Jacob Katuva used to get up at dawn to cycle 12 miles from his village to collect water with his uncles and cousins when he was growing up in Kenya. Now he is part of a research team at the University of Oxford using cloud computing and mobile sensors to monitor water wells and help ensure that thousands of villages in rural Africa and Asia have a safe, secure supply of water. The time spent finding and carrying water, if local wells are not reliable, steals precious time from farming, making a living or going to school. It can even force people to revert to unsanitary water sources shared with animals. Water issues are tied to a cycle of poverty.

Machine learning is becoming increasingly pervasive in the modern data-driven world. It is used extensively in many fields such as search engines, robotics, self-driving cars, and more. Through this book, you will learn how to perform various machine learning tasks in a range of environments. If you want to develop machine learning applications or implement machine learning in existing systems, Python is an excellent language to do so. Machine learning with Python is currently the most used standard to perform machine learning and is a great alternative for developers because of its wide selection of libraries and developer-friendly ecosystem.

Google has announced a new machine learning platform for developers at its NEXT Google Cloud Platform user conference. Eric Schmidt, Google's chairman, explained that Google believes machine learning is "what's next." There are two parts to Google's Cloud Machine Learning platform. The first allows developers to build machine learning models based on their own data stored in tools such as Google Cloud Dataflow, Google BigQuery, Google Cloud Dataproc, Google Cloud Storage, and Google Cloud Datalab. The pre-trained models include existing APIs like the Google Translate API and Cloud Vision API, but also new services like the Google Cloud Speech API.

Amy Ingram, the artificial intelligence personal assistant from startup X.ai, sounds remarkably like a real person. The company designed her to take on the mundane tasks of scheduling meetings and e-mailing about appointments. If a bot had access to your calendar and was cc-ed on correspondence, why couldn't it do the work for you? After she made her debut in 2014, users praised her "humanlike tone" and "eloquent manners." But what most people don't realize about this artificial intelligence is that it isn't totally artificial: Behind almost every e-mail is an actual human--someone like 24-year-old Willie Calvin. Calvin, who worked as an AI trainer for X.ai before he said he quit in October, was part of the reason Amy never tripped up, sending the sort of blind response that reveals she's a bot.

Seems like Google's RankBrain AI is a Skynet in making because even the engineers working on it are unable to understand it. According to Paul Haahr, one of the company's top engineers working on the Google Search team said that Google's new RankBrain AI engine is actually more complex than thought before, and even some of Google's own staff is clueless how it is exactly working. The statement was made by Haahr at SMX West, a search marketing conference that was scheduled in San Jose, California between March 1 and 3. Google understands how RankBrain works but not really what it is doing. Haahr was responding to queries about Google's search products in general during the event's keynote, when someone questioned him about the company's latest addition, the RankBrain AI. The engineer's answer, as Barry Schwartz, SERoundtable reporter, and many other conference attendants confirmed on Twitter, was that many of Google's own engineers don't quite fully understand how the new RankBrain algorithm works. Google started working on RankBrain, an artificial intelligence system, during the past years under the supervision of top engineer John Giannandrea, an AI expert.

There was more about Foundations of Multidimensional and Metric Data Structures by Hanan Samet being too detailed, Stack Overflow being too high-level, and more hand-wringing after that, too. The email was pleading for some book or series of blog posts that would somehow educate data science folks on more fundamental issues of data structures and algorithms. Perhaps getting them to drop some dimensions when doing k-NN problems or perhaps exploit some other data structure that didn't involve 100's of columns. I'm guessing because -- like a lot of hand-waving emails -- it didn't involve code. If there is a lack of awareness of appropriate data structures, the real place to start is The Algorithm Design Manual by Steven Skiena.

In order to get a grasp on what makes optimization difficult in machine learning, it is important to specialize our focus. Nonsmooth optimization is so general, and what makes deep learning hard may be completely different from what makes tensor decomposition difficult. So in this post, I want to focus on deep learning and take a bit of a controversial stand. It has been my experience, that optimization is not at all what makes deep learning challenging. On the left I show the training error on everyone's favorite machine learning benchmark MNIST.

More than 20 private companies working to advance artificial intelligence technologies have been acquired in the last 3 years by corporate giants competing in the space, including Google, Amazon, Apple, IBM, Yahoo, Facebook, Intel, and, more recently, Salesforce. There have been 4 major acquisitions already in 2016.

Nathaniel Mott reports in The Guardian, "The battle for the future of computing is a battle to bring artificial intelligence to the mainstream – and Google is quietly overhauling a machine learning tool used to improve some of its most popular services including Google Translate and Google Photos. TensorFlow can be used to help teach computers how to process data in ways similar to how the human brain handles information. It is also open source, meaning Google has published and shared the code online so that external developers can use and improve it. The latest version, released by Google on Wednesday, adds a feature many TensorFlow users have asked for since the tool made its public debut in late 2015: the ability to operate on multiple devices."

Artificial intelligence is one of the greatest goals of the 21st century. Major developments in AI do astound, machines learning how to turn words into images and how to beat world class players in Go. A new microscope, developed by researchers from UCLA, uses AI in helping detect and spot blood samples with cancer cells. Faster and more accurate than its contemporary techniques, it can analyze 36 million images every second without damaging the blood samples. The new microscope features something called "photonic time stretch".