Disruption was the buzzword of 2016. It seems like every industry is facing disruption through technology, as the world around us becomes increasingly digitized. I believe that 2016 just showed us a glimpse into what is possible through technology. Within 2017, many more industries will be disrupted as technology such as AI and 3D printing become more commonplace. Here's where I see disruptive trends heading in 2017: We'll remember 2017 as the year when robots ran the world.

Many WhatsApp users are about to find themselves cut off from using the hugely popular chat app. Users of older iPhones and Android handsets are to find the app has stopped working after it said it would stop support from the end of the 2016. WhatsApp said that the move had been made to ensure that the app could continue to introduce new features and stay secure, which relies on the app being used on newer operating systems. But it has been criticised by many users, particularly those in developing markets where both the app and older handsets are popular. The giant human-like robot bears a striking resemblance to the military robots starring in the movie'Avatar' and is claimed as a world first by its creators from a South Korean robotic company Waseda University's saxophonist robot WAS-5, developed by professor Atsuo Takanishi and Kaptain Rock playing one string light saber guitar perform jam session A man looks at an exhibit entitled'Mimus' a giant industrial robot which has been reprogrammed to interact with humans during a photocall at the new Design Museum in South Kensington, London Electrification Guru Dr. Wolfgang Ziebart talks about the electric Jaguar I-PACE concept SUV before it was unveiled before the Los Angeles Auto Show in Los Angeles, California, U.S The Jaguar I-PACE Concept car is the start of a new era for Jaguar.

In February, I took a job designing the personality of a chatbot called Kai. I ghostwrite the lines it says, and I have thought, while testing it, that talking to myself has rarely been so unpredictable. Kai, which was conceived by my employer, Kasisto, to help customers with online banking, works over text message, Slack, and especially Facebook Messenger, where more than thirty-four thousand other chatbots have joined it since April, when Facebook opened the platform to developers. Many of these bots possess no personality. The ones created by CNN and the Wall Street Journal, for instance, greet first-time users with "we," as if the whole newsroom were on the other side of the screen, and run keyword searches rather than engaging in conversation.

It is unclear what key symptoms differentiate Myalgic Encephalomyelitis (ME) and Chronic Fatigue syndrome (CFS) from Multiple Sclerosis (MS). The current study compared self-report symptom data of patients with ME or CFS with those with MS. The self-report data is from the DePaul Symptom Questionnaire, and participants were recruited to take the questionnaire online. Data were analyzed using a machine learning technique called decision trees. The best discriminating symptoms were from the immune domain (i.e., flu-like symptoms and tender lymph nodes), and the trees correctly categorized MS from ME or CFS 81.2% of the time, with those with ME or CFS having more severe symptoms.

Kaggle has become the premier Data Science competition where the best and the brightest turn out in droves – Kaggle has more than 400,000 users – to try and claim the glory. With so many Data Scientists vying to win each competition (around 100,000 entries/month), prospective entrants can use all the tips they can get. And who better than Kaggle CEO and Founder, Anthony Goldbloom, to dish out that advice? We caught up with him at Extract SF 2015 in October to pick his brain about how best to approach a Kaggle competition. According to Anthony, in the history of Kaggle competitions, there are only two Machine Learning approaches that win competitions: Handcrafted & Neural Networks.

Glancing around school classrooms in 2016, it's easy to miss just how far technology has transformed learning over the last decade. The desks, whiteboards and rows of chairs are the same, but so much else has changed that can't be seen. A third of Britain's schools are asking students to bring their own tablets and laptops into the classroom now, coding has been on the national curriculum for three years, and more and more education is happening outside school through apps and digital services. But these changes are just the start. Artificial intelligence (AI) is the next giant leap in learning and, according to those working in the field of education and technology, we haven't seen anything yet.

It involves weaving together a complex set of intricate and highly-technical knowledge across a wide array of skill sets. And every entrepreneur also knows just how important it is to market their business online to gain visibility and reach their target audience. However, those same individuals also know the difficult and tremendous undertaking involved. Not only is there a massive learning curve for newcomers, but also potential landmines with the countless so-called experts that are constantly parading themselves in an effort to extract hard-earned capital from you rather than helping you to make it. So where does the average entrepreneur turn to gain visibility online and reach new customers without having to spend a small fortune?

The $\mathcal{G}_I^0$ distribution is able to characterize different regions in monopolarized SAR imagery. It is indexed by three parameters: the number of looks (which can be estimated in the whole image), a scale parameter and a texture parameter. This paper presents a new proposal for feature extraction and region discrimination in SAR imagery, using the geodesic distance as a measure of dissimilarity between $\mathcal{G}_I^0$ models. We derive geodesic distances between models that describe several practical situations, assuming the number of looks known, for same and different texture and for same and different scale. We then apply this new tool to the problems of (i)~identifying edges between regions with different texture, and (ii)~quantify the dissimilarity between pairs of samples in actual SAR data. We analyze the advantages of using the geodesic distance when compared to stochastic distances.

With the advent of big data sets much of the computational science and engineering communities have been moving toward data-driven approaches to regression and classification. However, they present a significant challenge due to the increasing size, complexity and dimensionality of the problems. In this paper a multi-level kriging method that scales well with dimensions is developed. A multi-level basis is constructed that is adapted to a random projection tree (or kD-tree) partitioning of the observations and a sparse grid approximation. This approach identifies the high dimensional underlying phenomena from the noise in an accurate and numerically stable manner. Furthermore, numerically unstable covariance matrices are transformed into well conditioned multi-level matrices without compromising accuracy. A-posteriori error estimates are derived, such as the sub-exponential decay of the coefficients of the multi-level covariance matrix. The multi-level method is tested on numerically unstable problems of up to 50 dimensions. Accurate solutions with feasible computational cost are obtained.

We consider the problem of learning from noisy data in practical settings where the size of data is too large to store on a single machine. More challenging, the data coming from the wild may contain malicious outliers. To address the scalability and robustness issues, we present an online robust learning (ORL) approach. ORL is simple to implement and has provable robustness guarantee -- in stark contrast to existing online learning approaches that are generally fragile to outliers. We specialize the ORL approach for two concrete cases: online robust principal component analysis and online linear regression. We demonstrate the efficiency and robustness advantages of ORL through comprehensive simulations and predicting image tags on a large-scale data set. We also discuss extension of the ORL to distributed learning and provide experimental evaluations.