These are people (psst, conspiracy theorists) who believe that the earth is flat. Even after years of research and all the evidence to the contrary, they still believe that if we walk far enough we will fall off the edge. Do you find that crazy? So when someone tried to translate the line "I am a flat-earther" to French, Translate wrote, "Je suis un fou" which literally means "I am a crazy person" in English. Of course, the spokesperson apologised profusely when this was brought to his notice, calling it a "glitch" that will be "taken care of immediately."

A slew of AI weed killers are on the horizon and have the potential to disrupt the multibillion dollar pesticides business. Among them is Swiss-company ecoRobotix and its weed-killing robot. It's solar-powered and can kill weeds for 12 hours straight without an operator at the helm. EcoRobotix uses 20 times less herbicide than traditional methods that spray entire fields. Founded in 2011, ecoRobotix develops autonomous weeding robots, which help farmers to produce healthier food with a more efficient and sustainable use of herbicides.

Samantha, a sex robot stands, in the home of robotics expert Dr Sergi Santos and his partner of 16 years, Maritsa Kissamitaki. The couple deisgned the artificial intelligence-driven robot that they say is capable of enjoying sex. Want to have sex with no strings attached? How about sex with wires attached (or at least wires involved)? Sex robots (or so-called sexbots) are not just coming, they are already here.

"Artificial intelligence" technology is on a roll these days, but it's about to hit a major blockage: Europe's new General Data Protection Regulation (GDPR). The privacy law, which came into effect across the EU on Friday, has several elements that will make life very difficult for companies building machine learning systems, according to a leading Internet law academic. "Big data is completely opposed to the basis of data protection," said Lilian Edwards, a law professor at the University of Strathclyde in Glasgow, Scotland. "I think people have been very glib about saying we can make the two reconcilable, because it's very difficult." Machine learning--the basis of what we call AI--involves algorithms that progressively improve themselves.

Digital assistants have the potential to be the bridge between smart homes, smart cars, smartphones, PCs, wearables and other devices that we use in our personal and professional lives. They could make our lives better organised, and our devices and services much easier to use. What are the promises and challenges of this brave new world of intelligence augmentation? Join us at "A.I. everywhere: How digital assistants will transform our lives" on 5 June 2018 at the Centre for Global Dialogue in Rüschlikon, outside of Zurich. The conference will connect networks of artificial intelligence research and practice with thought leaders and decision makers from society, business and government.

The advent of artificial intelligence and robotics creates new opportunities and risks, necessitating new forms of rule and regulation. Amongst the recommendations contained in Ms. Delvaux's report in this regard are the establishment of an EU agency for robotics and AI, an advisory code of conduct for robotics engineers and a new reporting structure to take account of robotics and AI for the purposes of taxation. Ms. Delvaux will discuss the EU's progress on these and other matters during her address. Mady Delvaux has been an MEP since 2014, and is a member of the Group of the Progressive Alliance of Socialists and Democrats. She has held the positions of Minister of Education, Minister of Social Security, Transport and Communication and State Secretary for Health, Social Security and Youth in the government of Luxembourg.

"I think what's key about AI is that it's a horizontal technology wave--AI is a profound enabling technology which is cutting across all sectors which explains why it's ranked quite so highly," LocalGlobe's cofounder and Forbes Midas List alumnus Saul Klein told Forbes about the results of the survey.

Artificial intelligence (AI) and machine learning (ML) are among the most disruptive technologies when it comes to reshaping the enterprise. New capabilities and applications of AI and ML are being realised every day and will permeate the workforce sooner than we think. However, the implementation of AI and ML in practise doesn't necessarily mean that the number of jobs will decrease or that long-anticipated effects on employment may not come to fruition. Instead, technology can be used to augment human capabilities and improve efficiency levels rather than replace workers. Data-driven companies making in-roads in digital transformation are applying AI and ML to build real-time analytics infrastructures that offer valuable insights, which confer a competitive advantage.

In this paper, five different approaches for reduced-order modeling of brittle fracture in geomaterials, specifically concrete, are presented and compared. Four of the five methods rely on machine learning (ML) algorithms to approximate important aspects of the brittle fracture problem. In addition to the ML algorithms, each method incorporates different physics-based assumptions in order to reduce the computational complexity while maintaining the physics as much as possible. This work specifically focuses on using the ML approaches to model a 2D concrete sample under low strain rate pure tensile loading conditions with 20 preexisting cracks present. A high-fidelity finite element-discrete element model is used to both produce a training dataset of 150 simulations and an additional 35 simulations for validation. Results from the ML approaches are directly compared against the results from the high-fidelity model. Strengths and weaknesses of each approach are discussed and the most important conclusion is that a combination of physics-informed and data-driven features are necessary for emulating the physics of crack propagation, interaction and coalescence. All of the models presented here have runtimes that are orders of magnitude faster than the original high-fidelity model and pave the path for developing accurate reduced order models that could be used to inform larger length-scale models with important sub-scale physics that often cannot be accounted for due to computational cost.

This Ph.D. thesis deals with the optimization of several renewable energy resources development as well as the improvement of facilities management in oceanic engineering and airports, using computational hybrid methods belonging to AI to this end. Energy is essential to our society in order to ensure a good quality of life. This means that predictions over the characteristics on which renewable energies depend are necessary, in order to know the amount of energy that will be obtained at any time. The second topic tackled in this thesis is related to the basic parameters that influence in different marine activities and airports, whose knowledge is necessary to develop a proper facilities management in these environments. Within this work, a study of the state-of-the-art Machine Learning have been performed to solve the problems associated with the topics above-mentioned, and several contributions have been proposed: One of the pillars of this work is focused on the estimation of the most important parameters in the exploitation of renewable resources. The second contribution of this thesis is related to feature selection problems. The proposed methodologies are applied to multiple problems: the prediction of $H_s$, relevant for marine energy applications and marine activities, the estimation of WPREs, undesirable variations in the electric power produced by a wind farm, the prediction of global solar radiation in areas from Spain and Australia, really important in terms of solar energy, and the prediction of low-visibility events at airports. All of these practical issues are developed with the consequent previous data analysis, normally, in terms of meteorological variables.