New Delhi, Aug 12: The youth today are more likely to trust an artificial intelligence-run system than one controlled by humans, and they believe the "fractures" in society are manifestations of an underlying political problem, a World Economic Forum (WEF) report has claimed. The report was launched as part of a drive to mark the International Youth Day, which is celebrated on August 12. The WEF drive, called a'Youth-Driven Recovery Plan', saw participation of over two million young people from across countries, including India, who shared their views on what will become the next normal for society, government, and business. Artificial Intelligence is Creating New Opportunities Which Could Not Be Achieved by Traditional Technology, Say Experts. According to the survey, young people believe there is a serious crisis in politics, especially with regards to climate change policies, and income inequality.

Artificial intelligence is already capable of doing some incredibly useful things, like predicting flooding, diagnosing disease, and instantly translating languages. Advances in neural networks coupled with enormous computational power have allowed tech companies to create incrementally smarter AI models over the last decade. Jeff Dean, Google's AI chief, thinks we're just scratching the surface. Speaking at the TED conference in Monterey, California, this week, he revealed that Google is developing a nimble, multi-purpose AI that can perform millions of tasks. Called Pathways, Google's solution seeks to centralize disparate AI into one powerful, all-knowing algorithm.

When you think about solving the climate crisis, what springs to mind? Most people's knee-jerk reaction is along the lines of "electrification," "carbon sequestration," "recycling," or "renewable agriculture." While not many think of phrases like "big data" or "artificial intelligence," several recent conversations have convinced me how important these fields are to helping our civilization thrive and survive into the next century. The two founder / CEOs with whom I have had the pleasure to speak recently use AI in very different ways and in completely different fields, but it is clear that the ubiquity of cheap computing power, combined with smart engineers and focused, visionary entrepreneurs represents a formidable force in helping us mitigate and adapt to today's harsher, more challenging post-climate world. The companies featured in this article are Clir and SINAI Technologies.

TU Delft is a top tier university and is exceedingly active in the field of Artificial Intelligence. The AIFluids lab was recently established to foster the use of AI in the Aerospace Sciences. Designing more efficient aircrafts and wind farms requires a deeper understanding of complex flows. The AIFluids Lab is focused on two major challenges of fluid mechanics: the prediction and the control of complex, transitional and turbulent flows. New experimental techniques and high-fidelity flow simulations are providing larger and more detailed datasets.

We propose a method that efficiently learns distributions over articulation model parameters directly from depth images without the need to know articulation model categories a priori. By contrast, existing methods that learn articulation models from raw observations typically only predict point estimates of the model parameters, which are insufficient to guarantee the safe manipulation of articulated objects. Our core contributions include a novel representation for distributions over rigid body transformations and articulation model parameters based on screw theory, von Mises-Fisher distributions, and Stiefel manifolds. Combining these concepts allows for an efficient, mathematically sound representation that implicitly satisfies the constraints that rigid body transformations and articulations must adhere to. Leveraging this representation, we introduce a novel deep learning based approach, DUST-net, that performs category-independent articulation model estimation while also providing model uncertainties. We evaluate our approach on several benchmarking datasets and real-world objects and compare its performance with two current state-of-the-art methods. Our results demonstrate that DUST-net can successfully learn distributions over articulation models for novel objects across articulation model categories, which generate point estimates with better accuracy than state-of-the-art methods and effectively capture the uncertainty over predicted model parameters due to noisy inputs.

Machine learning on sets towards sequential output is an important and ubiquitous task, with applications ranging from language modelling and meta-learning to multi-agent strategy games and power grid optimization. Combining elements of representation learning and structured prediction, its two primary challenges include obtaining a meaningful, permutation invariant set representation and subsequently utilizing this representation to output a complex target permutation. This paper provides a comprehensive introduction to the field as well as an overview of important machine learning methods tackling both of these key challenges, with a detailed qualitative comparison of selected model architectures.

Bayesian optimization (BO) is a flexible and powerful framework that is suitable for computationally expensive simulation-based applications and guarantees statistical convergence to the global optimum. While remaining as one of the most popular optimization methods, its capability is hindered by the size of data, the dimensionality of the considered problem, and the nature of sequential optimization. These scalability issues are intertwined with each other and must be tackled simultaneously. In this work, we propose the Scalable$^3$-BO framework, which employs sparse GP as the underlying surrogate model to scope with Big Data and is equipped with a random embedding to efficiently optimize high-dimensional problems with low effective dimensionality. The Scalable$^3$-BO framework is further leveraged with asynchronous parallelization feature, which fully exploits the computational resource on HPC within a computational budget. As a result, the proposed Scalable$^3$-BO framework is scalable in three independent perspectives: with respect to data size, dimensionality, and computational resource on HPC. The goal of this work is to push the frontiers of BO beyond its well-known scalability issues and minimize the wall-clock waiting time for optimizing high-dimensional computationally expensive applications. We demonstrate the capability of Scalable$^3$-BO with 1 million data points, 10,000-dimensional problems, with 20 concurrent workers in an HPC environment.

Kevin Yager (front) and Masafumi Fukuto at Brookhaven Lab's National Synchrotron Light Source II, where they've been implementing a method of autonomous experimentation. In the popular view of traditional science, scientists are in the lab hovering over their experiments, micromanaging every little detail. For example, they may iteratively test a wide variety of material compositions, synthesis and processing protocols, and environmental conditions to see how these parameters influence material properties. In each iteration, they analyze the collected data, looking for patterns and relying on their scientific knowledge and intuition to select useful follow-on measurements. This manual approach consumes limited instrument time and the attention of human experts who could otherwise focus on the bigger picture.

This forecast is part of the Stocks Under 10 Dollars Package, as one of I Know First's forecast services. Package Name: Stocks Under $10 Recommended Positions: Long Forecast Length: 1 Year (8/9/20 – 8/10/21) I Know First Average: 98.79% Several predictions in this 1 Year forecast saw significant returns. The algorithm had correctly predicted 10 out of 10 stock movements. The prediction with the highest return was AR, at 250.64%. KIRK and RAIL also performed well for this time horizon with returns of 229.82% and 176.35%, respectively.

In the age of big data, data processing and analytics are fundamental, ubiquitous, and crucial to many organizations which undertake a digitalization journey to improve and transform their businesses and operations. Data analytics typically entails other key operations such as data acquisition, data cleansing, data integration, modeling, etc., before insights could be extracted. Big data can unleash significant value creation across many sectors such as health care and retail[56]. However, the complexity of data (e.g., high volume, high velocity, and high variety) presents many challenges in data analytics and hence renders the difficulty in drawing meaningful insights. To tackle the challenge and facilitate the data processing and analytics efficiently and effectively, a lot of algorithms and techniques have been designed and numerous learning systems have also been developed by researchers and practitioners such as Spark MLlib[63], and Rafiki[104]. To support fast data processing and accurate data analytics, a huge number of algorithms rely on rules that are developed based on human knowledge and experience. For example, Shortest-job-first is a scheduling algorithm that chooses the job with the smallest execution time for the next execution. However, without fully exploiting characteristics of the workload, it can achieve inferior performance compared to DRL-based scheduling algorithm [58].