Machine learning techniques have been successfully applied to super-resolution tasks on natural images where visually pleasing results are sufficient. However in many scientific domains this is not adequate and estimations of errors and uncertainties are crucial. To address this issue we propose a Bayesian framework that decomposes uncertainties into epistemic and aleatoric uncertainties. We test the validity of our approach by super-resolving images of the Sun's magnetic field and by generating maps measuring the range of possible high resolution explanations compatible with a given low resolution magnetogram.

We present a response to the 2018 Request for Information (RFI) from the NITRD, NCO, NSF regarding the "Update to the 2016 National Artificial Intelligence Research and Development Strategic Plan." Through this document, we provide a response to the question of whether and how the National Artificial Intelligence Research and Development Strategic Plan (NAIRDSP) should be updated from the perspective of Fermilab, America's premier national laboratory for High Energy Physics (HEP). We believe the NAIRDSP should be extended in light of the rapid pace of development and innovation in the field of Artificial Intelligence (AI) since 2016, and present our recommendations below. AI has profoundly impacted many areas of human life, promising to dramatically reshape society --- e.g., economy, education, science --- in the coming years. We are still early in this process. It is critical to invest now in this technology to ensure it is safe and deployed ethically. Science and society both have a strong need for accuracy, efficiency, transparency, and accountability in algorithms, making investments in scientific AI particularly valuable. Thus far the US has been a leader in AI technologies, and we believe as a national Laboratory it is crucial to help maintain and extend this leadership. Moreover, investments in AI will be important for maintaining US leadership in the physical sciences.

Despite outstanding contribution to the significant progress of Artificial Intelligence (AI), deep learning models remain mostly black boxes, which are extremely weak in explainability of the reasoning process and prediction results. Explainability is not only a gateway between AI and society but also a powerful tool to detect flaws in the model and biases in the data. Local Interpretable Model-agnostic Explanation (LIME) is a recent approach that uses a linear regression model to form a local explanation for the individual prediction result. However, being so restricted and usually oversimplifying the relationships, linear models fail in situations where nonlinear associations and interactions exist among features and prediction results. This paper proposes an extended Decision Tree-based LIME (TLIME) approach, which uses a decision tree model to form an interpretable representation that is locally faithful to the original model. The new approach can capture nonlinear interactions among features in the data and creates plausible explanations. Various experiments show that the TLIME explanation of multiple blackbox models can achieve more reliable performance in terms of understandability, fidelity, and efficiency.

A recent market intelligence report that is published by Data Insights Partner on the global Artificial Intelligence in Healthcare Application market makes an offering of in-depth analysis of segments and sub-segments in the regional and international Artificial Intelligence in Healthcare Application market. The research also emphasizes on the impact of restraints, drivers, and macro indicators on the regional and global Artificial Intelligence in Healthcare Application market over the short as well as long period of time. A detailed presentation of forecast, trends, and dollar values of global Artificial Intelligence in Healthcare Application market is offered. In accordance with the report, the global Artificial Intelligence in Healthcare Application market is projected to expand at a CAGR of 35% over the period of forecast. One of the greatest challenges in the healthcare arena is huge healthcare data and it is impossible to handle all of these data for medical personnel. Here comes the opportunity of artificial intelligence (AI) in healthcare field.

"I am very interested in what is happening in Latin America and I think (in) Brazil there is an awful lot of quite smart investment," he added, noting that he thinks Europe is being a bit slow. The latest research report titled "Artificial Intelligence in Retail Market" published by Industry Research expects AI in the retail market to grow globally at a compound annual growth rate (CAGR) of over 35 percent from 2019 to 2024. The report also notes that the growing trend of rising technology adoption in the industry can be associated with the need for streamlining retail operations, minimising efforts, and increasing revenue mostly for e-commerce retailers. The application of artificial intelligence (AI), big data and analytics will enable businesses with a data-driven model by expanding the types of data that can be analysed and raise the level of sophistication of the resulting insight, according to the report. Owen Farrow, Leader Consumer Industry at IBM Middle East told Zawya on the sidelines of the event that the UAE particularly is becoming a more common place to talk to people about AI and stands out for leading with experience.

"I am very interested in what is happening in Latin America and I think (in) Brazil there is an awful lot of quite smart investment," he added, noting that he thinks Europe is being a bit slow. The latest research report titled "Artificial Intelligence in Retail Market" published by Industry Research expects AI in the retail market to grow globally at a compound annual growth rate (CAGR) of over 35 percent from 2019 to 2024. The report also notes that the growing trend of rising technology adoption in the industry can be associated with the need for streamlining retail operations, minimising efforts, and increasing revenue mostly for e-commerce retailers. The application of artificial intelligence (AI), big data and analytics will enable businesses with a data-driven model by expanding the types of data that can be analysed and raise the level of sophistication of the resulting insight, according to the report. Owen Farrow, Leader Consumer Industry at IBM Middle East told Zawya on the sidelines of the event that the UAE particularly is becoming a more common place to talk to people about AI and stands out for leading with experience.

We show that consistency of Statistical EL knowledge bases, as defined by Penaloza and Potyka in SUM 2017 [4] is ExpTime-hard. Together with the existing ExpTime upper bound by Baader in FroCos 2017 [1], the result leads to the ExpTime-completeness of the mentioned logic. Our proof goes via a reduction from consistency of EL extended with an atomic negation, which is known to be equivalent to the well-known ExpTime-complete description logic ALC.

Note: By submitting this form, you agree to Third Door Media's terms. Google is making the largest change to its search system since the company introduced RankBrain, almost five-years ago. The company said this will impact 1 in 10 queries in terms of changing the results that rank for those queries. BERT started rolling out this week and will be fully live shortly. It is rolling out for English language queries now and will expand to other languages in the future.

As artificial intelligence (AI) and autonomous machines become more common in agriculture, the industry is going through enormous changes. Ofir Schlam, CEO and co-founder of Taranis, a leading precision agriculture intelligence platform, recently shared more information about these changes in an interview. Taranis is an AI-powered agriculture intelligence platform that was selected to be part of John Deere's startup collaborator. It uses sophisticated computer vision, data science and deep learning algorithms to enable farmers to make informed decisions. The platform is capable of monitoring fields and finding early symptoms of uneven emergence, weeds, nutrient deficiencies, disease or insect infestations, water damage and equipment issues.

Volute, a technology company developing new enterprise workflow solutions, today announced the appointment of a new member to its Board of Directors – Enrique Shadah, Chief Operating Officer at Filtered.ai and Head of Venture Relations at MIT's LinQ Program for Biomedical Innovation. Volute, a technology company developing new enterprise workflow solutions, today announced the appointment of a new member to its Board of Directors – Enrique Shadah, Chief Operating Officer at Filtered.ai and Head of Venture Relations at MIT's LinQ Program for Biomedical Innovation. "On behalf of Volute and the Board of Directors, I am pleased to welcome Mr. Shadah to the Board," stated Michael Croft, Founder and Chief Executive Officer of Volute. "Enrique has an impressive track record of working with thought leaders and trending technologies that bring innovations to market by marquee corporations as well as startups in numerous industries." "Michael is highly attuned to large organizations needs to increase productivity of knowledge workers through better workflow. This insight coupled with his deep understanding of technology, is the foundation on which Volute is building a leading position in the field of intelligent workflow and process automation. I look forward to working with Michael and the Volute team to accelerate progress towards this goal," Mr. Shadah added.