The purpose of this article is to demonstrate how the practical Data Scientist can implement a Locality Sensitive Hashing system from start to finish in order to drastically reduce the search time typically required in high dimensional spaces when finding similar items. Locality Sensitive Hashing accomplishes this efficiency by exponentially reducing the amount of data required for storage when collecting features for comparison between similar item sets. In other words, Locality Sensitive Hashing successfully reduces a high dimensional feature space while still retaining a random permutation of relevant features which research has shown can be used between data sets to determine an accurate approximation of Jaccard similarity [2,3]. The concept of Locality Sensitive Hashing has been around for some time now with publications dating back as far as 1999 [1] exploring its use for breaking the curse of dimensionality in nearest neighbor query problems. Since this time various applications of Locality Sensitive Hashing have been making appearances in academic publications all over the world.

Fiat Chrysler and Google will work together to more than double the size of Google's self-driving vehicle fleet by adding 100 Chrysler Pacifica minivans. The companies announced the agreement on Tuesday, saying that Chrysler engineers would work with Google to install sensors and software so the vans can drive themselves. The added vehicles are needed as Google expands real-world testing. Fiat Chrysler and Google will work together to more than double the size of Google's self-driving vehicle fleet by adding 100 Chrysler Pacifica (pictured) minivans. Google says it will own the gas-electric hybrid vans, and it's not currently licensing autonomous car technology to Fiat Chrysler or anyone else.

On 25 April, Zooniverse launched a website that asks volunteers to analyse rapidly-snapped satellite imagery of the disaster, which led to more than 650 reported deaths and 16,000 injuries. The aim is to help relief workers on the ground to find the most heavily damaged regions and identify which roads are passable. Several crisis-mapping programmes with thousands of volunteers already exist -- but it can take days to train satellites on the damaged region and to transmit data to humanitarian organizations, and results have not always proven useful. The Ecuador quake marked the first live public test for an effort dubbed the Planetary Response Network (PRN), which promises to be both more nimble than previous efforts, and to use more rigorous machine-learning algorithms to evaluate the quality of crowd-sourced analyses. The network relies on imagery from the satellite company Planet Labs in San Francisco, California, which uses an array of shoebox-sized satellites to map the planet.

Machines equipped with deep-learning algorithms may be as good as humans in detecting cancer in ultrasound images and in identifying it in pathology reports, according to recent news out of Samsung, the Regenstrief Institute, and Indiana University. Evidence continues to mount when it comes to the potential of artificial intelligence to help health providers spot signs of illness in patient, including such deadly maladies as cancer. Samsung Medison, for example, has updated its RS80A ultrasound imaging machine with a feature it's calling S-Detect for Breast to analyze breast lesions. It uses big data collected from breast-exam cases and recommends whether a particular lesion is benign or malignant, according to a report in Kurzweil Accelerating Intelligence. The affiliate of Samsung Electronics designed the update for use in lesion segmentation, characteristic analysis, and assessment processes for more accurate results, the company said in a statement.

Google has been given access to an estimated 1.6 million NHS patient records, it has been revealed. The data-sharing agreement, revealed by New Scientist, includes full names as well as patient histories. Google says it will use the data to develop an early warning system for patients at risk of developing acute kidney injuries. But critics have questioned why it needs the data of all patients to create such a specific app. Under the data-sharing agreement, Google's artificial intelligence division DeepMind will have access to all of the data of patients from the Royal Free, Barnet and Chase Farm hospitals in London going back over the past five years and continuing until 2017.

Airlines around the world are eager to take advantage of rapidly emerging technologies to improve their passengers' experience and become more efficient. But while executives recognize the opportunities, they know they can't do it alone. The two industry leaders in aircraft engines and technology are collaborating to offer carriers their expertise and ideas in a business where cutting 1 percent of fuel usage amounts to 250,000 in annual savings per plane. A recent PricewaterhouseCoopers report estimates digital tools in aircraft maintenance could save more than 100 million a year for a large carrier with a fleet of about 500 planes. "Our TotalCare maintenance program was revolutionary in the '90s, so we're pioneers ourselves, and by collaborating with a fellow pioneer like Microsoft, we can absolutely bring innovative digital solutions to airlines now," says Alex Dulewicz, head of marketing for services at Rolls-Royce's civil aerospace division.

Machine-reengineering is a way to automate business processes using machine learning. Although machine-reengineering is new, companies are already seeing striking results with it, particularly in boosts to speed and efficiency. Studying 168 early adopters, we've seen speed improvements of two times or more for most business processes -- and some organizations are reporting speed improvements of 10 times or more. How do companies do it? Our study found that organizations are using machine-reengineering to establish new forms of human-machine collaboration that break through the bottlenecks of complex digital processes.

In the last few years, an increasing amount of public-private initiatives have adopted IoT solutions, ranging from smart grids to energy efficiency applications. The unprecedented growth of the urban population, meanwhile, highlights the importance of increased public-private cooperation in smart cities and the circular economy to deliver more scalable low-carbon development models. For example, IBM's China Research Lab is working with the Beijing Environmental Protection Bureau (BEPB) and other municipal authorities in China to scale-up its air quality forecasting system, as part of IBM's Green Horizons Initiative. The system uses pollution data from a network of sensors spread throughout Beijing. Through complex modeling and machine learning techniques, it returns increasingly precise forecasts of air pollution levels in different neighborhoods.

This column is a little cheerful, slightly analytical, both confident and tentative and just a tiny bit angry. At least that's what IBM's Watson thinks. Last week, IBM revealed that its Jeopardy-winning supercomputer has a new capability. It's called Watson Tone Analyzer. You can use it like spell check, except instead of checking your spelling, it checks the "tone" of your writing.

The rise of the chatbot and the fall of the app-based economy have recently taken centre stage, thanks in large part to Mark Zuckerberg's announcement that Facebook was opening up Messenger to third-party bots. The potential for brands to interact with users at the same level they do with their peers is immense. As Kik's Ivar Chan says, "In a world where messenger apps have surpassed social networks, companies need to expand their digital presence to these greenfield pastures." OK, he's a partner at a leading chatbot specialist, so he does have a dog in the fight… but he's also right. This isn't about bots and artificial intelligence, this is about ubiquity of messaging as platform.