A website presents users with moral dilemmas on who should theoretically die if a driverless car had to choose in a crash. Called Moral Machine, millions of people from around the world have already made over 40million judgments on which lives matter more, should a driverless car be unable to stop or swerve. The scenarios are complex, such as choosing whether to run over children who are crossing the road despite a red light versus older pedestrians who are abiding by the law. Another variable is whether the passengers in a self-driving car should die over people in the street. Characteristics given to the people in the scene purposely – perhaps even subconsciously – forces the survey takers to determine whether a male or female is more important to save.

Tech CEOs and politicians alike have issued grave warnings about the capability of automation, including AI, to replace large swaths of our current workforce. But the people who actually study this for a living -- economists -- have very different ideas about just how large the scale of that automation will be. For example, researchers at Citibank and the University of Oxford estimated that 57 percent of jobs in OECD countries -- an international group of 36 nations including the U.S. -- were at high risk of automation within the next few decades. In another well-cited study, researchers at the OECD calculated only 14 percent of jobs to be at high risk of automation within the same timeline. That's a big range when you consider this means a difference of hundreds of millions of potential lost jobs in the next few decades.

The Scripps Research Translational Institute is partnering with graphics firm NVIDIA to develop AI and deep learning best practices, tools and infrastructure to develop AI applications using genomic and digital health sensor data. With NVIDIA, California-based research organisation Scripps will establish a centre of excellence for artificial intelligence in genomics and digital sensors. Scripps and NVIDIA will work to advance the use of machine learning and deep learning to harness the exploding quantity of health data. The partnership will focus on data generated by faster, more affordable genome sequencing gear, and digital health sensors such as smartwatches, blood pressure cuffs and glucose monitors. NVIDIA AI experts and Scripps researchers and clinicians will use deep learning and machine learning, to tackle the deluge of genomics and sensor data.

There is huge demand for robots to work alongside humans in heterogeneous teams. To achieve a high degree of fluidity, robots must be able to (1) recognize their human co-worker's intent, and (2) adapt to this intent accordingly, providing useful aid as a teammate. The literature to date has made great progress in these two areas -- recognition and adaptation -- but largely as separate research activities. In this work, we present a unified approach to these two problems, in which recognition and adaptation occur concurrently and holistically within the same framework. We introduce Pike, an executive for human-robot teams, that allows the robot to continuously and concurrently reason about what a human is doing as execution proceeds, as well as adapt appropriately. The result is a mixed-initiative execution where humans and robots interact fluidly to complete task goals.Key to our approach is our task model: a contingent, temporally-flexible team-plan with explicit choices for both the human and robot. This allows a single set of algorithms to find implicit constraints between sets of choices for the human and robot (as determined via causal link analysis and temporal reasoning), narrowing the possible decisions a rational human would take (hence achieving intent recognition) as well as the possible actions a robot could consistently take (hence achieving adaptation). Pike makes choices based on the preconditions of actions in the plan, temporal constraints, unanticipated disturbances, and choices made previously (by either agent).Innovations of this work include (1) a framework for concurrent intent recognition and adaptation for contingent, temporally-flexible plans, (2) the generalization of causal links for contingent, temporally-flexible plans along with related extraction algorithms, and (3) extensions to a state-of-the-art dynamic execution system to utilize these causal links for decision making.

`No' belongs to the first ten words used by children and embodies the first active form of linguistic negation. Despite its early occurrence the details of its acquisition process remain largely unknown. The circumstance that `no' cannot be construed as a label for perceptible objects or events puts it outside of the scope of most modern accounts of language acquisition. Moreover, most symbol grounding architectures will struggle to ground the word due to its non-referential character. In an experimental study involving the child-like humanoid robot iCub that was designed to illuminate the acquisition process of negation words, the robot is deployed in several rounds of speech-wise unconstrained interaction with na\"ive participants acting as its language teachers. The results corroborate the hypothesis that affect or volition plays a pivotal role in the socially distributed acquisition process. Negation words are prosodically salient within prohibitive utterances and negative intent interpretations such that they can be easily isolated from the teacher's speech signal. These words subsequently may be grounded in negative affective states. However, observations of the nature of prohibitive acts and the temporal relationships between its linguistic and extra-linguistic components raise serious questions over the suitability of Hebbian-type algorithms for language grounding.

Most network-based speech recognition methods are based on the assumption that the labels of two adjacent speech samples in the network are likely to be the same. However, assuming the pairwise relationship between speech samples is not complete. The information a group of speech samples that show very similar patterns and tend to have similar labels is missed. The natural way overcoming the information loss of the above assumption is to represent the feature data of speech samples as the hypergraph. Thus, in this paper, the three un-normalized, random walk, and symmetric normalized hypergraph Laplacian based semi-supervised learning methods applied to hypergraph constructed from the feature data of speech samples in order to predict the labels of speech samples are introduced. Experiment results show that the sensitivity performance measures of these three hypergraph Laplacian based semi-supervised learning methods are greater than the sensitivity performance measures of the Hidden Markov Model method (the current state of the art method applied to speech recognition problem) and graph based semi-supervised learning methods (i.e. the current state of the art network-based method for classification problems) applied to network created from the feature data of speech samples.

Flying robots that can carry objects 40 times of their own weight and even open doors have been developed in a collaboration between Stanford University and Ecole Polytechnique Federale de Lausanne in Switzerland. Called FlyCroTug the tiny robots have advanced gripping technologies and the ability to move and pull on objects around it. When working in pairs, two FlyCroTugs can jointly lasso the door handle and heave the door open. The clever bots can adhere themselves to surfaces using adhesives inspired by the feet of geckos and insects. These sticky'hands' allow the robust to pull objects 40 times their weight, such as door handles, cameras or water bottles.

"Everything is true…everything anybody has ever thought," Philip K. Dick – Do Androids Dream of Electric Sheep. It is impossible to escape from the fact that technology, and increasingly artificial intelligence (AI), has transformed everyday life. It all started with how we play our music, but Apple's Siri and Amazon's Alexa (along with other similar "virtual assistants") now have a daily interface with many of us. We are also, increasingly, now daily users of the Internet of Things (IoT) – connecting up smart fridges, boilers and alarm systems, each controllable from a smartphone. The "everyday" form of AI is almost unavoidable in the modern home, but, while not necessarily as obvious to you and me, there is also an ongoing, yet unseen growth in AI in the manufacturing sector. What is still lacking, however, is concrete regulation in place for the use and development of AI in the industry.

Verisk Analytics (NASDAQ:VRSK) and Globant (NYSE:GLOB) are both business services companies, but which is the superior investment? We will contrast the two companies based on the strength of their profitability, dividends, institutional ownership, earnings, analyst recommendations, risk and valuation. This table compares Verisk Analytics and Globant's revenue, earnings per share (EPS) and valuation. Verisk Analytics has higher revenue and earnings than Globant. Verisk Analytics is trading at a lower price-to-earnings ratio than Globant, indicating that it is currently the more affordable of the two stocks.

If you ask these tiny drones, "Do you even lift, bro?" you will get a resounding yes. Researchers at Ecole Polytechnique Fédérale de Lausanne (EPFL) in Switzerland and Stanford University have developed a line of small flying bots that can move objects that are 40 times their weight. The drones, called FlyCroTugs (short for "flying, micro tugging robots"), are equipped with a system of winches, adhesives and microspines that allow the tiny crafts, which weigh just a few ounces each, to latch onto just about anything. The winch is one of the few immovable parts of the highly customizable drone -- just about everything else about it can be modified for a given scenario. The grippers can be moved around depending on the landing surface, and the drone can take on additional accessories like wheels when a job calls for it.