Everyone now is learning, or claiming to learn, Deep Learning (DL), the only field of Artificial Intelligence (AI) that went viral. Paid and free DL courses count 100,000s of students of all ages. Too many startups and products are named "deep-something", just as buzzword: very few are using DL really. Most ignore that DL is the 1% of the Machine Learning (ML) field, and that ML is the 1% of the AI field. Remaining 99% is what's used in practice for most tasks.

The startup behind the Prisma style transfer app is shifting focus onto the b2b space, building tools for developers that draw on its expertise using neural networks and deep learning technology to power visual effects on mobile devices. It's launched a new website, Prismalabs.ai, detailing this new offering. Initially, say Prisma's co-founders, they'll be offering an SDK for developers wanting to add effects like style transfer and selfie lenses to their own apps -- likely launching an API mid next week. Then, in the "next month or so", they also plan to offer another service for developers wanting help to port their code to mobile. This was, after all, how the co-founders originally came up with the idea for the Prisma app -- having seen a style transfer effect working (slowly) on a desktop computer and realized how much potential it would have if it could be made to work in near real-time on mobile.

Given that computers, software, artificial intelligence, machine learning and other'intelligent' systems are human creations then it makes sense that such systems may contain social biases. Social biases may include interpretations of gender, ethnicity or social class, for example. Researchers from University of Massachusetts at Amherst have put forward the case that greater care needs to be taken when developing artificial intelligence that social biases are minimized. Outlining this, Professor Alexandra Meliou, who head up the university's College of Information and Computer Sciences, states: "The increased role of software and the potential impact it has on people's lives makes software fairness a critical property." As an example the researcher states that ethnic bias exists in online advertising delivery systems.

From his perch as editor in chief, he watched as the nascent internet took off, fulfilling his prediction that the world was about to be swept by a digital "Bengali typhoon." Among other things, that epochal storm spawned a dotcom wave that was cresting in 1998. Now, two decades later, Rossetto has written a novel that captures the optimism, greed, fervor, and madness of that era. Set in a fictional San Francisco, Change Is Good: A Story of the Heroic Era of the Internet, follows the intertwined adventures of a startup CEO, a WIRED reporter, a code-writing true believer, and many more instantly iconic characters ripped from the mists of the first dotcom boom. What follows is a chapter from Rossetto's novel, which takes place during a wild party thrown by the fictional WIRED magazine. Carl Hess stands in the line flowing into a looming warehouse off Third Street in the Mission Bay wasteland that was once the old Union Pacific yards.

AI will rise up and kill us all. Didn't Facebook have to shut down their latest monstrous experiment because it went rogue and developed its own secret language? For all we know, Skynet's factories are cranking out an army of Terminators already! The only problem is, it's all nonsense. It's an "existential threat worse than North Korea," he warns. Last I checked they have nukes and a little madman in power and super-AI is still confined to the pages of cyberpunk novels, so I'm not buying it. Look, the guy is a lot smarter than me and I think his batteries, cars and solar roof tiles will change the world but he's spent a little too much time watching 2001: A Space Odyssey.

While we usually see robotics applied to industrial or research applications, there are plenty of ways they could help in everyday life as well: an autonomous guide for blind people, for instance, or a kitchen bot that helps disabled folks cook. Or -- and this one is real -- a robot arm that can perform rudimentary sign language. It's part of a masters thesis from grad students at the University of Antwerp who wanted to address the needs of the deaf and hearing impaired. In classrooms, courts and at home, these people often need interpreters -- who aren't always available. Their solution is "Antwerp's Sign Language Actuating Node," or ASLAN. It's a robotic hand and forearm that can perform sign language letters and numbers.

Nielsen announced that it has acquired vBrand, an Israel-based technology startup that has developed a machine learning-enabled platform to measure brand exposure and impact in sports programming. Financial terms were not disclosed. The acquisition of vBrand's advanced technology supercharges Nielsen Sports' already industry-leading sponsorship measurement capabilities and methodologies, considered among the most robust in sports. Specifically, the vBrand technology could allow brands and rights holders to monitor and track sponsorship visibility within hours of an event and make adjustments to digital signage and social campaigns within a tournament, competition weekend or season. Sports marketing is increasingly important for brands looking to reach consumers in a competitive and fragmented marketplace.

Almost all companies these days are investing thousands of dollars in data analysis to get their data analyzed. Well, in fact studies say that there are around 73% of organizations have invested in Big Data. Why do you think that is the case? What can you reap of the data, ideally just 1s and 0s? Moreover, how does this data help an organization's future?

Abstract argumentation offers an appealing way of representing and evaluating arguments and counterarguments. This approach can be enhanced by considering probability assignments on arguments, allowing for a quantitative treatment of formal argumentation. In this paper, we regard the assignment as denoting the degree of belief that an agent has in an argument being acceptable. While there are various interpretations of this, an example is how it could be applied to a deductive argument. Here, the degree of belief that an agent has in an argument being acceptable is a combination of the degree to which it believes the premises, the claim, and the derivation of the claim from the premises. We consider constraints on these probability assignments, inspired by crisp notions from classical abstract argumentation frameworks and discuss the issue of probabilistic reasoning with abstract argumentation frameworks. Moreover, we consider the scenario when assessments on the probabilities of a subset of the arguments are given and the probabilities of the remaining arguments have to be derived, taking both the topology of the argumentation framework and principles of probabilistic reasoning into account. We generalise this scenario by also considering inconsistent assessments, i.e., assessments that contradict the topology of the argumentation framework. Building on approaches to inconsistency measurement, we present a general framework to measure the amount of conflict of these assessments and provide a method for inconsistency-tolerant reasoning.

Training deep networks is expensive and time-consuming with the training period increasing with data size and growth in model parameters. In this paper, we provide a framework for distributed training of deep networks over a cluster of CPUs in Apache Spark. The framework implements both Data Parallelism and Model Parallelism making it suitable to use for deep networks which require huge training data and model parameters which are too big to fit into the memory of a single machine. It can be scaled easily over a cluster of cheap commodity hardware to attain significant speedup and obtain better results making it quite economical as compared to farm of GPUs and supercomputers. We have proposed a new algorithm for training of deep networks for the case when the network is partitioned across the machines (Model Parallelism) along with detailed cost analysis and proof of convergence of the same. We have developed implementations for Fully-Connected Feedforward Networks, Convolutional Neural Networks, Recurrent Neural Networks and Long Short-Term Memory architectures. We present the results of extensive simulations demonstrating the speedup and accuracy obtained by our framework for different sizes of the data and model parameters with variation in the number of worker cores/partitions; thereby showing that our proposed framework can achieve significant speedup (upto 11X for CNN) and is also quite scalable.