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.

The objective of this article is to optimize the overall traffic flow on freeways using multiple ramp metering controls plus its complementary Dynamic Speed Limits (DSLs). An optimal freeway operation can be reached when minimizing the difference between the freeway density and the critical ratio for maximum traffic flow. In this article, a Multi-Agent Reinforcement Learning for Freeways Control (MARL-FWC) system for ramps metering and DSLs is proposed. MARL-FWC introduces a new microscopic framework at the network level based on collaborative Markov Decision Process modeling (Markov game) and an associated cooperative Q-learning algorithm. The technique incorporates payoff propagation (Max-Plus algorithm) under the coordination graphs framework, particularly suited for optimal control purposes. MARL-FWC provides three control designs: fully independent, fully distributed, and centralized; suited for different network architectures. MARL-FWC was extensively tested in order to assess the proposed model of the joint payoff, as well as the global payoff. Experiments are conducted with heavy traffic flow under the renowned VISSIM traffic simulator to evaluate MARL-FWC. The experimental results show a significant decrease in the total travel time and an increase in the average speed (when compared with the base case) while maintaining an optimal traffic flow.

In this blog post, I am going to unfold some reasonably technical ideas pertinent most directly to the fourth point in the list: How to make meta-learning work in reality, in the context of a complex multi-algorithm cognitive architecture carrying out a variety of complicated tasks. Dr. Nil Geisweiller has recently written a research blog post describing his current work on "probabilistic inference meta-learning." In his research, he discusses using OpenCog's Probabilistic Logic Networks (PLN) framework as the base-level algorithm for meta-learning, via using pattern-mining and then PLN itself to learn patterns in large sets of PLN inference examples, to learn what sorts of inferences work better in what contexts. This gets at the crux of the meta-learning problem in an OpenCog context; it is about using PLN to help PLN learn how to reason better. This blog post is complementary to Dr. Nil's, in this post I am going to describe some work currently underway to, in effect, fuse various learning/reasoning algorithms now working separately within OpenCog so that they appear as aspects of a single unified learning/reasoning algorithm. This sort of unification provides greater elegance than a situation where there are multiple markedly distinct learning/reasoning algorithms.

Artificial Intelligence (AI) has been termed a "Digital Quake". This term powerfully illustrates the impact of AI on our lives. There is a tendency for people to think that AI does not impact them. But anyone who uses a smartphone or even just e-mail is impacted by AI several times a day. In South Africa alone, the estimated number of smartphones in use is 20.3 million (www.statista.com),

The Toilet Board Coalition, in collaboration with the European Space Agency, is currently soliciting applications from technology providers to improve remote data collection, transmission and synthesis to inform the development of next generation sanitation products and services. Multinational telecommunication company Ericsson has led a myriad of water-related projects across the world ranging from the US to Kenya. Ericsson is once again leading in the water space by crafting an entire smart water network around the Internet of Things (IoT). IoT enables inter-operable data acquisition resulting in real-time water monitoring with intel from the source of the water, its distribution throughout the network, and its final discharge into a receiving water body. By utilizing this technology, water data that has always evaded water managers will now be at their fingertips 24/7, 365 days a year.