The biggest recent news in the crypto world is that China issued a ban on ICOs, swiftly followed by South Korea. Japan is also rumored to be considering a similar ban. The U.S. SEC has also issued a warning against "pump-and-dump" ICO schemes, and is evaluating whether some offerings were not disguised securities following the DAO case. The above raises questions about the meaning of money, securities, and might help "correct the market". Some of it is reminiscent of China's ban of digital currencies for use in the "real world" way back in 2009 -- especially Tencent's.

Social Cognitive Optimization (SCO) is an optimization algorithm for solving the (constrained) numerical optimization problem. SCO is a simple agent-based model based on the observational learning mechanism in human social cognition. Related Information: Please find other related code and software in our Source Code Library. License information: SCO is free software; you can redistribute and/or modify it under the terms of Creative Commons Non-Commercial License 3.0. Problem to be solved: (constrained) numerical optimization problem (NOP), or called the nonlinear programming problem.

Jordan Bitterman, CMO of IBM Watson Content & IoT Platform, explores in this seminar the power and promise of the new cognitive era and how it will enable marketers make better decisions, with more confidence and less risk. While AI is expected to create 15 million new jobs over the next 10 years, experts also anticipate 25 million jobs will be replaced by automation in that time period. Peter Spande, CRO of Business Insider, and four other panelists take part in a thoughtful debate about risks and rewards of the technology set to transform our lives, for better or worse. Right now, Artificial Intelligence only has the equivalent of a couple of hundred brain neurons as compared to the 100-billion of our brains. See an insightful discussion lead by Zach Seward, SVP of Product and Executive Editor at Quartz, about the future of A.I. for the advertising industry.

Robots filling orders for Amazon AMZN, 0.99% and driverless vehicles hardly spell the end of work, but the artificial intelligence driving the current wave of automation -- if Americans don't embrace it effectively -- could catapult China ahead of the United States once and for all. Those of us who were around in the 1960s remember elevator operators and bowling alley pin-setters losing their jobs. Alarmists warn that artificial intelligence now is beginning to enable machines to replace not just unskilled workers but knowledge workers too -- for example, insurance adjusters. They worry society will divide between those owning the intellectual property and indolent masses who will depend on government handouts. More compelling is the example of mid-20th century office workers.

Mumbai: In a newly-published research paper that sharpens focus on the intersection of machine intelligence and neuroscience, Purdue University researchers have demonstrated how to decode what the human brain is seeing by using artificial intelligence (AI) to interpret Functional magnetic resonance imaging (fMRI) scans from people watching videos, representing a sort of mind-reading technology. The advancement, according to the researchers, could aid efforts to improve AI and lead to new insights into brain function. Critical to the research, which appeared online on 20 October in the journal Cerebral Cortex, is a type of algorithm called a convolutional neural network. Convolutional neural networks, a form of deep learning algorithm, have been used to study how the brain processes static images and other visual stimuli. Deep learning itself is an advanced machine learning technique that uses layered (hence "deep") neural networks (neural nets) that are loosely modelled on the human brain.

Apple sees its mobile devices as a major platform for artificial intelligence in the future, Chief Operating Officer Jeff Williams said on Monday. Later this week, Apple is set to begin taking pre-orders for its new smartphone, the iPhone X - which starts at $999 and uses artificial intelligence (AI) features embedded in the company's latest A11 chips. The phone promises new facial recognition features such as Face ID that uses a mathematical model of a person's face to allow the user to sign on to their phones or pay for goods with a steady glance at their phones. "We think that the frameworks that we've got, the'neural engines' we've put in the phone, in the watch ... we do view that as a huge piece of the future, we believe these frameworks will allow developers to create apps that will do more and more in this space, so we think the phone is a major platform," Williams said. He was speaking at top chip manufacturer Taiwan Semiconductor Manufacturing Company's 30th anniversary celebration in Taipei, which was attended by global tech executives.

TOKUSHIMA – The Tokushima Prefectural Government and e-books distributor Media Do Holdings Co. announced Tuesday they will launch an experiment to use AI to summarize the governor's news conferences in a bid to make more administrative documents available online more quickly. Under the experiment, which they plan to start Monday, they will use a speech recognition system to catch remarks at news conferences given by the governor, and the complete transcription will be posted on the prefectural government's website after being checked by prefecture officials. Users can choose the amount of words they want to read from 10 percent to 90 percent, and AI summarizes the documents in accordance with the users' requests. The new system will reduce the time needed to transcribe news conferences from roughly 10 hours to two hours, and will enable the prefecture to post documents about four hours after the news conferences are held, according to the officials. "Government agencies create a lot of documents but most of them are difficult to read. I hope summarized documents will help people familiarize themselves with the government," said Tokushima Gov. Kamon Iizumi, adding that the move will also help reduce prefectural officials' working hours.

Performance of data-driven network for tumor classification varies with stain-style of histopathological images. This article proposes the stain-style transfer (SST) model based on conditional generative adversarial networks (GANs) which is to learn not only the certain color distribution but also the corresponding histopathological pattern. Our model considers feature-preserving loss in addition to well-known GAN loss. Consequently our model does not only transfers initial stain-styles to the desired one but also prevent the degradation of tumor classifier on transferred images. The model is examined using the CAMELYON16 dataset.

Abstract--We investigate anomaly detection in an unsupervised framework and introduce Long Short Term Memory (LSTM) neural network based algorithms. In particular, given variable length data sequences, we first pass these sequences through our LSTM based structure and obtain fixed length sequences. We then find a decision function for our anomaly detectors based on the One Class Support Vector Machines (OC-SVM) and Support Vector Data Description (SVDD) algorithms. As the first time in the literature, we jointly train and optimize the parameters of the LSTM architecture and the OC-SVM (or SVDD) algorithm using highly effective gradient and quadratic programming based training methods. To apply the gradient based training method, we modify the original objective criteria of the OC-SVM and SVDD algorithms, where we prove the convergence of the modified objective criteria to the original criteria. We also provide extensions of our unsupervised formulation to the semisupervised and fully supervised frameworks. Thus, we obtain anomaly detection algorithms that can process variable length data sequences while providing high performance, especially for time series data. Our approach is generic so that we also apply this approach to the Gated Recurrent Unit (GRU) architecture by directly replacing our LSTM based structure with the GRU based structure. In our experiments, we illustrate significant performance gains achieved by our algorithms with respect to the conventional methods. Anomaly detection [1] has attracted significant interest in the contemporary learning literature due its applications in a wide range of engineering problems, e.g., sensor failure [2], network monitoring [3], cybersecurity [4] and surveillance [5].