Throughout its history, humanity strives to improve themselves and all that surrounds us. For the most part, comfort, simplification of routine tasks, to increase the speed of complex and labor-intensive processes by replacing human work with machine one. Indeed, with the universal proliferation of mass computerization and the Internet of things, our life becomes much easier. And probably saturated – both in terms of events and technological development. Remember what was 10-15 years ago.

This book is not an introductory text. Anyone interested in a particular topic should consult the preface of the text to find out what it is about. The negative reviewers were not fair to the authors on that score. Had they read the preface they would have found out (1) how the authors define data mining, (2) that they see it as a subject with an important mix of statistical methodology and computer science and (3) that it is intended as an advanced undergraduate or first year graduate text on the topic. They also provide a very well organized structure for the text that is well described in the preface.

With the release of CTP3 SQL Server 2016 and its native In-database support for the open source R language (SQL Server R Services), users can now call both R and RevoScaleR functions and scripts directly from within a SQL query and benefit from multi-threaded and multi-core in-DB computations. The R integration brings the utility of data science to your applications without the need to'export' the data to your R environment. Today, I will use the Adventure Works samples for SQL Server 2016 CTP3 to showcase how we can use SSIS to operationalize a R prediction from doing data preparation, to using the training data to build and save the "trained" model and running prediction using the trained model. In this specific example, we will use the IRIS flower dataset from Ronald Fisher that is built-in dataset from R as our data source and we will load this dataset into a SQL Server table called IRIS_RX_DATA. This will be our training data.

In the closing weeks of 2016, Google published an article which quietly sailed under most people's radar. Which is a shame, because the article may just be the most astonishing thing about machine learning that I read last year. Don't feel bad if you missed it. Not only was the article competing with the pre-Christmas rush most of us were navigating, it was also tucked away on Google's Research Blog beneath the geektastic headline Zero-Shot Translation with Google's Multilingual Neural Machine Translation System. It doesn't exactly scream must read, does it?

JERUSALEM/BEIJING, Jan. 24 (Xinhua) -- What will happen if Artificial Intelligence (AI) knows us better than we do about ourselves? Yuval Noah Harari, author of the international bestseller Sapiens: A Brief History of Humankind, shared his insight on how trends in science and technology may progress and influence human kind in a written interview with Xinhua. Harari is recently making quite a splash in China with the launch of the Chinese version of his equally compelling new book Homo Deus: A Brief History of Tomorrow, in which he turns his focus on humanity's future and the quest to upgrade humans, as science, especially AI, advance rapidly nowadays. According to Harari, people have already taken the first steps on the path of integration of humans and smart machines. People are already merging with their smartphones, and in the case of China, their Wechat accounts -- the intelligent devices and apps that constantly study us, adapt to our unique personality, and shape our worldview and innermost desires.

Machine learning was undoubtedly one of the hottest topics in radiology last year, with a steady stream of academic research papers highlighting how machine learning, particularly deep learning, can outperform traditional algorithms or manual processes in certain use-cases. Investment in machine learning start-ups also continued, with several companies attracting early stage funding. To date, more than $100m has been invested in start-ups that are developing AI solutions for radiology. Furthermore, commercial activity gained pace, with at least 20 companies exhibiting AI-based products at the RSNA conference towards the end of the year, although most were prototypes and only a handful had regulatory clearance. Whilst the enthusiasm for machine learning is certainly justified, it inevitably raises expectations, potentially to unrealistic levels.

The images above are examples of the three possible classes I needed to predict: no traffic light (left), red traffic light (center) and green traffic light (right). The challenge required the solution to be based on Convolutional Neural Networks, a very popular method used in image recognition with deep neural networks. The submissions were scored based on the model's accuracy along with the model's size (in megabytes). Smaller models got higher scores. In addition, the minimum accuracy required to win was 95%.

Maintaining data reliability is a resource-intensive uphill task for many organizations. Companies often spend too much effort on data reviews and cleanup, but seldom seem to catch up. Most of the time, teams don't even know what the issues are, how to look for them, and how to solve them. They just know that the data is dirty, and like a sitting on a ticking time bomb, we wait for the disaster to happen. The issues are often only illuminated when the data is put to operational use and trips up the end user or the customer with wrong information.

It seems pretty clear that 2017 is going to be the year that Amazon's Alexa virtual assistant really begins to hit its stride, going from a fast-growing niche product to a mainstream must-have. While Microsoft and especially Google have made their competitive strategies clear -- even Samsung and Baidu have started to make rumbles in the market -- there's one elephant that, notably, isn't in the room yet: Apple, the most valuable company in the world and a notorious latecomer to any new product category. While Apple recently built Siri into the Apple TV, the company is said to be working on a direct competitor to the Amazon Echo -- one that would apparently be more advanced than anything we've seen, down to a possible facial-recognition camera so it knows who's talking. That device, if and when it comes out, would bring Apple's 5-year-old Siri assistant head-to-head with Alexa. And the clock is ticking.

Many important real-world datasets come in the form of graphs or networks: social networks, knowledge graphs, protein-interaction networks, the World Wide Web, etc. (just to name a few). Yet, until recently, very little attention has been devoted to the generalization of neural network models to such structured datasets. In the last couple of years, a number of papers re-visited this problem of generalizing neural networks to work on arbitrarily structured graphs (Bruna et al., ICLR 2014; Henaff et al., 2015; Duvenaud et al., NIPS 2015; Li et al., ICLR 2016; Defferrard et al., NIPS 2016; Kipf & Welling, 2016), some of them now achieving very promising results in domains that have previously been dominated by, e.g., kernel-based methods, graph-based regularization techniques and others. In this post, I will give a brief overview of recent developments in this field and point out strengths and drawbacks of various approaches. I wrote a short comment on Ferenc's review here (at the very end of this post).