Self-driving cars without a human supervisor will be tested on public roads in the UK by the end of the year, under government plans. Fully driverless trials have previously only taken place on a limited scale in the US and Europe. The Department of Transport said the move towards advanced trials would push the UK to the forefront of the industry. "Thanks to the UK's world class research base, this country is in the vanguard of the development of new transport technologies, including automation," said Jesse Norman, the transport minister. "The government is supporting the safe, transparent trialling of this pioneering technology, which could transform the way we travel."

Besides being a hot topic these days, emerging technologies such as artificial intelligence and blockchain have received a reputation for being especially male-dominated in an already bro-saturated tech world. However, the buzz around artificial intelligence and cryptography isn't without merit, as these technologies are much more than just one more thing to be mansplained. "It's expected that soon, artificial intelligence will combine the intricacy and pattern recognition strength of human intelligence with the speed, memory and knowledge sharing of machine intelligence." Similarly, decentralized blockchain systems have the potential to change our lives from the way we do business, to the way we drive, vote, make purchases and even prove our identity. With such diverse and far-reaching applications, it is clear that a diversity of perspectives will be necessary to create effective and sustainable solutions.

Reinforcement learning (RL) algorithms have been around for decades and employed to solve various sequential decision-making problems. These algorithms however have faced great challenges when dealing with high-dimensional environments. The recent development of deep learning has enabled RL methods to drive optimal policies for sophisticated and capable agents, which can perform efficiently in these challenging environments. This paper addresses an important aspect of deep RL related to situations that require multiple agents to communicate and cooperate to solve complex tasks. A survey of different approaches to problems related to multi-agent deep RL (MADRL) is presented, including non-stationarity, partial observability, continuous state and action spaces, multi-agent training schemes, multi-agent transfer learning. The merits and demerits of the reviewed methods will be analyzed and discussed, with their corresponding applications explored. It is envisaged that this review provides insights about various MADRL methods and can lead to future development of more robust and highly useful multi-agent learning methods for solving real-world problems.

The fuzzy quantification model FA has been identified as one of the best behaved quantification models in several revisions of the field of fuzzy quantification. This model is, to our knowledge, the unique one fulfilling the strict Determiner Fuzzification Scheme axiomatic framework that does not induce the standard min and max operators. The main contribution of this paper is the proof of a convergence result that links this quantification model with the Zadeh's model when the size of the input sets tends to infinite. The convergence proof is, in any case, more general than the convergence to the Zadeh's model, being applicable to any quantitative quantifier. In addition, recent revisions papers have presented some doubts about the existence of suitable computational implementations to evaluate the FA model in practical applications. In order to prove that this model is not only a theoretical approach, we show exact algorithmic solutions for the most common linguistic quantifiers as well as an approximate implementation by means of Monte Carlo. Additionally, we will also give a general overview of the main properties fulfilled by the FA model, as a single compendium integrating the whole set of properties fulfilled by it has not been previously published.

RL algorithm learns how to act best through many attempts and failures. Trial-and-error learning is connected with the so-called long-term reward. This reward is the ultimate goal the agent learns while interacting with an environment through numerous trials and errors. The algorithm gets short-term rewards that together lead to the cumulative, long-term one. So, the key goal of reinforcement learning used today is to define the best sequence of decisions that allow the agent to solve a problem while maximizing a long-term reward. And that set of coherent actions is learned through the interaction with environment and observation of rewards in every state. Reinforcement learning is distinguished from other training styles, including supervised and unsupervised learning, by its goal and, consequently, the learning approach. Three ML training styles compared.

Mark Zuckerberg says that Facebook is being criticised too much – and that people dislike it partly because it is empowering people. In a message posted to celebrate his company's 15th birthday, the founder and boss defended his company after a bruising period that has seen it accused of abusing people's most personal data and failing to act to stop deadly misinformation. He admitted the company had more to do around disturbing content but also suggested he does not get enough credit for the positive impact Facebook has had on the world. Mr Zuckerberg claimed he had founded Facebook in response to the fact that it was possible to find many things – such as films and music – on the internet, but not people. He did not make any reference to any of the more sordid uses that the early Facebook was put to, including a central feature that allowed people to rate how attractive other students were.

Besides being a hot topic these days, emerging technologies such as artificial intelligence and blockchain have received a reputation for being especially male-dominated in an already bro-saturated tech world. However, the buzz around artificial intelligence and cryptography isn't without merit, as these technologies are much more than just one more thing to be mansplained. "It's expected that soon, artificial intelligence will combine the intricacy and pattern recognition strength of human intelligence with the speed, memory and knowledge sharing of machine intelligence." Similarly, decentralized blockchain systems have the potential to change our lives from the way we do business, to the way we drive, vote, make purchases and even prove our identity. With such diverse and far-reaching applications, it is clear that a diversity of perspectives will be necessary to create effective and sustainable solutions.

With the rapid development of digital information, the data volume generated by humans and machines is growing exponentially. Along with this trend, machine learning algorithms have been formed and evolved continuously to discover new information and knowledge from different data sources. Learning algorithms using hyperboxes as fundamental representational and building blocks are a branch of machine learning methods. These algorithms have enormous potential for high scalability and online adaptation of predictors built using hyperbox data representations to the dynamically changing environments and streaming data. This paper aims to give a comprehensive survey of literature on hyperbox-based machine learning models. In general, according to the architecture and characteristic features of the resulting models, the existing hyperbox-based learning algorithms may be grouped into three major categories: fuzzy min-max neural networks, hyperbox-based hybrid models, and other algorithms based on hyperbox representation. Within each of these groups, this paper shows a brief description of the structure of models, associated learning algorithms, and an analysis of their advantages and drawbacks. Main applications of these hyperbox-based models to the real-world problems are also described in this paper. Finally, we discuss some open problems and identify potential future research directions in this field.

Attention is an increasingly popular mechanism used in a wide range of neural architectures. Because of the fast-paced advances in this domain, a systematic overview of attention is still missing. In this article, we define a unified model for attention architectures for natural language processing, with a focus on architectures designed to work with vector representation of the textual data. We discuss the dimensions along which proposals differ, the possible uses of attention, and chart the major research activities and open challenges in the area.

Besides being a hot topic these days, emerging technologies such as artificial intelligence and blockchain have received a reputation for being especially male-dominated in an already bro-saturated tech world. However, the buzz around artificial intelligence and cryptography isn't without merit, as these technologies are much more than just one more thing to be mansplained. "It's expected that soon, artificial intelligence will combine the intricacy and pattern recognition strength of human intelligence with the speed, memory and knowledge sharing of machine intelligence." Similarly, decentralized blockchain systems have the potential to change our lives from the way we do business, to the way we drive, vote, make purchases and even prove our identity. With such diverse and far-reaching applications, it is clear that a diversity of perspectives will be necessary to create effective and sustainable solutions.