Much as mass electrification enabled the rise of the United States and other advanced economies, so AI is poised to reshape the global order. Forecasts suggest that AI will add a massive $15.7 trillion to the global economy by 2030. Prospects for sustaining global competitiveness are now directly tied to the industrialization of AI. AI and machine learning are predicted to reshape manufacturing, energy management, urban transportation, agricultural production, labor markets, and financial management. Governments that can successfully cultivate a culture of disruptive innovation will be strategically positioned to lead in the twenty-first century.

"We always overestimate the change that will occur in the next two years and underestimate the change that will occur in the next ten" -- Bill Gates. When I started working in AI, some 30 years ago, it was virtually unknown -- very few had heard of AI. Nowadays, I rarely meet anyone who has not heard of AI. Hardly a week now passes without major announcements on AI technology – such as that being used in driverless cars, robotics, and other innovate uses. Yet, AI is hardly new. Indeed, the term AI was first coined at the Dartmouth Conference in 1956 – 0ver 60 years ago.

The ability of machines to rapidly analyze and respond to the unprecedented quantities of data is becoming indispensable as cyber attacks' frequency, scale and sophistication all continue to increase. The research being done today shows that automated cybersecurity systems can do many things with only limited human oversight. Through neural networks, heuristics, data science, etc. systems are being designed to identify cyber attacks, to spot and remove malware, and to find ways to fix bugs faster than any human could. In some respects, this work is simply an extension of the principles that people have got used to in their mail-filters or firewalls. That being said, there is something qualitatively different about the AI's "endgame", i.e. having cybersecurity decisions taken by technology without human intermediation.

But "trying to draw a line between what is military and what is commercial is exceedingly difficult," said R. David Edelman, a technology policy researcher at the Massachusetts Institute of Technology. It is difficult to put a "made in America" label on artificial intelligence. Research on the technology is often done collaboratively by scientists and engineers all over the world. Companies rarely hold on to the details of their A.I. work, as if it were a secret recipe. Instead, they share what they learn, in hopes that other researchers can build on it.

We find ourselves surrounded by a rapidly increasing number of autonomous and semi-autonomous systems. Two grand challenges arise from this development: Machine Ethics and Machine Explainability. Machine Ethics, on the one hand, is concerned with behavioral constraints for systems, so that morally acceptable, restricted behavior results; Machine Explainability, on the other hand, enables systems to explain their actions and argue for their decisions, so that human users can understand and justifiably trust them. In this paper, we try to motivate and work towards a framework combining Machine Ethics and Machine Explainability. Starting from a toy example, we detect various desiderata of such a framework and argue why they should and how they could be incorporated in autonomous systems. Our main idea is to apply a framework of formal argumentation theory both, for decision-making under ethical constraints and for the task of generating useful explanations given only limited knowledge of the world. The result of our deliberations can be described as a first version of an ethically motivated, principle-governed framework combining Machine Ethics and Machine Explainability

In this article, we provide and overview of what we consider to be some of the most pressing research questions facing the fields of artificial intelligence (AI) and computational intelligence (CI); with the latter focusing on algorithms that are inspired by various natural phenomena. We demarcate these questions using five unique Rs - namely, (i) rationalizability, (ii) resilience, (iii) reproducibility, (iv) realism, and (v) responsibility. Notably, just as air serves as the basic element of biological life, the term AIR5 - cumulatively referring to the five aforementioned Rs - is introduced herein to mark some of the basic elements of artificial life (supporting the sustained growth of AI and CI). A brief summary of each of the Rs is presented, highlighting their relevance as pillars of future research in this arena.

In the last decade, several causes have determined the increasing need for rationalization of After years of economic crisis and the resulting resources within regions. The crucial ones are the reduction of resources available for research and increased globalization, mainly pursued by development investments, Smart Specialization has multinational enterprises, the economic crisis immediately become a very relevant concept to get involving all EU regions with different magnitudes these two questions answered (Foray, 2013). It and the diffusion of a new wave of general purpose represents an important chance for a progressive technologies. This situation calls for a deep economical restart. In order to develop a policyprioritization rethinking of the overall approach to regional logic to foster regional growth is development; policy-makers and experts largely important to have a deep knowledge of the potential agree on the fact that the new economic boost should evolutionary pathways related with the existing originate exploiting and enhancing the specific dynamics and the structures at regional level potential and competitive advantage of each region (McCann and Raquel Ortega-Argilès, 2013). In this through focused innovation policies. On this line, the light, each region should start this process using as European Commission has established a program standpoints the knowledge-based sectors in which labelled'Smart Specialization', consisting in a set of already presents a consistent'critical mass' or, at policies and guidelines aimed to promote the efficient and effective use of public investment in other individual to perceive.

This work presents a new strategy for multi-class classification that requires no class-specific labels, but instead leverages pairwise similarity between examples, which is a weaker form of annotation. The proposed method, meta classification learning, optimizes a binary classifier for pairwise similarity prediction and through this process learns a multi-class classifier as a submodule. We formulate this approach, present a probabilistic graphical model for it, and derive a surprisingly simple loss function that can be used to learn neural network-based models. We then demonstrate that this same framework generalizes to the supervised, unsupervised cross-task, and semi-supervised settings. Our method is evaluated against state of the art in all three learning paradigms and shows a superior or comparable accuracy, providing evidence that learning multi-class classification without multi-class labels is a viable learning option.

End-to-end neural models have made significant progress in question answering, however recent studies show that these models implicitly assume that the answer and evidence appear close together in a single document. In this work, we propose the Coarse-grain Fine-grain Coattention Network (CFC), a new question answering model that combines information from evidence across multiple documents. The CFC consists of a coarse-grain module that interprets documents with respect to the query then finds a relevant answer, and a fine-grain module which scores each candidate answer by comparing its occurrences across all of the documents with the query. We design these modules using hierarchies of coattention and self-attention, which learn to emphasize different parts of the input. On the Qangaroo WikiHop multi-evidence question answering task, the CFC obtains a new state-of-the-art result of 70.6% on the blind test set, outperforming the previous best by 3% accuracy despite not using pretrained contextual encoders.

AI'woebots' could be the answer to the growing demand for counselling in the UK, the head of Britain's largest relationship charity said today. A need for more human counsellors has also opened to door to emotional support offered by computers via online chat, text or WhatsApp. Aidan Jones, chief executive of Relate, says that AI chatbots can in some cases offer couples support in the same way a human can. Experts also claim that some people can'open up' more away from a counselling room. Similar'woebots' including one called'Ellie' have already been tested on US Army soldiers with post-traumatic stress disorder.