The on-chip implementation of learning algorithms would speed-up the training of neural networks in crossbar arrays. The circuit level design and implementation of backpropagation algorithm using gradient descent operation for neural network architectures is an open problem. In this paper, we proposed the analog backpropagation learning circuits for various memristive learning architectures, such as Deep Neural Network (DNN), Binary Neural Network (BNN), Multiple Neural Network (MNN), Hierarchical Temporal Memory (HTM) and Long-Short Term Memory (LSTM). The circuit design and verification is done using TSMC 180nm CMOS process models, and TiO2 based memristor models. The application level validations of the system are done using XOR problem, MNIST character and Yale face image databases

AAAS analyst assists a human rights organization in gathering data during an exhumation. Against a backdrop of summer heat and a constant roar of distant howler monkeys, a scientific analyst piloted a drone to collect data from a hillside in northern Guatemala. At his side, anthropologists affiliated with a regional human rights group painstakingly cleared soil and roots from human remains in a mass grave. "Remains contorted, overlapping, interlaced, a cruel, tragic mashup of Hieronymus Bosch and H.R. Giger," noted Jonathan Drake, senior program associate of the American Association for the Advancement of Science's Geospatial Technologies Project, summoning images from 15th- and 20th-century artists to describe the nightmarish remnants of an atrocity estimated to have occurred sometime after 1980, during Guatemala's lengthy civil war. Clothing with burnt edges stuck to the bones of some.

From facial recognition for unlocking our smartphones to speech recognition and intent analysis for voice assistance, artificial intelligence is all around us today. In the business world, AI is helping us uncover new insight from data and enhance decision-making. For example, online retailers use AI to recommend new products to consumers based on past purchases. And, banks use conversational AI to interact with clients and enhance their customer experiences. However, most of the AI in use now is "narrow AI," meaning it is only capable of performing individual tasks. In contrast, general AI – which is not available yet – can replicate human thought and function, taking emotions and judgment into account.

Text representations using neural word embeddings have proven effective in many NLP applications. Recent researches adapt the traditional word embedding models to learn vectors of multiword expressions (concepts/entities). However, these methods are limited to textual knowledge bases (e.g., Wikipedia). In this paper, we propose a novel and simple technique for integrating the knowledge about concepts from two large scale knowledge bases of different structure (Wikipedia, and Probase) in order to learn concept representations. We adapt the efficient skip-gram model to seamlessly learn from the knowledge in Wikipedia text and Probase concept graph. We evaluate our concept embedding models on two tasks: 1) analogical reasoning, where we achieve a stateof-the-art performance of 91% on semantic analogies, 2) concept categorization, where we achieve a state-of-the-art performance on two benchmark datasets achieving categorization accuracy of 100% on one and 98% on the other. Additionally, we present a case study to evaluate our model on unsupervised argument type identification for neural semantic parsing. We demonstrate the competitive accuracy of our unsupervised method and its ability to better generalize to out of vocabulary entity mentions compared to the tedious and error prone methods which depend on gazetteers and regular expressions. In this paper, we use the terms "concept" and "entity" interchangeably. Hongxia Jin Samsung Research America 665 Clyde Avenue, Mountain View, CA 94043, USA Email: hongxia.jin@samsung.com 2 Walid Shalaby et al. Figure 1 Integrating knowledge from Wikipedia text (left) and Probase concept graph (right). Local concept-concept, concept-word, and word-word contexts are generated from both KBs and used for training the skip-gram model.

The biggest threat to jobs might not be physical robots, but intelligent software agents that can understand our questions and speak to us, integrating seamlessly with all the other programs we use at home and at work. And call centres are particularly at risk. A Chatbot is a computer program designed to simulate conversation with human users, especially over the Internet. The term "ChatterBot" was originally coined by Michael Mauldin (creator of the first Verbot, Julia) in 1994 to describe these conversational programs. Today, most chatbots are either accessed via virtual assistants such as Google Assistant and Amazon Alexa, via messaging apps such as Facebook Messenger or WeChat, or via individual organizations' apps and websites Online chatbots save time and efforts by automating customer support.

We propose a method to perform automatic document summarisation without using reference summaries. Instead, our method interactively learns from users' preferences. The merit of preference-based interactive summarisation is that preferences are easier for users to provide than reference summaries. Existing preference-based interactive learning methods suffer from high sample complexity, i.e. they need to interact with the oracle for many rounds in order to converge. In this work, we propose a new objective function, which enables us to leverage active learning, preference learning and reinforcement learning techniques in order to reduce the sample complexity. Both simulation and real-user experiments suggest that our method significantly advances the state of the art. Our source code is freely available at https://github.com/UKPLab/

Aug 21, 2018 (Heraldkeeper via COMTEX) -- New York, August 22, 2018: Artificial intelligence (AI) Robots is arguably the foremost exciting field in artificial intelligence. It's definitely the foremost controversial: everyone agrees that a mechanism will add a production line, however there is not any consensus on whether a robot will ever be intelligent. Factors like the growing adoption of customer-centric marketing methods, increased use of social media for advertising, and increase in demand for virtual assistants are conducive to the expansion of the AI in promoting market. The Artificial Intelligence (AI) Robots Market is expected to exceed more than US$ 12 Billion by 2024 at a CAGR of 29% in the given forecast period. The Artificial Intelligence (AI) Robots Market is segmented on the lines of its application, offering, robot type and regional.

Killer robots must be banned to prevent unlawful killings, injuries and other violations of human rights'before it's too late', according to Amnesty International. The human rights non-profit is calling upon the United Nations to place tough new restraints on the development of autonomous weapon systems ahead of key negotiations in Geneva this week. The development of automated weapons, which can pick out and eliminate targets without input from a human being, has proliferated over the past decade. Countries including the UK, France, Israel and the US are known to be developing the technology for use in military and police operations. Amnesty International argues humans should remain'at the core of critical decisions' on the use of deadly force, such as the selection and engagement of targets.

There is a "real danger to humanity" if there is no ban on so-called killer robots, says professor of artificial intelligence (AI) and robotics Noel Sharkey. The long-time campaigner against killer robots told Verdict in an exclusive video interview about the dangers of lethal autonomous weapons (LAWS), which are being developed by militaries to kill without human oversight. He made the warning ahead of the UN's 6th meeting on LAWS, taking place in Geneva today and lasting throughout the week. The most recent meeting was in April and saw Austria and China join the list of countries calling for a prohibition on LAWS. This meeting will see more than 70 governments from around the world meet under the auspices of the Convention of Certain Conventional Weapons (CCW).

With deployments of online monitoring systems in distribution networks, massive amounts of data collected through them contain rich information on the operating status of distribution networks. By leveraging the data, based on bidirectional generative adversarial networks (BiGANs), we propose an unsupervised approach for online distribution reliability assessment. It is capable of discovering the latent structure and automatically learning the most representative features of the spatio-temporal data in distribution networks in an adversarial way and it does not rely on any assumptions of the input data. Based on the extracted features, a statistical magnitude for them is calculated to indicate the data behavior. Furthermore, distribution reliability states are divided into different levels and we combine them with the calculated confidence level $1-\alpha$, during which clear criteria is defined empirically. Case studies on both synthetic data and real-world online monitoring data show that our proposed approach is feasible for the assessment of distribution operation reliability and outperforms other existed techniques.