In Plato's Republic, Socrates challenges the idea that justice should boil down to telling the truth and returning things that were taken. He raises an interesting dilemma: what if the goods in question would harm other individuals if returned? In other words, giving a weapon back to its owner constitutes a moral dilemma as it could lead to people being harmed by the weapon's use. For Socrates, there was no doubt that protecting people was what should always prevail. Centuries later, Socrates's view is still very relevant in light of recent developments regarding the use of artificial intelligence in warfare.

AI and robotics are going to shape our future. Next there are 10 issues that professionals and researchers need to address in order to desing intelligent systems that help humanity. The flow of misinformation together with our natural inability of perceiving reality based on evidence (a phenomenon called confirmation bias) is a threat to having an informed democracy. Russian hackers influencing the US elections, Brexit campaign and Catalonia crisis are examples of how social media can massively spread misinformation and fake news. It is an open question how institutions are going to address this threat. The scientific revolution in the 18th century and the industrial revolution in the 19th marked a complete change in society.

MIAMI – Two people were killed and 11 others wounded Sunday when a video game tournament competitor went on a shooting rampage before turning the gun on himself in the northern Florida city of Jacksonville, local police said. Sheriff Mike Williams named the suspect of the shooting at a Madden 19 American football eSports tournament as 24-year-old David Katz from Baltimore, Maryland. "There were three deceased individuals at the scene, one of those being the suspect, who took his own life," Williams told reporters. He said local fire and rescue transported nine victims -- seven of whom had gunshot wounds -- to local hospitals, while another two people who were shot took their own transportation to hospital. Williams said Katz was a competitor in the eSports tournament and used "at least one handgun" to carry out the shooting.

After just three days using its new cutting-edge facial comparison biometric system, US customs intercepted an imposter posing as a French citizen trying to enter America. The 26-year-old man who was travelling from Sao Paulo, Brazil, last week became the first person to be caught out by the new technology, which is currently being tested at 14 international US airports. After the system alerted to a facial discrepancy, a search of the passenger revealed the man had concealed a Republic of Congo identification card in his shoe. He was deported without charge. US Customs and Border Protection hope the facial recognition software will detect terrorists and criminals before they can enter the US.

It is not clear what impact, if any, Mr. Mattis's memo had. Though the White House announced in May -- about three weeks before he sent his note -- that it would establish a panel of government officials to study A.I. issues, critics say the administration still has not done enough to set federal policy. Officials with the Office of Science and Technology Policy, which would most likely take a leadership role in setting an agenda for A.I., said that A.I. is a national research and development priority and that it is part of the president's national security and defense strategies. Nonetheless, the Pentagon appears to be pushing ahead on its own, looking for ways to strengthen its ties with A.I. researchers, particularly in Silicon Valley, where there is considerable wariness about working with the military and intelligence agencies. In late June, the Pentagon announced the creation of the Joint Artificial Intelligence Center, or JAIC.

Blockchain technology and artificial intelligence, two cutting-edge technologies, have the potential to change the face of healthcare as we know it by improving the quality and reducing costs through improved efficiencies. Most of us are at least somewhat familiar with artificial intelligence primarily through virtual assistants such as Siri and Alexa. Artificial intelligence automates repetitive learning and discovery through data after initially being set up by a human being. As many people also know, you have to be fairly specific when asking Siri and Alexa any questions -- the question must be posed in the right way -- to get the answer you are looking for. As an example, our interactions with Alexa, Siri, Google Search and Google Photos are based on deep learning.

Some of the best-known applications for artificial intelligence, or AI, range from autonomous vehicles to finance to medical diagnosis. But AI is rapidly expanding to every sector of the economy, including fashion. For a growing number of fashion companies, AI is already transforming the methods used to predict trends, create products and interact with suppliers and customers. Not surprisingly, AI advances raise unique new legal questions, chiefly in the realm of intellectual property and privacy. Broadly defined, AI is computer technology that aims to simulate intelligent human behavior, or to perform cognitive tasks that ordinarily require human intelligence.

While neural networks have shown impressive performance on large datasets, applying these models to tasks where little data is available remains a challenging problem. In this paper we propose to use feature transfer in a zero-shot experimental setting on the task of semantic parsing. We first introduce a new method for learning the shared space between multiple domains based on the prediction of the domain label for each example. Our experiments support the superiority of this method in a zero-shot experimental setting in terms of accuracy metrics compared to state-of-the-art techniques. In the second part of this paper we study the impact of individual domains and examples on semantic parsing performance. We use influence functions to this aim and investigate the sensitivity of domain-label classification loss on each example. Our findings reveal that cross-domain adversarial attacks identify useful examples for training even from the domains the least similar to the target domain. Augmenting our training data with these influential examples further boosts our accuracy at both the token and the sequence level.

Many real-world objects are designed by smooth curves, especially in the domain of aerospace and ship, where aerodynamic shapes (e.g., airfoils) and hydrodynamic shapes (e.g., hulls) are designed. To facilitate the design process of those objects, we propose a deep learning based generative model that can synthesize smooth curves. The model maps a low-dimensional latent representation to a sequence of discrete points sampled from a rational Bézier curve. We demonstrate the performance of our method in completing both synthetic and real-world generative tasks. Results show that our method can generate diverse and realistic curves, while preserving consistent shape variation in the latent space, which is favorable for latent space design optimization or design space exploration. Keywords: Shape synthesis, aerodynamic design, hydrodynamic design, generative adversarial networks 1. Introduction Smooth curves are widely used in the geometric design of products, ranging from daily supplies like bottles and drinking glasses to engineering structures such as aerodynamic or hydrodynamic shapes.

In the year 2000, the Food and Drug Administration (FDA) approved the da Vinci Surgical system, the first robotic surgical aid. Since its introduction, surgeons have used da Vinci to complete more than 20,000 procedures. Simultaneously, medical researchers have developed many innovative robotic technologies. As the public grows more familiar with these advanced systems, health care robotics are becoming a strong marketing point for health care institutions. Get the entire 10-part series on Timeless Reading in PDF.