With all this talk about chatbots from Facebook and Microsoft, teaching artificial intelligence to be smarter has become a central topic of the tech world. But what about what AI can teach us? Ashok Goel, a computer science professor at Georgia Tech, put that question to the test when he added "Jill Watson" – a chatbot powered by IBM's Watson technology – to his list of of teaching assistants for an online course. The chatbot was so good at answering questions that students did not notice their TA was made of silicon until after they'd turned in their finals. Some of the biggest names in tech are coming to TNW Conference in Amsterdam this May.

You may remember a little while back it was revealed that Google has been feeding its neural networks steamy romance novels to read. The aim through this exercise was to teach it to produce more human-like responses in order to power its search results and'smart reply' systems. As well as forcing its neural networks to digest more than 11,000 unpublished books (3,000 of which were romance), Google Brain's engineers have also been teaching it to relate two unique phrases to each other. As revealed in a Quartz article, the method was fairly straightforward and resulted in some really weird, romantic, dark'poetry'. That is just one of many examples churned up by Google's AI machine.

Of course, if you are an experienced Python programmer you will be able to skip this step. Even if so, I suggest keeping the very readable Python documentation handy. KDnuggets' own Zachary Lipton has pointed out that there is a lot of variation in what people consider a "data scientist." This actually is a reflection of the field of machine learning, since much of what data scientists do involves using machine learning algorithms to varying degrees. Is itnecessary to intimately understand kernel methods in order to efficiently create and gain insight from a support vector machine model?

He goes on, "(3) You should be comfortable with failure. A lot of your models and experiments will fail. The best people are the ones who are genuinely curious about the world around them and channel that curiosity when working on machine learning. You should be good at identifying patterns in the data. Being able to create quick data visualizations (using R, Python, Matlab or Excel etc.) helps. You should to be able to establish metrics that define success or failure of your system. You should feel comfortable with blind experiments [1] and terms like precision, recall, accuracy, ROC, conversion rates, NDCG etc."

Last week, I wrote an introductory article on the package data.table. It was intended to provide you a head start and become familiar with its unique and short syntax. The next obvious step is to focus on modeling, which we will do in this post today. Atleast, I used to think of myself as a crippled R user when faced with large data sets. I would like to thank Matt Dowle again for this accomplishment. Algorithms like random forest (ntrees 1000) takes forever to run on my data set with 800,000 rows. I'm sure there are many R users who are trapped in a similar situation. To overcome this painstaking hurdle, I decided to write this post which demonstrates using the two most powerful packages i.e. For practical understanding, I've taken the data set from a previously held competition and tried to improve the score using 4 different machine learning algorithms (with H2O) & feature engineering (with data.table).

A detailed guide to setting up your machine for deep learning research. Includes instructions to install drivers, tools and various deep learning frameworks. This was tested on a 64 bit machine with Nvidia Titan X, running Ubuntu 14.04 There are several great guides with a similar goal. Some are limited in scope, while others are not up to date.

We're working hard, having fun, making history; come join us! As a member of the Digital Products Machine Learning Platform team, you will be responsible for leading the development and launch of core platform features. You will have significant influence on our overall strategy by helping define these features, drive the system architecture, and spearhead the best practices that enable a quality product. The ideal candidate is clearly passionate about new opportunities and has a demonstrable track record of success in delivering new features and products. A commitment to team work, hustle, and strong communication skills (to both business and technical partners) are absolute requirements.

If you want to tackle competition and be a leader in your own right you need inevitably to gain competitive advantages. Otherwise, you are bound to be just one more in the bunch of those left behind. So the question is: how to become a leader and, if possible, an awesome leader? By this, I mean the kind of front-runner that sees what the others don't; the kind of strategist that anticipates his competitor's actions; the resilient believer that doesn't hesitate and goes staunchly the extra mile before the others. Altogether, the driving force that irradiates a huge resolve that convinces the others around that there is an upcoming light at the end of the tunnel.

Artificial Intelligence has been assuming significance beyond academic debates in the past couple of years. Google and Facebook have claimed that they now have face recognition systems based on Artificial Intelligence that can beat humans at the task. There are reports that many of the text chats are now manned by Artificial Intelligence systems without the user's knowledge, thus surpassing the Turing test criterion. Proponents of Artificial Intelligence like Ray Kurzweil has been predicting that within the next 30 years, AI will enable immortality through a concept known as Singularity, where we will be able to upload our brain on to a cloud and then onwards, our thoughts live on forever. On the other end of the spectrum, people like Stephen Hawking predict Artificial Intelligence could spell the end of human civilization with computer systems eventually overpowering humans.

Artificial Intelligence (AI) is bettering the world in myriad ways, and its next task is to revolutionize healthcare. According to Dr Robert Wachter, MD – chair of the Department of Medicine at the University of San Francisco – radiology, dermatology and pathology will soon be upended by the technology. In his latest book, The Digital Doctor: Hope, Hype, and Harm at the Dawn of Medicine's Computer Age, Dr Wachter outlines his case for why AI and other technologies are joining forces to create a "digital tsunami". In many ways, 2016 will mark the tipping point, as improvements in underlying technologies that power AI have helped more than three dozen startups to expand their presence in the market. Putting specific technologies aside for a moment, the biggest trend to watch may be how advances are dramatically altering the healthcare landscape.