John Oliver addressed bitcoin and the crypto-craze on Last Week Tonight yesterday evening, describing the phenomenon as, "Everything you don't understand about money combined with everything you don't understand about computers." The British comedian satirised consumers' rush to enter the digicoin market without fully understanding how it works or what much of its accompanying jargon actually means. "The vast majority of people buying these coins are not paying much attention to the details of the startups they are attached to, they are responding to the huge fervour," Oliver observed. "Many people are buying coins for no reason other than other people are buying them." Elsewhere in an extensive 25-minute address, Oliver sent up the "cult-like devotion" with which investors follow sub-Reddits and YouTubers preaching the bitcoin gospel.

Mariano will be one of our experts at the upcoming OpenText Innovation Tour Stockholm on 20 April at The Grand Hotel. The advent of Artificial Intelligence (AI) and Machine Learning (ML) over the past couple of years has been incrementally accelerating, moving from theoretical to tangible solutions that are indeed providing "a way to do it better". Edison would no doubt embrace all the technology disruption around us if he were alive to see it. "There's a way to do it better – find it,"Thomas Edison Let me give you an example. I recently met with a large construction company who was looking to find a better way to decide which big RFIs it should participate in, and which it should decline to bid.

FRANKFURT (Reuters) - Utilities can increase their efficiency by using more artificial intelligence (AI) technology, such as software to predict demand swings in the power grid or to control home appliances, consultancy Roland Berger said. European utilities could achieve efficiency gains of up to a fifth over the next five years using such technology, it said, adding that less than a quarter of firms had a strategy to do this. Power firms across Europe, which previously depended on coal or gas-fired power plants, are having to adapt to the expanding use of renewable power sources and facing a profit squeeze as wholesale electricity prices have fallen. "Companies need to respond to this change and come up with new business models," Torsten Henzelmann, partner at Roland Berger, said."To The rise of renewables, such as solar and wind that provide intermittent supply, has increased the need for intelligent IT systems to balance demand and supply swings as companies seek to meet energy and carbon emissions targets, the consultancy said.

Put your screens away and worship God, I was often told on Sundays. When they do, it's usually fuelled by outrage or misunderstanding – PlayStation game Resistance: Fall of Man's use of Manchester Cathedral for a violent gun battle in 2007 led to legal threats from the Church of England and an apology from Sony. These arguments stem from a misunderstanding of what video games are. If games are seen only as entertainment, it's inappropriate for them to address culturally sensitive topics or be used in sacred settings. However, games are more than just entertainment.

I open Google Translate twice as often as Facebook, and the instant translation of the price tags is not a cyberpunk for me anymore. That's what we call reality. It's hard to imagine that this is the result of a centennial fight to build the algorithms of machine translation and that there has been no visible success during half of that period. The precise developments I'll discuss in this article set the basis of all modern language processing systems -- from search engines to voice-controlled microwaves. The story begins in 1933.

Bottom line: Machine learning algorithms, applications, and platforms are helping manufacturers find new business models, fine-tune product quality, and optimize manufacturing operations to the shop floor level. Manufacturers care most about finding new ways to grow, excel at product quality while still being able to take on short lead-time production runs from customers. New business models often bring the paradox of new product lines that strain existing ERP, CRM and PLM systems by the need always to improve time-to-customer performance. New products are proliferating in manufacturing today, and delivery windows are tightening. Manufacturers are turning to machine learning to improve the end-to-end performance of their operations and find a performance-based solution to this paradox.

First came banks, then came Fintech, now we've got a complicated relationship on our hands. Here's what influencers said last year when we talked about the love-hate relationship between banks and Fintech: Getting back to technology, the nature of the FinTech narrative over the past few years has been evolving. As well, the pace of technology change continues to accelerate. Rapidly evolving advances in artificial intelligence across chatbots, robo-advisors, claims, underwriting, IoT and soon blockchain, add another layer of potential to further shake-up the traditional business model. The rise of startups is a logical step and part of the progress tech has made.

We here summarize our experience running a challenge with open data for musical genre recognition. Those notes motivate the task and the challenge design, show some statistics about the submissions, and present the results.

Deep learning is still not a very common tool in speaker verification field. We study deep convolutional neural network performance in the text-prompted speaker verification task. The prompted passphrase is segmented into word states - i.e. digits -to test each digit utterance separately. We train a single high-level feature extractor for all states and use cosine similarity metric for scoring. The key feature of our network is the Max-Feature-Map activation function, which acts as an embedded feature selector. By using multitask learning scheme to train the high-level feature extractor we were able to surpass the classic baseline systems in terms of quality and achieved impressive results for such a novice approach, getting 2.85% EER on the RSR2015 evaluation set. Fusion of the proposed and the baseline systems improves this result.

Generative adversarial networks are used to generate images but still their convergence properties are not well understood. There have been a few studies who intended to investigate the stability properties of GANs as a dynamical system. This short writing can be seen in that direction. Among the proposed methods for stabilizing training of GANs, {\ss}-GAN was the first who proposed a complete annealing strategy to change high-level conditions of the GAN objective. In this note, we show by a simple example how annealing strategy works in GANs. The theoretical analysis is supported by simple simulations.