, neural networks aren't magic–nothing is! I recently read a paper, "Explaining and Harnessing Adversarial Examples", that helped demystify neural networks a little for me. The paper explains how to force a neural network to make really egregious mistakes. It does this by exploiting the fact that the network is simpler (more linear!) than you might expect. It's important to understand that this doesn't explain all (or even most) kinds of mistakes neural networks make. There are a lot of possible mistakes!

The next frontier for artificial intelligence may involve teaching computers to distinguish albacore tuna from its yellowfin cousin. The Nature Conservancy, an environmental non-profit, is working with several Pacific Island nations and a big tuna fishing company to more easily count and identify fish caught at sea using cutting edge technology. The goal is to use trendy artificial intelligence techniques like deep learning to help fishermen reduce the number of protected animals like sharks and turtles that are accidentally caught along with the tuna. The Nature Conservancy hopes that the program could prevent overfishing and help threatened and endangered sea life recover without putting fishermen out of work. "We have real optimism that data science community can help us differentiate a turtle from a tuna, and flag when a shark comes on board," said Mark Zimring, a project director for The Nature Conservancy.

However, many businesses may ask; are Chat Bots an innovation and investment too far? What should be the strategy for building a successful Bot? How to make the Bot human-like? A human-like Bot will require the use of text analytics, artificial intelligence and sentimental analysis in varying degrees. The study answers these questions and highlights a way to classify and explain the range of available Bot solutions.

Artificial Intelligence (AI) is one of the most exciting and controversial developments in science and technology. While the recognised applications of AI are limited, think Google and Siri, many believe it is poised to make significant contributions to organizations as it is used to advance and accelerate decision-making. Can we apply cognitive technology to amplify our intelligence and improve the effectiveness and efficiency of medicine? To enhance the patient experience and improve engagement? To improve diagnostic accuracy and personalize treatment with remarkable precision?

AI technologies have clear value paths in marketing, including sales and enhancing customer experience. When most companies think about artificial intelligence (AI)--and its many sub-categories, such as machine learning, natural language processing (NLP), or cognitive computing--they think about applications such as IBM's Watson technology being used to diagnose patients. What they often miss, according to Fern Halper, VP and senior research director of advanced analytics at TDWI, is that "marketing is often one of the first departments in an organization to use advanced analytics." In a recent webinar, Halper discussed the current potential and future pathways for marketing analytics with David Stodder, senior research director of business intelligence at TDWI, and Wilson Raj, the global director of customer intelligence with SAS. They all agreed that among all the different business departments, applying AI to marketing could provide the most immediate, recognizable return on investment .

If you're into enterprise, cloud computing -- and data, analytics, machine learning, and conversational interfaces -- this article is for you. It's my report from the 2016 World of Watson conference, written for both those who missed the event and those who attended. IBM stated 17,000 Las Vegas attendance and there was a lot to absorb! At World of Watson, I saw computing's future… and also technologies that I associate with a pre-cognitive past. I encountered a tech giant with vision, amazing alliances, and unique computing assets… and I saw lingering limitations I wouldn't have expected. I'll open this report with three key IBM messaging points, followed by three observations about products and positioning. I'll close with four points where, in my view, IBM hasn't yet hit the mark.

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A collection of minimal and clean implementations of machine learning algorithms. This project is targeting people who wants to learn internals of ml algorithms or implement them from scratch. The code is much easier to follow than the optimized libraries and easier to play with. All algorithms are implemented in Python, using numpy, scipy and autograd.