There is an increasing demand for algorithms to explain their outcomes. So far, there is no method that explains the rankings produced by a ranking algorithm. To address this gap we propose LISTEN, a LISTwise ExplaiNer, to explain rankings produced by a ranking algorithm. To efficiently use LISTEN in production, we train a neural network to learn the underlying explanation space created by LISTEN; we call this model Q-LISTEN. We show that LISTEN produces faithful explanations and that Q-LISTEN is able to learn these explanations. Moreover, we show that LISTEN is safe to use in a real world environment: users of a news recommendation system do not behave significantly differently when they are exposed to explanations generated by LISTEN instead of manually generated explanations.

With the aid of an intelligent system, a group of people (the key group) faces a hiring decision about many candidates who are characterized by attributes, such as experiences, technical skills, communication skills, etc. The goal is to help the key group make a group decision without directly eliciting their full preferences over all candidates, which is often infeasible given the vast number of candidates. Instead, the intelligent system may ask fellow employees (the regular group) about their preferences in order to learn about the key group's preferences. How can the intelligent system decide which member in the regular group to ask and which question should be asked? This example illustrates the preference elicitation problem, which has been widely studied in the field of recommender systems [Loepp et al., 2014], healthcare [Erdem and Campbell, 2017, Weernink et al., 2014], marketing [Huang and Luo, 2016], stable matching [Drummond and Boutilier, 2014, Rastegari et al., 2016], etc. Most previous works studied a special case of the aforementioned scenario, in which the regular group is the key group. The objective of preference elicitation is to achieve some goal using as few samples (data) as possible. A common approach is to adaptively ask questions that maximize expected information gain, measured by some information criteria. Moreover, most previous work focused on a specific type of elicitation questions, e.g.

We conduct an empirical study on discovering the ordered collective dynamics obtained by a population of intelligence agents, driven by million-agent reinforcement learning. Our intention is to put intelligent agents into a simulated natural context and verify if the principles developed in the real world could also be used in understanding an artificially-created intelligent population. To achieve this, we simulate a large-scale predator-prey world, where the laws of the world are designed by only the findings or logical equivalence that have been discovered in nature. We endow the agents with the intelligence based on deep reinforcement learning (DRL). In order to scale the population size up to millions agents, a large-scale DRL training platform with redesigned experience buffer is proposed. Our results show that the population dynamics of AI agents, driven only by each agent's individual self-interest, reveals an ordered pattern that is similar to the Lotka-Volterra model studied in population biology. We further discover the emergent behaviors of collective adaptations in studying how the agents' grouping behaviors will change with the environmental resources. Both of the two findings could be explained by the self-organization theory in nature.

There's a lot of buzz about artificial intelligence being a game-changer for the telecom industry, but machine learning is paving the way in the short term. BT's Neil McRae, chief architect, said there are elements of artificial intelligence in today's machine learning, and that machine learning could enable more, and deeper automation in networks. "BT Labs have been doing some work with Cambridge University leveraging both machine learning and artificial intelligence to improve our ability to react to events on the network," McRae said. "The initial findings are very promising. Networks are more complex, customers demand more and more from the network and I want to ensure that the network is the strongest part of our customers supply change. Today though, more often than not, humans are the reason for problems in the network. Using machine learning to let the network learn rather than scripting that automation will accelerate automation and I believe will bring benefits quicker. "When you actually look at what artificial intelligence is and what you need to do to deploy and use it in a network function, that's not a trivial thing, but using approaches such as machine learning are going to be crucial to enable the end-to-end automation that we need and we need those ASAP." As cloud, 5G and IoT began to converge, ML adds value to operating models by helping to create "smart" software networks. Pushed by IoT platforms, automation and cloud-based technologies, the global machine learning as a service (MLaaS) market is projected to grow from $ 679.32 million in 2016 to $7620.18 million by 2023 with a CAGR of 41.2%, according to Stratistics MRC. McRae said that web-scale Internet companies, such as Google and Amazon Web Services, were among the current leaders for using machine learning (ML) and automation. "They're still at the very start of this journey, but I believe in the future, for sure, those things will play a huge part in network operations and network optimization," McRae said. As for current machine learning use cases, BT is trialing ML for deploying segment routing on its network. "We're using machine learning and artificial intelligence as part of our path computation engine to show what's the best path and what has the least risks in that path," McRae said. "What is the most bandwidth on that path?

Facial recognition software used by the UK's biggest police force has returned false positives in more than 98 per cent of alerts generated, The Independent can reveal, with the country's biometrics regulator calling it "not yet fit for use". The Metropolitan Police's system has produced 104 alerts of which only two were later confirmed to be positive matches, a freedom of information request showed. In its response the force said it did not consider the inaccurate matches "false positives" because alerts were checked a second time after they occurred. Facial recognition technology scans people in a video feed and compares their images to pictures stored in a reference library or watch list. It has been used at large events like the Notting Hill Carnival and a Six Nations Rugby match. The system used by another force, South Wales Police, has returned more than 2,400 false positives in 15 deployments since June 2017.

Thursday, May 10, 2018, Baltimore, MD - Insilico Medicine, a Baltimore-based next-generation artificial intelligence company specializing in the application of deep learning for target identification, drug discovery and aging research announces the publication of a new research paper in Molecular Pharmaceutics journal titled "Adversarial Threshold Neural Computer for Molecular De Novo Design". The described Adversarial Threshold Neural Computer (ATNC) model based on the combination of Generative Adversarial Networks (GANs) with Reinforcement Learning (RL) is intended for the design of novel small organic molecules with the desired set of pharmacological properties. "This is a proof of concept scratching the surface of what we have in house. Stay tuned for the cool experimental validation results to be announced this Summer. I hope that part of this work integrated into our pipeline will help make the world a better and healthier place and help make perfect molecules for specific targets and multiple targets that will have a much higher chance of becoming great drugs", said Evgeny Putin, the deep learning lead at Insilico Medicine. The architecture of GANs was initially proposed by Ian Goodfellow in 2015, and since the inception, the GAN-based models have achieved the unprecedented accuracy in image, video and text generation.

Not even the highest Roman Catholic church archivists know what's hiding in the archives' endless volumes, which are carefully stored near the Sistine Chapel. Only a tiny amount of these archives have been digitized–the rest is an endless ocean of inaccessible papers and parchments. Going through its tomes in search of something would be a task that not even the goddess Minerva herself would be able to accomplish. Some libraries have used technology to digitize their collections, like Optical Character Recognition software that's trained to recognize fixed, separated individual letter shapes. However, OCR is useless when it comes to the endless variety of free-flowing cursive styles featured in many of the Vatican's tomes, which go all the way back to the eighth century.

Multiagent coordination in cooperative multiagent systems (MASs) has been widely studied in both fixed-agent repeated interaction setting and the static social learning framework. However, two aspects of dynamics in real-world multiagent scenarios are currently missing in existing works. First, the network topologies can be dynamic where agents may change their connections through rewiring during the course of interactions. Second, the game matrix between each pair of agents may not be static and usually not known as a prior. Both the network dynamic and game uncertainty increase the coordination difficulty among agents. In this paper, we consider a multiagent dynamic social learning environment in which each agent can choose to rewire potential partners and interact with randomly chosen neighbors in each round. We propose an optimal rewiring strategy for agents to select most beneficial peers to interact with for the purpose of maximizing the accumulated payoff in repeated interactions. We empirically demonstrate the effectiveness and robustness of our approach through comparing with benchmark strategies. The performance of three representative learning strategies under our social learning framework with our optimal rewiring is investigated as well.

Matrix completion is one of the key problems in signal processing and machine learning, with applications ranging from image pro- cessing and data gathering to classification and recommender sys- tems. Recently, deep neural networks have been proposed as la- tent factor models for matrix completion and have achieved state- of-the-art performance. Nevertheless, a major problem with existing neural-network-based models is their limited capabilities to extend to samples unavailable at the training stage. In this paper, we propose a deep two-branch neural network model for matrix completion. The proposed model not only inherits the predictive power of neural net- works, but is also capable of extending to partially observed samples outside the training set, without the need of retraining or fine-tuning. Experimental studies on popular movie rating datasets prove the ef- fectiveness of our model compared to the state of the art, in terms of both accuracy and extendability.

Predictive Analytics World for Industry 4.0, the leading vendor-independent conference for applied predictive analytics is back, in Munich, 12-13 Jun. Discover and discuss the latest trends and technologies in machine & deep learning for the era of Internet of Things and artificial intelligence.