Missing data is quite common when dealing with real world datasets. There are several ways to improve prediction accuracy when missing data in some predictors without completely discarding the entire observation. This example shows how decision trees with surrogate splits can be used to improve prediction accuracy in the presence of missing data. Bagging (bootstrap aggregating), is an ensemble approach which involves training several weak learners to create a strong classifier. Decreasing value with number of trees indicates good performance.

OpenAI, Elon Musk's artificial intelligence research company, just became the proud owner of the first ever DGX-1 supercomputer. Made by NVIDIA, the rig boasts a whopping 170 teraflops of computing power, equivalent to 250 usual servers -- and OpenAI is gonna use it all to read Reddit comments. OpenAI's researchers gather around the first AI supercomputer in a box, NVIDIA DGX-1. OpenAI is a non-profit AI research company whose purpose is to "advance digital intelligence in the way that is most likely to benefit humanity as a whole, unconstrained by a need to generate financial return." And now, NVIDIA CEO CEO Jen-Hsun Huang just delivered the most powerful tool the company has ever had at its disposal, a US 2 billion supercomputer.

Scientists in Japan reportedly saved a woman's life by applying artificial intelligence to help them diagnose a rare form of cancer. Faced with a 60-year-old woman whose cancer diagnosis was unresponsive to treatment, they supplied an AI system with huge amounts of clinical cancer case data, and it diagnosed the rare leukemia that had stumped the clinicians in just ten minutes. The Watson AI system from IBM matched the patient's symptoms against 20m clinical oncology studies uploaded by a team headed by Arinobu Tojo at the University of Tokyo's Institute of Medical Science that included symptoms, treatment and response. The Memorial Sloan Kettering Cancer Center in New York has carried out similar work, where teams of clinicians and data analysts trained Watson's machine learning capabilities with oncological data in order to focus its predictive and analytic capabilities on diagnosing cancers. IBM Watson first became famous when it won the US television game show Jeopardy in 2011.

In 2011, a supercomputer won 1 million on Jeopardy! In 2016, that same supercomputer is tackling a challenge quantified not in millions of dollars but in millions of cancer patients. The goal is to use Watson's natural language processing to mine the medical literature and a patient's records to provide treatment advice. And this month the Watson computer system is drastically expanding its reach -- from one hospital in Thailand to six in India and a planned 21 more in China. This instantiation of Watson, dubbed Watson for Oncology, is an artificial intelligence system that has access to millions of pages of medical textbooks and journal articles.

The year was 2012, and IBM's AI software Watson was in the midst of its heyday. Watson beat two of Jeopardy's all-time champions a year earlier in 2011, and the world was stunned. It was the first widespread and successful demonstration of a natural language processing computer of its class. Combined with the popularity of Jeopardy, Watson became an immediate mainstream icon. Later in 2012, IBM announced one of the first major practical partnerships for Watson--a Cleveland Clinic collaboration to bring the system into medical training.

Snyder expects that machine learning will be able to complement the fields of precision medicine cancer genomics, transcriptomics and proteomics. "We launched this study because we wanted to begin marrying imaging to our'omics' studies to better understand cancer processes at a molecular level," he said. "This brings cancer pathology into the 21st century and has the potential to be an awesome thing for patients and their clinicians.

Uber will test its fleet of autonomous cars in Pittsburg. Some guy made a bionic hand out of a coffee machine. This article focuses not what the athletes are putting into their bodies, but what they are putting on their bodies and shows how technology affects gears used by them. An hour long lecture by Demis Hassabis, the CEO of DeepMind, where he discusses what is happening at the cutting edge of AI research, including the recent historic AlphaGo match, and its future potential impact on fields such as science and healthcare, and how developing AI may help us better understand the human mind. Here, Margaret Boden, a Professor of cognitive science at the University of Sussex, examines what it means to be "creative" and whether we can ever translate this into our computers.

What is A.I. or artificial intelligence but the'space of possible minds', argues Murray Shanahan, scientific advisor on the movie Ex Machina and Professor of Cognitive Robotics at Imperial College London. But where are we now in the A.I. evolution? What players do we think will lead, if not win, the current race? And how should we think about issues such as ethics and automation of jobs without descending into obvious extremes? All this and more, including a surprise easter egg in Ex Machina shared by Shanahan, whose work influenced the movie.

There are few bigger challenges than trying to solve world poverty. While there are plenty of initiatives going on in this area, one of the most intriguing is being carried out by researchers at Stanford University. Using a combination of satellite data and machine learning, they've developed a "poverty map" of Africa that could help direct aid to some of the world's most deprived areas. "One part of the problem when it comes to dealing with poverty is that we don't have very good data," Neal Jean, a Ph.D student in Machine Learning at Stanford, told Digital Trends. "If we want to help people, but we don't know exactly where they are, that makes it very difficult to do. Traditionally, the way data is collected on poverty is by going out into the field and having people conduct surveys. Our objective in doing this project was to come up with a cost-effective and scalable way of filling in some of these data gaps."

There was news recently in bloomberg about how google was able to cut electricity usage in its datacenter by using an AI scheme made by DeepMind (of AlphaGo fame). Earlier this week, i decided to make a quick-and-dirty implemetation in python and share it here for anyone interested in a practical example of what exactly they did. First lets take a quick look at why one would want to make such a thing... Datacenters (and indeed any other large scale structures that use a lot of energy) need to be carefully optimized for efficiency as even a 10% - 15% saving on the electricity bill can add up to millions of dollars a year. The biggest challenge here is that even though there are certain simple steps that anyone can take to reduce energy use (don't use a very low server room set-point, use free-cooling when possible, etc…) one can never actually predict quantitatively what the effect of changing variable x by z% will have on total consumption. This is because there simply are too many variables that affect the net consumption of a datacenter (chillers, AHUs, compressors, condensers, fans, outside conditions, latitude, etc…) and its impossible to actually write down a formula that can quantify all these relationships. However, as long as you have a lot of data, ML is perfect for learning complex relationships between multiple features and outcomes.