The recent surveys, studies, forecasts and other quantitative assessments of the health and progress of AI estimated the impact on productivity of human-machine collaboration, the number of jobs that could be automated in major U.S. cities, and the size of the future AI in retail and healthcare markets; and found AI optimism among the general population, algorithms outperforming (again) pathologists, and that our very limited understanding of how our brains learn may improve machine learning. Do you think securing your devices and personal data will become more or less complicated over the next 12 months? DeepMind has developed a machine learning model that can label most animals at Tanzania's Serengeti National Park at least as well as humans while shortening the process by up to 9 months (it normally takes up to a year for volunteers to return labeled photos) [Engadget] In a simulation, biological learning algorithms outperformed state-of-the-art optimal learning curves in supervised learning of feedforward networks, indicating "the potency of neurobiological mechanisms" and opening "opportunities for developing a superior class of deep learning algorithms" [Scientific Reports] The AI in retail market is estimated to reach $4.3 billion by 2024 [P&S Intelligence] [e.g., Nike acquires Celect, August 6, 2019] The AI in healthcare market is estimated to reach $12.2 billion by 2023 [Market Research Future] [e.g., BlueDot has raised $7 million in Series A funding, August 7, 2019] AI companies funded in the last 3 months: 417 for total funding of $8.7 billion Data is eating the world quote of the week: "Although it is fashionable to say that we are producing more data than ever, the reality is that we always produced data, we just didn't know how to capture it in useful ways"--Subbarao Kambhampati, Arizona State University AI is eating the world quote of the week: "We advocate for a new perspective for designing benchmarks for measuring progress in AI. Unlike past decades where the community constructed a static benchmark dataset to work on for the next decade or two, we propose that future benchmarks should dynamically evolve together with the evolving state-of-the-art"--Keisuke Sakaguchi, Ronan Le Bras, Chandra Bhagavatula, Yejin Choi, Allen Institute for Artificial Intelligence and the University of Washington

We propose a novel approach for estimating the difficulty and transferability of supervised classification tasks. Unlike previous work, our approach is solution agnostic and does not require or assume trained models. Instead, we estimate these values using an information theoretic approach: treating training labels as random variables and exploring their statistics. When transferring from a source to a target task, we consider the conditional entropy between two such variables (i.e., label assignments of the two tasks). We show analytically and empirically that this value is related to the loss of the transferred model. We further show how to use this value to estimate task hardness. We test our claims extensively on three large scale data sets -- CelebA (40 tasks), Animals with Attributes 2 (85 tasks), and Caltech-UCSD Birds 200 (312 tasks) -- together representing 437 classification tasks. We provide results showing that our hardness and transferability estimates are strongly correlated with empirical hardness and transferability. As a case study, we transfer a learned face recognition model to CelebA attribute classification tasks, showing state of the art accuracy for tasks estimated to be highly transferable.

The Hawaiian poʻo-uli, a small bird from the honeycreeper family, was first discovered in 1973. Less than half a century later, it disappeared from the planet. Declared extinct in 2018, it is one of almost 700 vertebrate species that have been driven to extinction in the last 500 years. According to a United Nations report issued earlier this year to policymakers, one million species are at risk of extinction: Human actions threaten more plants and animals than ever before. Although the precise number of species on the planet is difficult to calculate, recent estimates put it at around 8.7 million.

Charles Brayne is EY's UK Chief Tax Innovation Officer and Partner and has a dual role. On the one hand, he works with clients to help them adopt new tax technologies and on the other, he oversees the implementation of AI technologies within EY's own tax business. Meanwhile, as EY's UK Chief Tax Data Scientist, Harvey Lewis works directly with tax and law professionals to create and deliver new AI tools and applications, as well as provide strategic oversight for their automation projects. In this keynote, Charles and Harvey discuss EY's lessons from implementing AI within their own organisation. With flagship shows in San Francisco, London, New York, Munich, Hong Kong, Singapore, and Cape Town, 2019 will see over 30,000 delegates from businesses globally joining the AI revolution through The AI Summit events. The AI Summit series uniquely has the support of tech's elite, with our 2019 Industry Partners featuring Agorai, AWS, IBM Watson, Microsoft, Oracle, Google, HCL, Publicis Sapient, Genpact, Intel alongside 300 sponsors and partners.

Amazon Web Services (AWS) has connected the Middle East to its global network with the launch of its Bahrain AWS region. The cloud supplier already has infrastructure in the region, but the launch of the Bahrain AWS region, with three datacentres, will connect to its global network. This will bring the Middle East region up to par with its other global AWS regions as the Middle East accelerates its digital transformation. Andy Jassy, CEO at AWS, said the cloud could unlock digital transformation in the Middle East. "Today, we are launching advanced and secure technology infrastructure that matches the scale of our other AWS regions around the world and are already seeing strong demand in the Middle East for AWS technologies like artificial intelligence (AI) and machine learning, data analytics, IoT [internet of things] and much more," he said.

Object detection is a computer vision technique whose aim is to detect objects such as cars, buildings, and human beings, just to mention a few. The objects can generally be identified from either pictures or video feeds. Object detection has been applied widely in video surveillance, self-driving cars, and object/people tracking. In this piece, we'll look at the basics of object detection and review some of the most commonly-used algorithms and a few brand new approaches, as well. Object detection locates the presence of an object in an image and draws a bounding box around that object.

Explainable AI is an important area of research within which Explainable Planning is an emerging topic. In this paper, we argue that Explainable Planning can be designed as a service -- that is, as a wrapper around an existing planning system that utilises the existing planner to assist in answering contrastive questions. We introduce a prototype framework to facilitate this, along with some examples of how a planner can be used to address certain types of contrastive questions. We discuss the main advantages and limitations of such an approach and we identify open questions for Explainable Planning as a service that identify several possible research directions.

In this paper, we generalize the basic notions and results of Dempster-Shafer theory from predicates to formal concepts. Results include the representation of conceptual belief functions as inner measures of suitable probability functions, and a Dempster-Shafer rule of combination on belief functions on formal concepts.

The team leading NASA's first mission to take a rock sample from the asteroid Bennu has selected four sites for the OSIRIS-REx spacecraft to'tag'. The spacecraft has already mapped the entire Bennu meteor - dubbed the'apocalypse asteroid' - in order to identify the safest and most accessible spots to retrieve a chunk of its surface. Now, the four locations will be studied before the final two sites – a primary and backup – are selected in December, this year. The OSIRIS-REx sample collection is scheduled for the latter half of 2020, and the spacecraft will return the asteroid samples to Earth on September 24, 2023. Osprey is set in a small crater, 66 feet (20 m) in diameter, which is also located in Bennu's equatorial region at 11 degrees north latitude, while Sandpiper is located in the meteor's southern hemisphere, at 47 degrees south latitude Sites: Nightingale is the northern-most site, situated at 56 degrees north latitude on Bennu, while Kingfisher is located in a small crater near Bennu's equator at 11 degrees north latitude The four candidate sample sites on Bennu are designated Nightingale, Kingfisher, Osprey, and Sandpiper – all birds native to Egypt. The naming theme complements the mission's two other naming conventions – Egyptian deities (the asteroid and spacecraft) and mythological birds (surface features on Bennu).

Using machine learning to accelerate ecological research Using machine learning to accelerate ecological research Share Pushmeet Kohli * External authors The Serengeti is one of the last remaining sites in the world that hosts an intact community of large mammals. These animals roam over vast swaths of land, some migrating thousands of miles across multiple countries following seasonal rainfall. As human encroachment around the park becomes more intense, these species are forced to alter their behaviours in order to survive. Increasing agriculture, poaching, and climate abnormalities contribute to changes in animal behaviours and population dynamics, but these changes have occurred at spatial and temporal scales which are difficult to monitor using traditional research methods. There is a great urgency to understand how these animal communities function as human pressures grow, both in order to understand the dynamics of these last pristine ecosystems, and to formulate effective management plans to conserve and protect the integrity of this unique biodiversity hotspot.