Brooklyn, New York, June 23, 2021 (GLOBE NEWSWIRE) -- According to a new market research report published by Global Market Estimates, the Global Artificial Intelligence in Cement Production Market is projected to grow at a CAGR value of 28.5% during the forecast period [2021 to 2026]. The rising need for automation, as this industry is yet to transform into digitalization, increasing need to the rising cost of manual procedures, and rising need from the end-user industry for high-quality cement will help the AI in cement production market to grow rapidly. Browse 151 Market Data Tables and 111 Figures spread through 181 Pages and in-depth TOC on "Global Artificial Intelligence in Cement Production Market - Forecast to 2026"

Breakthroughs in machine learning in the last decade have led to `digital intelligence', i.e. machine learning models capable of learning from vast amounts of labeled data to perform several digital tasks such as speech recognition, face recognition, machine translation and so on. The goal of this thesis is to make progress towards designing algorithms capable of `physical intelligence', i.e. building intelligent autonomous navigation agents capable of learning to perform complex navigation tasks in the physical world involving visual perception, natural language understanding, reasoning, planning, and sequential decision making. Despite several advances in classical navigation methods in the last few decades, current navigation agents struggle at long-term semantic navigation tasks. In the first part of the thesis, we discuss our work on short-term navigation using end-to-end reinforcement learning to tackle challenges such as obstacle avoidance, semantic perception, language grounding, and reasoning. In the second part, we present a new class of navigation methods based on modular learning and structured explicit map representations, which leverage the strengths of both classical and end-to-end learning methods, to tackle long-term navigation tasks. We show that these methods are able to effectively tackle challenges such as localization, mapping, long-term planning, exploration and learning semantic priors. These modular learning methods are capable of long-term spatial and semantic understanding and achieve state-of-the-art results on various navigation tasks.

If you're worried that robots are coming for your job, you can relax -- unless you're a manager. A new survey-based study explains how automation is reshaping the workplace in unexpected ways. Robots can improve efficiency and quality, reduce costs, and even help create more jobs for their human counterparts. But more robots can also reduce the need for managers. The study is titled "The Robot Revolution: Managerial and Employment Consequences for Firms."

The Techunting LinkedIn Robot is a valuable resource tasked with reducing work for business executives. This is a great opportunity that allows these people to focus on more valuable activities. In this article we will show you essential aspects about its creation and some tips for the pandemic era. If you want to know what this tool is about, uses and growth in markets, continue with us. Here we provide you with valuable information that can help you understand the reasons for its emergence and importance.

As we noted at the beginning of this series on the AI bank of the future, disruptive AI technologies can dramatically improve banks' performance in four key areas: higher profits, at-scale personalization, smart omnichannel experiences, and rapid innovation cycles. The stakes could not be higher, and success requires a holistic transformation spanning all layers of the organization's capability stack. Our previous articles have focused on the capability stack's technology layers: reimagined engagement, 1 1. Leveraging these capabilities to create value requires an operating model combining structure, talent, culture, and ways of working to synchronize all layers of the stack. Synchronizing these layers is not easy. Any organization undertaking an AI-bank transformation must determine how to structure the organization so that its people interact and leverage tools and capabilities to deliver value for each customer at scale. In this article, we take a closer look at the need for a platform operating model, the categories and scope of operating models, and the building blocks of effective models.

OECD.AI is an inclusive hub for public policy on AI that helps countries encourage, nurture and monitor the development and use of trustworthy AI. From the measurement of AI trends and developments to the direction and impact of national and regional AI policies and initiatives, OECD.AI is a prime example of how to move the AI discussion from principles to practice. Its up-to-date repository of over 600 AI policy initiatives from 60 countries enables the comparison of key elements of national AI policies in an interactive manner. Its work and indicators have informed and enhanced national and international analysis such as Pan Canadian AI Strategy Impact Assessment, the German AI Observatory, the G20 background paper on Trustworthy AI in Health multiple G20 reports and the recent EC Proposal for AI Regulation. Armando Guio, CAF Consultant at the Presidency of the Republic of Colombia believes that "the Observatory has rapidly become one of the most important sources of data and knowledge for AI governance."

Shapley values provide model agnostic feature attributions for model outcome at a particular instance by simulating feature absence under a global population distribution. The use of a global population can lead to potentially misleading results when local model behaviour is of interest. Hence we consider the formulation of neighbourhood reference distributions that improve the local interpretability of Shapley values. By doing so, we find that the Nadaraya-Watson estimator, a well-studied kernel regressor, can be expressed as a self-normalised importance sampling estimator. Empirically, we observe that Neighbourhood Shapley values identify meaningful sparse feature relevance attributions that provide insight into local model behaviour, complimenting conventional Shapley analysis. They also increase on-manifold explainability and robustness to the construction of adversarial classifiers.

Outgoing Secretary-General of the Organisation for Economic Co-operation and Development (OECD) ... [ ] Angel Gurria applauds as new Secretary-General of the Organisation for Economic Cooperation and Development (OECD) Mathias Cormann, of Australia, takes over at the OECD headquarters in Paris, Tuesday, June, 1 2021. A recent study from the Pew Research Center showed that 53% of people in 20 countries feel that artificial intelligence has been a good thing for society. While over half the world's population has a positive view of AI, this means that one in every three people in these countries are concerned about the impacts AI can have on society. How do we ensure that AI is trustworthy and its benefits are shared by all? As the statistics show, while there is incremental improvement, there is still a level of hesitancy and suspicion towards AI among the citizens around the world.

In November 2020, Alphabet-owned AI firm DeepMind announced that it had cracked one of biology's trickiest problems. For years the company had been working on an AI called AlphaFold that could predict the structure of proteins – a challenge that could prove pivotal for developing drugs and vaccines, and understanding diseases. When the results of the biennial protein-predicting challenge CASP were announced at the end of 2020, it was immediately clear that AlphaFold had swept the floor with the competition. John Moult, a computational biologist at the University of Maryland who co-founded the CASP competition, was both astonished and excited at AlphaFold's potential. "It was the first time a serious scientific problem had been solved by AI," he says.

Brooklyn, New York, June 22, 2021 (GLOBE NEWSWIRE) -- According to a new market research report published by Global Market Estimates, the Global Artificial Intelligence in Banking Market is projected to grow at a CAGR value of 24.5% during the forecast period of 2021 to 2026. The rising launch of advanced technologies such as AI-based core banking software for retail and commercial banks, also with increasing demand for hassle-free online and mobile banking services, and the increasing trend of offering customer-centric services will drive the market from 2021 to 2026. Browse 151 Market Data Tables and 111 Figures spread through 181 Pages and in-depth TOC on "Global Artificial Intelligence in Banking Market - Forecast to 2026"