Fifth time's the charm: SpaceX has successfully landed the first stage of its Falcon 9 rocket on its droneship out in the Atlantic Ocean, the first time in history such a landing has ever been achieved. All four previous instances of this landing ended in failure. SpaceX hopes to use this type of rocket landing to be able to re-use the first stage of its rockets and dramatically cut the cost of space travel going forward. This landing is a huge step towards that goal, and came during an especially pivotal launch for SpaceX: SpaceX's first cargo re-supply mission to the International Space Station since a Falcon 9 exploded in mid-flight in the summer of 2015, thwarting that particular re-supply attempt. On this mission, SpaceX's uncrewed Dragon capsule -- which sits atop the Falcon 9 rocket and detaches to fly onward to the space station -- was also carrying some really exciting cargo: the first-ever inflatable space habitat designed for the space station, the Bigelow Expandable Activity Module (BEAM).

It is the first company--the first anybody to send a rocket to space and then land it on a floating barge. Sixth time is the charm, apparently. Or at least, anyone with an interest in low cost access to space hopes it will. At 4:43pm ET, the nine engines on board the Falcon 9's stage 1 rocket began pushing 1.53 million pounds of thrust against Earth. After about two and a half minutes, and several hundred thousand feet of elevation gain, the first stage detached and began a controlled fall back to Earth, arcing towards the football field-sized barge (charmingly-named "Of Course I Still Love You") in the Atlantic Ocean.

A Falcon 9 rocket took off from Cape Canaveral, carrying more supplies to the International Space Station. SpaceX was able to land its rocket on a barge April 8, 2016, about 200 miles off the shore of Cape Canaveral, Fla. (Photo: SpaceX) CAPE CANAVERAL -- The first stage of a SpaceX Falcon 9 rocket almost hit the bull's eye Friday, landing on a barge about 200 miles offshore. Though not exactly in the center of the platform, the maneuver was enough to keep the equipment from getting wet in the Atlantic Ocean. The experiment was the first successful landing. The booster possibly could have returned to land, like one did in December, SpaceX said.

Humans become aroused when touching robots in sensitive places, a new study has found. Far from seeing robots as just computers, humans can become physiologically aroused from touching a human-shaped robot in private places like their eyes and buttocks, the Stanford study found. The results could have huge consequences for the creation of robots in the future, such as ones that people live or even have sex with. It might also help people create "robot stand-ins", that allow people to touch others when actually being there isn't an option, the researchers said. Scientists have taken a leaf out of the script of The Martian by showing how easy it would be to grow your own veg on the Red Planet.

For the vast majority of queries (for example, navigation, simple fact lookup, and others), search engines do extremely well. Their ability to quickly provide answers to queries is a remarkable testament to the power of many of the fundamental methods of AI. They also highlight many of the issues that are common to sophisticated AI question-answering systems. It has become clear that people think of search programs in ways that are very different from traditional information sources. Rapid and ready-at-hand access, depth of processing, and the way they enable people to offload some ordinary memory tasks suggest that search engines have become more of a cognitive amplifier than a simple repository or front-end to the Internet. Like all sophisticated tools, people still need to learn how to use them. Although search engines are superb at finding and presenting information—up to and including extracting complex relations and making simple inferences—knowing how to frame questions and evaluate their results for accuracy and credibility remains an ongoing challenge. Some questions are still deep and complex, and still require knowledge on the part of the search user to work through to a successful answer. And the fact that the underlying information content, user interfaces, and capabilities are all in a continual state of change means that searchers need to continually update their knowledge of what these programs can (and cannot) do.

Mesoscale ocean eddies are a critical component of the Earth System as they dominate the ocean's kinetic energy and impact the global distribution of oceanic heat, salinity, momentum, and nutrients. Therefore, accurately representing these dynamic features is critical for our planet's sustainability. The majority of methods that identify eddies from satellite observations analyze the data in a frame-by-frame basis despite the fact that eddies are dynamic objects that propagate across space and time. We introduce the notion of spatio-temporal consistency to identify eddies in a continuous spatio-temporal field, to simultaneously ensure that the features detected are both spatially and temporally consistent. Our spatio-temporal consistency approach allows us to remove most of the expert criteria used in traditional methods to reduce false negatives. The removal of arbitrary heuristics enables us to render more complete eddy dynamics by identifying smaller and longer lived eddies compared to existing methods.

A challenge to modeling and monitoring the health of the ocean environment is that it is largely under sensed and difficult to sense remotely. Autonomous underwater vehicles (AUVs) can improve observability, for example of algal bloom regions, ocean acidification, and ocean circulation. This AUV paradigm, however, requires robust operation that is cost effective and responsive to the environment. To achieve low cost we generate operational sequences automatically from science goals, and achieve robustness by reasoning about the discrete and continuous effects of actions. We introduce Kongming2, a generative planner for hybrid systems with temporally extended goals (TEGs) and temporally flexible actions. It takes as input high level goals and outputs trajectories and actions of the hybrid system, for example an AUV. Kongming2 makes two major extensions to Kongming1: planning for TEGs, and planning with temporally flexible actions. We demonstrated a proof of concept of the planner in the Atlantic ocean on Odyssey IV, an AUV designed and built by the MIT AUV Lab at Sea Grant.

While traditional research on text clustering has largely focused on grouping documents by topic, it is conceivable that a user may want to cluster documents along other dimensions, such as the author's mood, gender, age, or sentiment. Without knowing the user's intention, a clustering algorithm will only group documents along the most prominent dimension, which may not be the one the user desires. To address the problem of clustering documents along the user-desired dimension, previous work has focused on learning a similarity metric from data manually annotated with the user's intention or having a human construct a feature space in an interactive manner during the clustering process. With the goal of reducing reliance on human knowledge for fine-tuning the similarity function or selecting the relevant features required by these approaches, we propose a novel active clustering algorithm, which allows a user to easily select the dimension along which she wants to cluster the documents by inspecting only a small number of words. We demonstrate the viability of our algorithm on a variety of commonly-used sentiment datasets.

Adequate representation of natural language semantics requires access to vast amounts of common sense and domain-specific world knowledge. Prior work in the field was based on purely statistical techniques that did not make use of background knowledge, on limited lexicographic knowledge bases such as WordNet, or on huge manual efforts such as the CYC project. Here we propose a novel method, called Explicit Semantic Analysis (ESA), for fine-grained semantic interpretation of unrestricted natural language texts. Our method represents meaning in a high-dimensional space of concepts derived from Wikipedia, the largest encyclopedia in existence. We explicitly represent the meaning of any text in terms of Wikipedia-based concepts. We evaluate the effectiveness of our method on text categorization and on computing the degree of semantic relatedness between fragments of natural language text. Using ESA results in significant improvements over the previous state of the art in both tasks. Importantly, due to the use of natural concepts, the ESA model is easy to explain to human users.

Natural language generation (NLG) systems are computer software systems that produce texts in English and other human languages, often from non-linguistic input data. NLG systems, like most AI systems, need substantial amounts of knowledge. However, our experience in two NLG projects suggests that it is difficult to acquire correct knowledge for NLG systems; indeed, every knowledge acquisition (KA) technique we tried had significant problems. In general terms, these problems were due to the complexity, novelty, and poorly understood nature of the tasks our systems attempted, and were worsened by the fact that people write so differently. This meant in particular that corpus-based KA approaches suffered because it was impossible to assemble a sizable corpus of high-quality consistent manually written texts in our domains; and structured expert-oriented KA techniques suffered because experts disagreed and because we could not get enough information about special and unusual cases to build robust systems. We believe that such problems are likely to affect many other NLG systems as well. In the long term, we hope that new KA techniques may emerge to help NLG system builders. In the shorter term, we believe that understanding how individual KA techniques can fail, and using a mixture of different KA techniques with different strengths and weaknesses, can help developers acquire NLG knowledge that is mostly correct.