This paper discusses technology challenges and opportunities to embrace artificial intelligence (AI) era in the design of wireless networks. We aim to provide readers with motivation and general methodology for adoption of AI in the context of next-generation networks. First, we discuss the rise of network intelligence and then, we introduce a brief overview of AI with machine learning (ML) and their relationship to self-organization designs. Finally, we discuss design of intelligent agent and it's functions to enable knowledge-driven wireless networks with AI.

Over the last few years, deep reinforcement learning (RL) has shown impressive results in a variety of domains, learning directly from high-dimensional sensory streams. However, when networks are trained in a fixed environment, such as a single level in a video game, it will usually overfit and fail to generalize to new levels. When RL agents overfit, even slight modifications to the environment can result in poor agent performance. In this paper, we present an approach to prevent overfitting by generating more general agent controllers, through training the agent on a completely new and procedurally generated level each episode. The level generator generate levels whose difficulty slowly increases in response to the observed performance of the agent. Our results show that this approach can learn policies that generalize better to other procedurally generated levels, compared to policies trained on fixed levels.

Research on question answering with knowledge base has recently seen an increasing use of deep architectures. In this extended abstract, we study the application of the neural machine translation paradigm for question parsing. We employ a sequence-to-sequence model to learn graph patterns in the SPARQL graph query language and their compositions. Instead of inducing the programs through question-answer pairs, we expect a semi-supervised approach, where alignments between questions and queries are built through templates. We argue that the coverage of language utterances can be expanded using late notable works in natural language generation.

Online symptom checkers have significant potential to improve patient care, however their reliability and accuracy remain variable. We hypothesised that an artificial intelligence (AI) powered triage and diagnostic system would compare favourably with human doctors with respect to triage and diagnostic accuracy. We performed a prospective validation study of the accuracy and safety of an AI powered triage and diagnostic system. Identical cases were evaluated by both an AI system and human doctors. Differential diagnoses and triage outcomes were evaluated by an independent judge, who was blinded from knowing the source (AI system or human doctor) of the outcomes. Independently of these cases, vignettes from publicly available resources were also assessed to provide a benchmark to previous studies and the diagnostic component of the MRCGP exam. Overall we found that the Babylon AI powered Triage and Diagnostic System was able to identify the condition modelled by a clinical vignette with accuracy comparable to human doctors (in terms of precision and recall). In addition, we found that the triage advice recommended by the AI System was, on average, safer than that of human doctors, when compared to the ranges of acceptable triage provided by independent expert judges, with only a minimal reduction in appropriateness.

Malicious software, or malware, presents a continuously evolving challenge in computer security. These embedded snippets of code in the form of malicious files or hidden within legitimate files cause a major risk to systems with their ability to run malicious command sequences. Malware authors even use polymorphism to reorder these commands and create several malicious variations. However, if executed in a secure environment, one can perform early malware detection on emulated command sequences. The models presented in this paper leverage this sequential data derived via emulation in order to perform Neural Malware Detection. These models target the core of the malicious operation by learning the presence and pattern of co-occurrence of malicious event actions from within these sequences. Our models can capture entire event sequences and be trained directly using the known target labels. These end-to-end learning models are powered by two commonly used structures - Long Short-Term Memory (LSTM) Networks and Convolutional Neural Networks (CNNs). Previously proposed sequential malware classification models process no more than 200 events. Attackers can evade detection by delaying any malicious activity beyond the beginning of the file. We present specialized models that can handle extremely long sequences while successfully performing malware detection in an efficient way. We present an implementation of the Convoluted Partitioning of Long Sequences approach in order to tackle this vulnerability and operate on long sequences. We present our results on a large dataset consisting of 634,249 file sequences, with extremely long file sequences.

The Wimbledon tennis tournament, which starts next week, will use artificial intelligence (AI) technology to help compile its highlights packages this year. The All England Lawn Tennis Club and technology partner IBM said the Watson AI platform had been taught to recognise players' emotions, which it would combine with an analysis of crowd noise, players' movements and match data to help compile highlight reels. Reuters reports that IBM's Sam Seddon said it was using machine learning to find moments where players had a heightened sense of emotion after a key point or tough rally. If you've got the visual element from the player, and you know that it's a tight pressure point in the match, then those are the points that you are going to really target in on in the highlights package," he said. He said the technology will also analyse the noise of the crowd to gauge its excitement.

Hype and fears surround artificial intelligence taking jobs in healthcare. Will it render physicians obsolete? Will it replace the majority of medical professionals? The Medical Futurist decided to set things straight. Here are five fundamental reasons why A.I. won't replace doctors and never will.

When a group of about 40 players first tested out a live game called the Machine Learning President at a private event in San Francisco this February, they were unaware that the game would end up memorialized in the pages of The New Yorker. But during a ski vacation in March, the Republican mega-donor Rebekah Mercer gathered her friends together to play several rounds of the game, which pits special interest groups, political candidates, and activist organizations against each other in a simulated presidential election, aided by cash and artificial intelligence. A lawyer for Mercer told The New Yorker that she owned a copy of the Machine Learning President but had not created it and that it did not reflect her family's views. It's not hard to draw comparisons between the rules of the game, with its reliance on big cash and tech capabilities, and the actions of the Mercer-backed Cambridge Analytica during the 2016 U.S. presidential election. But, as Mercer's lawyer stated, she had nothing to do with creating the game--in fact, it was conceptualized by one of her vocal critics.

This half-day workshop aims at exploring emerging research in the areas of linked data in robotics and Industry 4.0. Industry 4.0 is a collective term (created in Germany) for the technological concepts of cyber-physical systems, the Internet of Things and the Internet of Services, leading to the vision of the Smart Factory. Within a modular structured Smart Factory, cyber-physical systems monitor physical processes, and make decentralized decisions. Over the Internet of Things, cyber-physical systems communicate and cooperate with each other and humans in real time. In addition, one of the aims in robotics is to build smarter robots that can communicate, collaborate and operate more naturally and safely.

Globally-renowned AI expert Dr Demis Hassabis will today be confirmed as an adviser to the new Office for Artificial Intelligence as the UK looks to cement its place as a world leader in the fast-growing technology. Hassabis, who is the co-founder of leading AI research company DeepMind, will provide expert industry guidance to help the country build the skills and capability it needs to capitalise on the huge social and economic potential of AI - a key part of the Government's modern Industrial Strategy. Digital Secretary Matt Hancock will also confirm Tabitha Goldstaub as the chair and spokesperson of the AI Council, a new industry body tasked with increasing growth in the AI sector and promoting its adoption in other sectors of the economy. Tabitha Goldstaub is the co-founder of AI company CognitionX, an online platform which provides companies with information and access to AI experts to boost their businesses, and runs CogX, one of the largest gatherings of AI experts in the world. She led the team who wrote the influential report London: the AI Growth Capital of Europe, and was the co-founder of Rightster, the largest online video distribution company outside the US.