There's one recurring obsession that keeps haunting almost every marketing and ad agency executive. It's in nearly every Powerpoint deck and on every marketer's mind… Millennials: the oh so documented and ubiquitous M Word!. For many years, most marketers (and their agencies) relied on the same old recipe. Brand awareness and reach were the only metrics that mattered. Problem is, the recipe started to turn sour.

You are sat in a café thinking about a last minute birthday present for a friend or relative who you will be meeting later that day. Using your smartphone, you find something suitable via Amazon or similar web-based retailer. You place the order, paying a slight premium for instant delivery. Twenty minutes later you receive a notification that a drone will be arriving shortly at a delivery-port close to the café. You make the five minutes' walk to the designated meeting point.

Nvidia CEO Jen-Hsun Huang said the partnership illustrates the commitment both companies have made to advancing the use-cases of AI. Nvidia and Chinese search engine giant Baidu are teaming up to develop a cloud-based platform for use in artificially intelligent, self-driving cars. The partnership combines Nvidia's self-driving computing platform with Baidu's cloud and mapping technology to develop an algorithm-based operating system capable of powering complex navigation systems in autonomous vehicles. The open platform will be available for branded car OEM consumer vehicle offerings, as well as fleets of driverless commercial vehicles. The companies are going after Level 3 autonomous vehicle control, which means the car can drive itself (and park itself) in some circumstances, but with a competent human driver at the ready.

Artificial intelligence could improve the interpretation of lung function tests for the diagnosis of long-term lung diseases, according to the findings of a new study. The results, presented today (04 September, 2016) at the European Respiratory Society's International Congress, are the first to explore the potential use of artificial intelligence for improving the accuracy of the diagnosis of lung diseases. Current testing requires a series of methods including a spirometry test, which measures the amount (volume) and the speed (flow) of air during breathing, followed by a body plethysmography test measuring static lung volumes and airways resistance and finally a diffusion test, which measures the amount of oxygen and other gases that cross the lungs' air sacs. Analysis of the results of these tests is largely based on expert opinion and international guidelines, attempting to detect a pattern in the findings. In this new study, researchers included data from 968 people who were undergoing complete lung function testing for the first time.

In an earlier post, I described work that I had initially done as an Insight Data Engineering Fellow. That work, now merged into Flink's master branch, was to do an efficient exact k-nearest neighbors (KNN) query using quadtrees. I have since worked on an approximate version of the KNN algorithm, and I will discuss one method I used for the approximate version using Z-value based hashing. For large and high dimensional data sets, an exact k-nearest neighbors query can become infeasible. There are many algorithms that reduce the dimensionality of the points by hashing them to lower dimensions.

This is the second part of Tamis van der Laan's article featured in the new DZone Guide to Big Data Processing, Volume III. Get your free copy for more insightful articles, industry statistics, and more. So far we have assumed we only have a machine learning model, a training set of samples, and a optimization algorithm to learn from these examples. The next thing we will talk about is the problem of overfitting. If we take our example of a discriminative classifier, we see that it splits the space into two distinct regions for each class.

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This paper, using science fiction as a heuristic support for exploring technical potentialities, is based on part of the works of Iain M. Banks, the novels of the "Culture series", in order to examine the role of artificial intelligences and the effects they could have on the life of a community from a political point of view. This series of science fiction novels portrays a galactic civilization based on anarchistic principles in which intelligent machines are largely responsible for managing the tasks linked to the handling of community affairs, thus freeing up the population to pursue more spiritual or fun activities. The first part of this paper shows that beyond the elements included in the stories, the Culture novels can be a way to address political questions that are raised by the widespread presence of highly evolved machines in the organization of a society. The second part, which takes into consideration the supposed founding principles of this civilization, examines the anarchist thought in order not only to display the correspondences between this thought and the vision of Iain M. Banks, but also to show that the various anarchistic currents are in a way outdistanced by the emerging challenges posed by these novels. The third part, written again from a political standpoint, attempts to establish more concrete connections, based on discernable evolutions in computerization or automation of technological systems, which seem to be working their way into a growing number of social processes and their regulation.

Not many people can answer that question.

In manufacturing, advanced robotic technology has opened up the possibility of integrating highly autonomous mobile robots into human teams. However, with this capability comes the issue of how to maximize both team efficiency and the desire of human team members to work with these robotic counterparts. To address this concern, we conducted a set of experiments studying the effects of shared decision-making authority in human–robot and human-only teams. We found that an autonomous robot can outperform a human worker in the execution of part or all of the process of task allocation (\(p 0.001\) for both), and that people preferred to cede their control authority to the robot \((p 0.001)\). We also established that people value human teammates more than robotic teammates; however, providing robots authority over team coordination more strongly improved the perceived value of these agents than giving similar authority to another human teammate \((p 0.001)\).