Your purchase helps support NPR programming. We've been talking to robots for a while now. In the decade or so since Siri and her compatriots first appeared, we've all gotten pretty used to having conversations with computers in various forms. While your Alexa doesn't look much like a Cylon (the scary metal kind or hotty flesh kind) now, it seems like it's just a matter of time of time before we'll be talking with all kinds of robots -- including those that look just like us. Time, robots and conversations are at the heart of David Ewing Duncan's new book Talking to Robots: Tales from Our Human-Robot Futures.

The lunar space station, Gateway, will orbit the moon in an ellipse -- with a path that will resemble a halo -- when it is assembled in the next decade, NASA and the European Space Agency have announced. The station will act as a half-way house between the Earth and the Moon, acting as a place of shelter, making trips to the moon more efficient and providing a launch pad for missions heading further out into the solar system. Much like the International Space Station, the Gateway will be a permanent base on which astronauts will live for extended periods, conducting research on-board and making regular excursions down to the moon's surface. The halo-like orbit of the lunar gateway will see it trace a halo-like path around the moon (pictured). A stepping stone to allow astronauts to more easily travel to the Moon as well as a forward outpost for crewed excursions further into the solar system, the Lunar Orbital Platform is due for construction within the next decade.

Sometimes even Bill Gates' crystal ball is cloudy. The Microsoft co-founder became one of the most successful tech moguls of all time by foreseeing and capitalizing on world-changing trends, but he acknowledged this week that it's difficult to predict when the coming tide of automation and artificial intelligence will have the most impact on the our economy and labor market. The biggest implications could still be decades away, Gates said in a conversation with Microsoft Research Labs director Eric Horvitz on stage at the Microsoft Research Faculty Summit in Redmond this week. "We have many decades to get this right, but it is a fairly dramatic thing that you want to get society broadly involved in helping you think about with plenty of lead time," he said. When a new technology is emerging, Gates observed, it's easy to imagine widespread adoption is just around the corner.

From improving customer service to automating work processes and providing predictive analysis, artificial intelligence (AI) is transforming the way organisations operate. AI is also bringing significant advantage to cybersecurity in uncovering vulnerabilities and responding to threats. Security correspondent Daniel Bardsley speaks to Paul O'Brien, Director of AI, Service, Security and Operations Lab Applied Research, BT Technology and Professor Nader Azarmi, Emirates ICT Innovation Centre (EBTIC) director and head of BT Global Research Centres to discuss how advancements in AI spells the future of security in the Middle East. There is no shortage of money being invested in cybersecurity research as the threats from attackers appear to grow. Microsoft, for example, spends more than $1 billion annually in cybersecurity research and development, with the firm having said that the amount is increasing as activity migrates to the cloud.

Iran's Revolutionary Guard claims the vessel was caught trying to smuggle Iranian oil to foreign ships; Trey Yingst reports. Iran on Friday denied President Trump's claim that a U.S. warship destroyed an Iranian drone near the Persian Gulf after it threatened the ship -- an incident that further escalated tensions between the countries. Trump said Thursday that the USS Boxer – which is among several U.S. Navy ships in the area – took defensive action after an Iranian drone came within 1,000 yards of the warship and ignored multiple calls to stand down. Trump blamed Iran for a "provocative and hostile" action and said the U.S. responded in self-defense. But Iran's foreign minister, Mohammad Javad Zarif, told reporters as he arrived for a meeting at the United Nations that "we have no information about losing a drone today."

WASHINGTON - A U.S. warship on Thursday destroyed an Iranian drone in the Strait of Hormuz after it threatened the ship, President Donald Trump said. The incident marked a new escalation of tensions between the countries less than one month after Iran downed an American drone in the same waterway and Trump came close to retaliating with a military strike. In remarks at the White House, Trump blamed Iran for a "provocative and hostile" action and said the U.S. responded in self-defense. He said the Navy's USS Boxer, an amphibious assault ship, took defensive action after the Iranian aircraft closed to within 1,000 yards of the ship and ignored multiple calls to stand down. "The United States reserves the right to defend our personnel, facilities and interests and calls upon all nations to condemn Iran's attempts to disrupt freedom of navigation and global commerce," Trump said.

Predicting the remaining useful life of machinery, infrastructure, or other equipment can facilitate preemptive maintenance decisions, whereby a failure is prevented through timely repair or replacement. This allows for a better decision support by considering the anticipated time-to-failure and thus promises to reduce costs. Here a common baseline may be derived by fitting a probability density function to past lifetimes and then utilizing the (conditional) expected remaining useful life as a prognostic. This approach finds widespread use in practice because of its high explanatory power. A more accurate alternative is promised by machine learning, where forecasts incorporate deterioration processes and environmental variables through sensor data. However, machine learning largely functions as a black-box method and its forecasts thus forfeit most of the desired interpretability. As our primary contribution, we propose a structured-effect neural network for predicting the remaining useful life which combines the favorable properties of both approaches: its key innovation is that it offers both a high accountability and the flexibility of deep learning. The parameters are estimated via variational Bayesian inferences. The different approaches are compared based on the actual time-to-failure for aircraft engines. This demonstrates the performance and superior interpretability of our method, while we finally discuss implications for decision support.

Regional rainfall-runoff modeling is an old but still mostly outstanding problem in Hydrological Sciences. The problem currently is that traditional hydrological models degrade significantly in performance when calibrated for multiple basins together instead of for a single basin alone. In this paper, we propose a novel, data-driven approach using Long Short-Term Memory networks (LSTMs), and demonstrate that under a'big data' paradigm, this is not necessarily the case. By training a single LSTM model on 531 basins from the CAMELS data set using meteorological time series data and static catchment attributes, we were able to significantly improve performance compared to a set of several different hydrological benchmark models. Our proposed approach not only significantly outperforms hydrological models that were calibrated regionally but also achieves better performance than hydrological models that were calibrated for each basin individually. Furthermore, we propose an adaption to the standard LSTM architecture, which we call an Entity-A ware-LSTM (EA-LSTM), that allows for learning, and embedding as a feature layer in a deep learning model, catchment similarities. We show that this learned catchment similarity corresponds well with what we would expect from prior hydrological understanding. 1 Introduction A longstanding problem in the Hydrological Sciences is about how to use one model, or one set of models, to provide spatially continuous hydrological simulations across large areas (e.g., regional, continental, global). This is the so-called regional modeling problem, and the central challenge is about how to extrapolate hydrologic information from one area to another - e.g., from gauged to ungauged watersheds, from instrumented to non-instrumented hillslopes, from areas with flux towers to areas without, etc. (Blöschl and Sivapalan, 1995). Often this is done using ancillary data (e.g.

The possible impact of algorithmic recommendation on the autonomy and free choice of Internet users is being increasingly discussed, especially in terms of the rendering of information and the structuring of interactions. This paper aims at reviewing and framing this issue along a double dichotomy. The first one addresses the discrepancy between users' intentions and actions (1) under some algorithmic influence and (2) without it. The second one distinguishes algorithmic biases on (1) prior information rearrangement and (2) posterior information arrangement. In all cases, we focus on and differentiate situations where algorithms empirically appear to expand the cognitive and social horizon of users, from those where they seem to limit that horizon. We additionally suggest that these biases may not be properly appraised without taking into account the underlying social processes which algorithms are building upon.

On 20 July 1969, before an estimated television audience of 650 million, a lunar module named Eagle touched down on the moon's Sea of Tranquility. The tension of the landing and the images of astronauts in futuristic spacesuits striding over the moon's barren surface, Earth reflected in their oversized visors, would prove wildly influential to artists, writers and film-makers. Also watching were the soon-to-be proponents of another technological field populated by brilliant young geeks: computer games. It is perhaps no coincidence that during the early 1960s, when Nasa was working with the Massachusetts Institute of Technology's Instrumentation Lab to develop the guidance and control systems for Apollo spacecraft, elsewhere on campus a programmer named Steve Russell was working with a small team to create one of the first true video game experiences. Inspired by the space race, and using the same DEC PDP-1 model of mainframe computer that generated spacecraft telemetry data for Nasa's Mariner programme, Russell wrote Spacewar!, a simple combat game in which two players controlled starships with limited fuel, duelling around the gravitational well of a nearby star.