South Korea's central bank is stepping up its efforts in digitizing the economy, launching a new department dedicated to emerging technologies. The new department will focus on blockchain, artificial intelligence and other cutting-edge technologies. The Bank of Korea revealed recently that it will be launching the'Digital Innovations' department. As per a report by local outlet In The News, the department will not only implement new digital technologies, but also expand the existing digital infrastructure. The department will have a number of teams that will focus on various sectors, including data service and innovation, the report states.

What if I told a story here, how would that story start?" Thus, the summarization prompt: "My second grader asked me what this passage means: …" When a given prompt isn't working and GPT-3 keeps pivoting into other modes of completion, that may mean that one hasn't constrained it enough by imitating a correct output, and one needs to go further; writing the first few words or sentence of the target output may be necessary.

A small chunk of Mars will be heading home when the US space agency launches its latest rover mission on Thursday. Nasa's Perseverance robot will carry with it a meteorite that originated on the Red Planet and which, until now, has been lodged in the collection of London's Natural History Museum (NHM). The rock's known properties will act as a calibration target to benchmark the workings of a rover instrument. It will give added confidence to any discoveries the robot might make. This will be particularly important if Perseverance stumbles across something that hints at the presence of past life on the planet - one of the mission's great quests.

Deep neural networks (DNNs) have achieved state-of-the-art performance across a variety of traditional machine learning tasks, e.g., speech recognition, image classification, and segmentation. The ability of DNNs to efficiently approximate high-dimensional functions has also motivated their use in scientific applications, e.g., to solve partial differential equations (PDE) and to generate surrogate models. In this paper, we consider the supervised training of DNNs, which arises in many of the above applications. We focus on the central problem of optimizing the weights of the given DNN such that it accurately approximates the relation between observed input and target data. Devising effective solvers for this optimization problem is notoriously challenging due to the large number of weights, non-convexity, data-sparsity, and non-trivial choice of hyperparameters. To solve the optimization problem more efficiently, we propose the use of variable projection (VarPro), a method originally designed for separable nonlinear least-squares problems. Our main contribution is the Gauss-Newton VarPro method (GNvpro) that extends the reach of the VarPro idea to non-quadratic objective functions, most notably, cross-entropy loss functions arising in classification. These extensions make GNvpro applicable to all training problems that involve a DNN whose last layer is an affine mapping, which is common in many state-of-the-art architectures. In numerical experiments from classification and surrogate modeling, GNvpro not only solves the optimization problem more efficiently but also yields DNNs that generalize better than commonly-used optimization schemes.

We present a novel method for generating symbolic adversarial examples: input regions guaranteed to only contain adversarial examples for the given neural network. These regions can generate real-world adversarial examples as they summarize trillions of adversarial examples. We theoretically show that computing optimal symbolic adversarial examples is computationally expensive. We present a method for approximating optimal examples in a scalable manner. Our method first selectively uses adversarial attacks to generate a candidate region and then prunes this region with hyperplanes that fit points obtained via specialized sampling. It iterates until arriving at a symbolic adversarial example for which it can prove, via state-of-the-art convex relaxation techniques, that the region only contains adversarial examples. Our experimental results demonstrate that our method is practically effective: it only needs a few thousand attacks to infer symbolic summaries guaranteed to contain $\approx 10^{258}$ adversarial examples.

A recent article outlines the growing danger of deepfakes and their increased realism, due to machine learning and artificial intelligence. MIT's Center for Advanced Virtuality has created a technologically advanced storytelling project that has manipulated archived footage of President Richard Nixon's 1969 speech during the Apollo 11 lunar landing. The goal of the video is to demonstrate the convincing deepfake technology and warn the public of the burgeoning threat of media misinformation. While the manipulation of photography has existed since the 19th century, becoming affluent during the era of motion pictures, the current state of deepfakes has become alarmingly realistic. Beginning in the late 90's, computer scientists began experimenting with facial reanimation.

The National Academies of Sciences, Engineering, and Medicine (NASEM) are now assembling an ad hoc committee to identify emerging scientific and technological advances from across a broad range of disciplines that the U.S. Environmental Protection Agency's (EPA) Office of Research and Development (ORD) should consider in its research planning to support EPA's mission for protecting human health and the environment. In addition, according to NASEM, the committee will recommend how ORD could best take advantage of those advances to meet current and future challenges during the next 10 - 20 years. NASEM states that the committee will consider EPA's mission, strategic planning documents, and current initiatives, as well as other broader topics, including, but not limited to, biotechnology, big data, climate impacts, environmental monitoring and sensors, impacts of stressors on ecological and human health, and artificial intelligence and machine learning. The committee also will consider advances that help EPA better incorporate systems thinking into multimedia, multidisciplinary approaches. Nominations for committee members and reviewers are due August 5, 2020.

Technology is now evolving at such a rapid pace that annual predictions of trends can seem out-of-date before they even go live as a published blog post or article. As technology evolves, it enables even faster change and progress, causing an acceleration of the rate of change, until eventually, it will become exponential. Technology-based careers don't change at the same speed, but they do evolve, and the savvy IT professional recognizes that his or her role will not stay the same. And an IT worker of the 21st century will constantly be learning (out of necessity if not desire). What does this mean for you?

The government's highest priority investment in artificial intelligence needs to be its AI workforce. It is not adopting AI as quickly as the private sector, and potentially as quickly as our adversaries. Most government teams developing AI solutions we have met face high barriers when they begin a project. They include limited access to data sets, constrained system authorities, and less computing power than they need. As a result, projects are slower and more expensive than they might be, delaying the fielding of systems that can decrease costs, increase capabilities, and help improve national security.An educated, trained, and empowered AI workforce can act as a catalyst, enabling the government to create and adopt AI capabilities far more quickly and effectively than it does now.

This article is co-written together with Syed Nazmus Sadat who Studies Forestry and Environmental Science at Shahjalal University of Science & Technology, Sylhet in Bangladesh. How can artificial intelligence help in efforts to prevent deforestation? Deforestation has an incredibly adverse impact on planet earth. The forests cover close to a third of the land area on our planet and provide us with purer air and fresher water. Eighty percent of the world's land based wildlife live in forests [1].