File photo: Tesla Chief Executive Elon Musk smiles as he attends a forum on startups in Hong Kong, China January 26, 2016. Despite Elon Musk's continued warnings, evil machines won't take over the world, two experts said this week. Artificial intelligence (AI) could be destined to turn against humanity, Musk has argued. The tech exec, who in addition to running high-profile companies such as Tesla and SpaceX, is a co-founder of OpenAI, a non-profit AI research company "discovering and enacting the path to safe artificial general intelligence." However, other executives in Silicon Valley have taken issue with Musk's comments, including the leader of Google's artificial intelligence efforts. "I'm definitely not worried about the AI apocalypse," said Google's John Giannandrea, when speaking at TechCrunch Disrupt SF. "I just object to the hype and soundbites that some people are making," he added.

Bose has officially announced the QuietComfort 25 II or the QC35 II, its first wireless headphones with Google Assistant support. The Bose QC35 II still comes with all the features included with last year's model, including the same sound quality and noise cancelling technology. "The QC35 is already the world's most celebrated wireless headphone, and a survival tool for modern life," Bose on-the-go products director Brian Maguire said. "We didn't change anything that people already love – with the Google Assistant built in, and new choices for what you hear, we made it better." The Bose QC35 II is the first pair of headphones to have Google Assitant integration.

Last week, we reported that Bose's QuietComfort 35 II Bluetooth headphones might include Google Assistant. Bose has revealed that this pair of QC headphones is the first to have a Google Assistant integration. In order to use Google Assistant, just press the "Action" button on the QC35 headphones. You can then talk to it, the same way you would on your phone. You can hear incoming messages, calendar appointments and more.

Data Mining is the computational process of discovering patterns in large data sets involving methods using the artificial intelligence, machine learning, statistical analysis, and database systems with the goal to extract information from a data set and transform it into an understandable structure for further use. Jubatus, MiningMart, Databionic ESOM, Apache Mahout, TraMineR, ROSETTA, KEEL, ADaM, ML-Flex, Modular toolkit for Data Processing, Dataiku, SenticNet API, LIBSVM and LIBLINEAR, Lattice Miner, Gnome datamine tools, yooreeka, AstroML, jHepWork, ARMiner, and arules are some of the top free data mining software. Orange is an open source data visualization and analysis tool. Orange is developed at the Bioinformatics Laboratory at the Faculty of Computer and Information Science, University of Ljubljana, Slovenia, along with open source community. Data mining is done through visual programming or Python scripting.

Generative Adversarial Networks (GANs) produce systematically better quality samples when class label information is provided., i.e. in the conditional GAN setup. This is still observed for the recently proposed Wasserstein GAN formulation which stabilized adversarial training and allows considering high capacity network architectures such as ResNet. In this work we show how to boost conditional GAN by augmenting available class labels. The new classes come from clustering in the representation space learned by the same GAN model. The proposed strategy is also feasible when no class information is available, i.e. in the unsupervised setup. Our generated samples reach state-of-the-art Inception scores for CIFAR-10 and STL-10 datasets in both supervised and unsupervised setup.

As traditional banks grapple with the challenges posed by FinTechs, legacy constraints and traditional operational models, Artificial Intelligence (AI) is emerging as the savior. Businesses in the business of wealth are sparing no efforts to start to tap the potential of AI by experimenting and prototyping: intelligent digital assistants to amplify service, data-models to automate smart lending decisions, fraud detection through pattern recognition, and speech & face recognition. Throughout the financial services world, whether it is machine learning, deep learning, or a series of algorithms that can crunch away into tons of big data, AI is giving the financial services sector distinct strategic advantages – ranging from breakthrough efficiencies to service differentiation advantages - to engage and connect more effectively with the one segment of humanity whose role will never be replaced by machines: customers. According to recent research, Amplifying Human Potential: Towards Purposeful Artificial Intelligence, conducted by Infosys which assessed the impact of AI and current levels of AI maturity in enterprises, adoption is rising smartly, creating the expectation that worldwide by 2020, companies will see AI contributing a 39 percent average increase in revenue and a 37 percent average cut in operating costs. The study found that financial organizations invested much more in AI than other businesses (US$14.6 million versus an average US$6.7 million for all respondents).

The term "industrial Internet of Things" has a more muted-sounding promise of driving operational efficiencies through automation, connectivity and analytics. But the focus of IIoT -- on industry at large -- is broader. Here, we take a comprehensive view, rounding up 20 IIoT leaders and pioneers, drawing on the feedback from industry analysts and consultants. The focus here is not on vendors offering, say, a cloud-based platform for monitoring industrial machines but on the companies that themselves are using IIoT technology to drive their business forward. For the sake of this feature, we focus on organizations that use connected technology in tandem with cloud-based analytics to drive efficiencies and launch new business models.

New research from Capgemini's Digital Transformation Institute shows that four out of five companies implementing AI have created new jobs as a result of AI technology Paris, September 7, 2017 – Capgemini, a global leader in consulting, technology and outsourcing services, has today announced the findings of "Turning AI into concrete value: the successful implementers' toolkit", a study of nearly 1,000 organizations with revenues of more than $500m that are implementing artificial intelligence (AI), either as a pilot or at scale[1]. The research both counters fears that AI will cause massive job losses in the short term, as 83% of firms surveyed say AI has generated new roles in their organizations, and highlights the growth opportunity presented by AI: three-quarters of firms have seen a 10% uplift in sales, directly tied to AI implementation. The report, which surveyed executives from nine countries and across seven sectors, found that four out of five companies (83%) have created new jobs as a result of AI technology. Specifically, organizations are producing jobs at a senior level, with two in three jobs being created at the grade of a manager or above. Furthermore, among organizations that have implemented AI at scale, more than 3 in 5 (63%) said that AI has not destroyed any jobs in their organization.

This paper considers a discrete-time opinion dynamics model in which each individual's susceptibility to being influenced by others is dependent on her current opinion. We assume that the social network has time-varying topology and that the opinions are scalars on a continuous interval. We first propose a general opinion dynamics model based on the DeGroot model, with a general function to describe the functional dependence of each individual's susceptibility on her own opinion, and show that this general model is analogous to the Friedkin-Johnsen model, which assumes a constant susceptibility for each individual. We then consider two specific functions in which the individual's susceptibility depends on the \emph{polarity} of her opinion, and provide motivating social examples. First, we consider stubborn positives, who have reduced susceptibility if their opinions are at one end of the interval and increased susceptibility if their opinions are at the opposite end. A court jury is used as a motivating example. Second, we consider stubborn neutrals, who have reduced susceptibility when their opinions are in the middle of the spectrum, and our motivating examples are social networks discussing established social norms or institutionalized behavior. For each specific susceptibility model, we establish the initial and graph topology conditions in which consensus is reached, and develop necessary and sufficient conditions on the initial conditions for the final consensus value to be at either extreme of the opinion interval. Simulations are provided to show the effects of the susceptibility function when compared to the DeGroot model.

"AI is not far off, it's just the next layer of automation," he said. He gave example of how photography jobs have changed since the early 1990s. He said the number of photographic developers and printers declined but the number of photographers has increased, which shows how automation and innovation allow people to focus on creative work. Kaila Colbin, New Zealand ambassador of Silicon Valley think tank Singularity University, said automation and subsequent loss of jobs have been happening for a long time but the emergence of AI posed threats for jobs that humans were traditionally considered to be more capable at. She said inventions such as AI lawyer "Ross" and chatbot which contested 160,000 parking tickets in London and New York - presents threats for humans.