Do you know what is common between San Bernardino's shooting spree and the terrorist attacks in Paris last month? Jillennials, Jihadis who are Millennials. We mine data worldwide, a lot of it, a ton of it, every day and every night, and we do this for a living at PredictifyMe. We have partnership with the United Nations to protect school-goers in Pakistan, Nigeria, Sudan and Lebanon using our proprietary software SecureSim and Soothsayer . When the Paris attacks unfolded, we asked ourselves (and our database), how can we use data sciences to prevent something like this from ever happening again. Can we find out what factors influence an otherwise ordinary citizen to become radicalized?

In late December 2016, I announced a small gift of 10 Deep Learning books to people interested in or working in AI. This is my way of paying back to the community which has been extremely generous with ideas and code. I asked people to send me an email letting me know their interest in AI and their contributions to the community. Here is the video of the announcement. I received nearly 300 emails from people from around the world -- pretty much every continent other than Antarctica!

Huawei has made a name for itself by partnering with Google on the final Nexus phone and selling affordable, feature-packed handsets in China, but now the company is looking to stand on its own in the U.S. Not only is it finally launching its 6.9-inch Mate 9 phablet to the States, it's bringing along a new friend along for the trip: Alexa. Among the features of the Mate 9--aside from its giant 1080p display--are a 4,000 mAh battery, 5A fast charging through its SuperCharge technology, and a Leica-made dual-lens camera that combines a 12MP standard camera with a 20MP monochrome one to capture more depth and detail. Also on board is Huawei's Hybrid Zoom system that delivers 2x magnification, and a 4-in-1 hybrid auto-focus that combines "laser focus, phase detection focus, depth focus, and contrast focus." The Mate 9 is powered by Huawei's in-house Kirin 960 chipset, which features an ARM Cortex-A73/A53 Octa-core CPU and Mali G71 Octa-core GPU. The company boasts that the chip utilizes a "Machine Learning Algorithm that helps deliver a fast and smooth experience, even after 18 months of continuous use."

One key concern raised in the video is the notion of uncontrolled development, "as impoverishment, slums and shantytowns rapidly expand alongside modern high-rises". Dharavi in Maharashtra India, Neza-Chalco-Itza in Mexico City and Kibera in Nairobi are prominent examples of such developments. However such massive developments do not happen overnight and Ilievski suggests that machine learning models can "capture the patterns of urban change driven by a diverse set of context factors" like economic growth, demographics changes, proximity, and neighborhood conditions. Ilievski demonstrates that machine learning can accurately predict how land use evolves over time across categories like undeveloped, main street, peripheral street, industry, house, education, park, etc.

This is the 2-part blog version of a talk I've given at DOAG Conference this week. I've also uploaded the slides (no ppt; just pretty R presentation;-)) to the articles section, but if you'd like a little text I'm encouraging you to read on. That is, if you're in the target group for this post/talk. For this post, let me assume you're a SQL girl (or guy). With SQL you're comfortable (an expert, probably), you know how to get and manipulate your data, no nesting of subselects has you scared;-).

When the general public first learned about drones, the context was largely related to military applications and spy tactics, so the unmanned aircraft were cast with a bad rap. Years later, a number of creative technologists have found ways to use drones to help people, animals, and the environment. Drones are being used to protect endangered animals from poachers in Africa, to beam internet access to parts of the world never before served, and even as remote flying builders. Drones are helping humans work on projects - from the curious to the comical - that wouldn't have been possible without the small autonomous aircraft. Read on to learn about some of the most amazing ways drones are helping people and the planet.

The following is the text of a talk I gave in San Francisco on December 1st, 2016. The audience was readers of my newsletter, Exponential View. You can sign up here. This is a long (7,500 word) transcript of the talk. You can scan it to see the slides and accompanying exhibits if that is easier. Or even read it in more than one sitting…. Exponential View has a purpose. In between all the emojis and all the spelling mistakes, this is what it's about: This is me on my first day at school back when I was in Zambia in sub-Saharan Africa. On the right is my friend Rehan, who I reconnected recently through Facebook. He is now known as Dr. Freeze and he does non-invasive body sculpting in Orange County. So I can get you a good rate. But I think it's important, this starting point is important. We often are inspired from where we come from and what the hell was I doing in Zambia? My dad was trained as economist and accountant, well he is retired now, but then he was an economist and was down in Zambia building the kind of institutions that we take for granted in countries like the U.S. and the U.K. to make the country function. Zambia had just got independence from the U.K. It needed a deeper civil service, it was having to build its legal system, create its system of distribution and so on. So I got an early exposure to the importance of economic institutions for making societies wealthier and making them work. While I was down in Zambia, which is a land-locked country and doesn't have great access to the sea and this is the 1970s, so we didn't have a vast range of toys.

Missing records are a perennial problem in analysis of complex data of all types, when the target of inference is some function of the full data law. In simple cases, where data is missing at random or completely at random (Rubin, 1976), well-known adjustments exist that result in consistent estimators of target quantities. Assumptions underlying these estimators are generally not realistic in practical missing data problems. Unfortunately, consistent estimators in more complex cases where data is missing not at random, and where no ordering on variables induces monotonicity of missingness status are not known in general, with some notable exceptions (Robins, 1997), (Tchetgen Tchetgen et al, 2016), (Sadinle and Reiter, 2016). In this paper, we propose a general class of consistent estimators for cases where data is missing not at random, and missingness status is non-monotonic. Our estimators, which are generalized inverse probability weighting estimators, make no assumptions on the underlying full data law, but instead place independence restrictions, and certain other fairly mild assumptions, on the distribution of missingness status conditional on the data. The assumptions we place on the distribution of missingness status conditional on the data can be viewed as a version of a conditional Markov random field (MRF) corresponding to a chain graph. Assumptions embedded in our model permit identification from the observed data law, and admit a natural fitting procedure based on the pseudo likelihood approach of (Besag, 1975). We illustrate our approach with a simple simulation study, and an analysis of risk of premature birth in women in Botswana exposed to highly active anti-retroviral therapy.

LONDON – Drones delivering blood and medicine to rural areas of Tanzania could help to save the lives of many mothers and newborn babies in a country where one of the biggest causes of maternal deaths is blood loss during childbirth, the U.K. aid department said. The Department for International Development (DFID), which has given funding for the trial due to start early next year, said the drone deliveries could assist more than 50,000 births a year in the East African country. The drones will be able to carry up to 1 kg (2 pounds) of medical supplies and reduce delivery times to 19 minutes from the 110 minutes it takes on average by vehicle. "The U.K. is at the forefront of investing in cutting-edge technology to tackle the global challenges of today such as disease pandemics, medical emergencies and disaster responses," said Priti Patel, U.K.'s international development secretary. "This innovative, modern approach ensures we are achieving the best results for the world's poorest people and delivering value for money for British taxpayers," she said in a statement Thursday.

Remark Media's image and video recognition technologies are based on Deep Learning, a type of algorithm-based machine learning that is used to model high-level abstractions in data. In developing its technologies, Remark Media leveraged its extensive KanKan data platform to train its AI with tens of millions of supervised and unsupervised samples, developing models that extract facial features and recognize objects in images or videos such as branded logos, animals, airplanes, license plates, etc., with high precision. The KanKan Data Intelligence Platform is a growing global platform of over 1.3 billion socially active user profiles, over 15 billion posts, more than 10 billion images, and over 50 billion comments and reviews. In addition to providing its technology to Alibaba Cloud, Remark Media will provide its turnkey AI platform as a service to companies or individuals requiring assistance with integrating the technology into their own models. Additional information on this partnership can be found on Alibaba Cloud's website.