At San Francisco's first fully automated restaurant, meals appear in little glass cubbies, just 90 seconds after customers order and pay on wall-mounted iPads. It's a human-less experience – no waitstaff, no cashier, no one to get your order wrong and no one to tip. The moment before the meal appears, the see-through display screen that fronts the cubbies goes black for the few seconds when you might catch sight of the hand that feeds you. Related: Would you bet against sex robots? AI'could leave half of world unemployed' Eatsa has not yet achieved total automation.

Last summer, Jacky Alciné learned just how biased computers can be. Alciné, who is African-American, took a bunch of pictures with friends at a concert. Later he loaded them into Google Photos, which stores and automatically organizes images. Google's software is able to group together pictures of a particular friend, or pictures of dogs, cats, etc. But when it labeled a picture of one of Alciné's friends, who is also African-American, it left him speechless.

The lifetime tenure of each of the U.S. Supreme Court's nine justices means they have long-lasting influences on the country's affairs. How a justice votes in a case is a reflection of his or her judicial temperament, personal philosophy and political ideology. Political scientists, lawyers, constitutional scholars and laymen alike closely watch the court. Virginia Tech designed a'data-driven framework' that learns justices' judicial preference and voting behaviors. Named Supreme Court Ideal Point Miner, it is 79% accurate.

Forget 1-Click ordering, buying goods on Amazon could soon be made in the blink of an eye, literally. The retail giant has been awarded a patent that would allow users to verify online payments by winking at a phone or tablet's front-facing camera. It takes the facial recognition already seen in a range of devices to the next level, to avoid people trying to use photos or similar to bypass the biometric technology. Forget 1-Click ordering, buying goods on Amazon could soon be made in the blink of an eye, literally. The retail giant has been awarded a patent that would allow users to verify online payments by winking at a phone or tablet's front-facing camera (illustrated) The patent, called'Image analysis for user authentication' patent was filed in October last year and awarded to the Washington-based firm this week.

Suresh Venkatasubramanian of the University of Utah presented a method for finding disparate impact in algorithms last year at the ACM Conference on Knowledge Discovery and Data Mining. If you have ever searched for hotel rooms online, you have probably had this experience: surf over to another website to read a news story and the page fills up with ads for travel sites, offering deals on hotel rooms in the city you plan to visit. Buy something on Amazon, and ads for similar products will follow you around the Web. The practice of profiling people online means companies get more value from their advertising dollars and users are more likely to see ads that interest them. The practice has a downside, though, when the profiling is based on sensitive attributes, such as race, sex, or sexual orientation.

Traditional theories of social models conceptualize teams as distributed processors, disregarding the interdependence necessary to multi-task. Yet, interdependence characterizes social behavior. Instead, traditional theory favor cooperation, a state of least entropy production (LEP), without understanding the causes, limits or consequences of cooperation. As a simple example of interdependence, foraging prey overgraze forests free of predators. In our model, interdependence creates uncertainty, tradeoffs and signals (e.g., prices, coordination, innovation). Unlike individuals, the ability of teams to multitask reflects a quantum-like entanglement that represents maximum entropy production (MEP) when solving the problems signaled by society to improve its welfare. Our model supports findings that evolution in nature is driven by the MEP from making intelligent choices. Exploiting interdependence improves team intelligence, improves performance and reduces the risk of human error; forced cooperation disorganizes it by increasing the risk of error; e.g., if team cooperation improves teamwork, widespread forced cooperation in an autocracy or bureaucracy reduces social intelligence by adding unnecessary noise to signals. In our model, competition between teams self-organizes outsiders willing to sort through the noise for signals of the choices that improve social welfare (e.g., teams in courtrooms; science; entertainment; sports; businesses). Social systems organized around competition (e.g., stronger signals from robust checks and balances) better control a society by more correctly sizing teams to solve problems with fewer errors compared to autocracies or bureaucracies. Overall, we predict, the density of MEP directed at solving problems in a society with the constraints imposed from strong checks and balances, yet able to freely self-organize its labor and capital within those constraints, is denser.

As autonomous systems are becoming more and more pervasive, they often have to make decisions concerning moral and ethical values. There are many approaches to incorporating moral values in autonomous decision-making that are based on some sort of logical deduction. However, we argue here, in order for decision-making to seem persuasive to humans, it needs to reflect human values and judgments. Employing some insights from our ongoing researchusing features of the blackboard architecture for a context-aware recommender system, and a legal decision-making system that incorporates supra-legal aspects, we aim to explore if this architecture can also be adapted to implement a moral decision-making system that generates rationales that are persuasive to humans. Our vision is that such a system can be used as an advisory system to consider a situation from different moral perspectives, and generate ethical pros and cons of taking a particular course of action in a given context.

Rules offer a convenient means of limiting the operational scope of our AI programs so as to not transgress predictable moral boundaries. Yet the imposition of an operational morality based on mere rules will not turn our machines into moral agents, just the unthinking tools of moral designers. If we are to imbue our machines with a profound functional morality, we must first gift them with a moral imagination, for empathic morality — where one agent treats another as it would want to be treated itself — requires an ability to project oneself into the realms of the counterfactual. In this paper we thus explore the role of the moral imagination in generating new and inspiring stories. The creation of novel tales with a built-in moral requires that an artificial system possess the ability to guess at the morality of characters and their actions in novel settings and events. Our moralizing tale-spinner — which generates Aesop-style tales about human-like animals with identifiable human qualities — also faces another challenge: it must render these tales as micro-texts that can be distributed as tweets. As we shall also use metaphor to lend elasticity to our moral conceptions, these short stories, rich in animal metaphors, will comprise part of the daily output of the @MetaphorMagnet Twitterbot.

This paper describes and frames a central ethical issue–the liability problem–facing the regulation of artificial computational agents, including artificial intelligence (AI) and robotic systems, as they become increasingly autonomous, and supersede current capabilities. While it frames the issue in legal terms of liability and culpability, these terms are deeply imbued and interconnected with their ethical and moral correlate–responsibility. In order for society to benefit from advances in AI technology, it will be necessary to develop regulatory policies which manage the risk and liability of deploying systems with increasingly autonomous capabilities. However, current approaches to liability have difficulties when it comes to dealing with autonomous artificial agents because their behavior may be unpredictable to those who create and deploy them, and they will not be proper legal or moral agents. This problem is the motivation for a research project that will explore the fundamental concepts of autonomy, agency and liability; clarify the different varieties of agency that artificial systems might realize, including causal, legal and moral; and the illuminate the relationships between these. The paper will frame the problem of liability in autonomous agents, sketch out its relation to fundamental concepts in human legal and moral agency–including autonomy, agency, causation, intention, responsibility and culpability–and their applicability or inapplicability to autonomous artificial agents.

Here ought does require able--computationally and indeed logically intractable systems The question of Robot Ethics is difficult to resolve not because such as Asimov's laws are excluded (Myers, 2010). of the nature of Robots but because of the nature of What makes moral reasoning about intelligent artefacts Ethics. As with all normative considerations, robot ethics requires different from moral reasoning about natural entities is that that we decide what "really" matters--our most fundamental our obligations can be met not only through constructing the priorities. Are we more obliged to our biological socio-ethical system but also through specifications of the kin or to those with whom we share ideas? Do we value the artefacts. This is the definition of an artefact.