We present a taxonomy of research on Machine Learning (ML) applied to enhance simulations together with a catalog of some activities. We cover eight patterns for the link of ML to the simulations or systems plus three algorithmic areas: particle dynamics, agent-based models and partial differential equations. The patterns are further divided into three action areas: Improving simulation with Configurations and Integration of Data, Learn Structure, Theory and Model for Simulation, and Learn to make Surrogates.

The evolution of artificial intelligence (AI) is one of the most important trends to watch for tech investors. More companies are jumping into the space every day, and while stock pickers still have to exercise caution and shouldn't embrace a business just because it touts an AI connection, the players that cement leading roles in this computing shift could enjoy forefront positions in the overall technology space for decades to come. Pure sales and earnings contributions aren't always front and center in earnings reports, but AI is already a big part of the growth story at many top technology companies. Investors looking to get a jump on big news in the artificial intelligence space this month might want to pay attention to Microsoft (NASDAQ: MSFT), Xilinx (NASDAQ: XLNX), and Amazon (NASDAQ: AMZN) -- three AI leaders that are expected to report earnings before October draws to a close. Microsoft has been one of the market's biggest large-cap winners in recent years, climbing roughly 200% over the last half-decade and quadrupling the S&P 500 index's rise over the stretch.

Hummingbird Technologies are a world-leading AI and machine learning business in the crop analysis space. We consolidate data from drones, planes and satellites and deliver value-driven actionable insights for farmers, agronomists and food companies. The Hummingbird platform delivers greater insight into crop health and yield potential with a range of crop-specific AI tools for earlier disease identification, optimum nutrient management, detailed plant counting, crop development modelling and yield prediction. Backed by Sir James Dyson, the European Space Agency, BASF and some of the leading tech VC and large agro businesses, Hummingbird have operations in UK, Brazil, Russia, Ukraine, Australia and North America.

One often finds in the literature connections between measures of fairness and measures of feature importance employed to interpret trained classifiers. However, there seems to be no study that compares fairness measures and feature importance measures. In this paper we propose ways to evaluate and compare such measures. We focus in particular on SHAP, a game-theoretic measure of feature importance; we present results for a number of unfairness-prone datasets.

Conformal methods create prediction bands that control average coverage under no assumptions besides i.i.d. data. Besides average coverage, one might also desire to control conditional coverage, that is, coverage for every new testing point. However, without strong assumptions, conditional coverage is unachievable. Given this limitation, the literature has focused on methods with asymptotical conditional coverage. In order to obtain this property, these methods require strong conditions on the dependence between the target variable and the features. We introduce two conformal methods based on conditional density estimators that do not depend on this type of assumption to obtain asymptotic conditional coverage: Dist-split and CD-split. While Dist-split asymptotically obtains optimal intervals, which are easier to interpret than general regions, CD-split obtains optimal size regions, which are smaller than intervals. CD-split also obtains local coverage by creating a data-driven partition of the feature space that scales to high-dimensional settings and by generating prediction bands locally on the partition elements. In a wide variety of simulated scenarios, our methods have a better control of conditional coverage and have smaller length than previously proposed methods.

Farmers today operate self-guided tractors steered by GPS, use drones to monitor crops and employ artificial intelligence in irrigation. Robots will probably take cowhands' jobs before they take yours. Agriculture is a major export business in the United States -- which has lately been a source of stress. American agricultural exports have been hampered recently by the Trump administration's trade war with China. "China was a big and important market" for farmers in the United States, said A. Blake Brown, a professor of agricultural and resource economics at North Carolina State University.

An important feature of successful supervised machine learning applications is to be able to explain the predictions given by the regression or classification model being used. However, most state-of-the-art models that have good predictive power lead to predictions that are hard to interpret. Thus, several model-agnostic interpreters have been developed recently as a way of explaining black-box classifiers. In practice, using these methods is a slow process because a novel fitting is required for each new testing instance, and several non-trivial choices must be made. We develop NLS (neural local smoother), a method that is complex enough to give good predictions, and yet gives solutions that are easy to be interpreted without the need of using a separate interpreter. The key idea is to use a neural network that imposes a local linear shape to the output layer. We show that NLS leads to predictive power that is comparable to state-of-the-art machine learning models, and yet is easier to interpret.

As the availability and importance of temporal interaction data--such as email communication--increases, it becomes increasingly important to understand the underlying structure that underpins these interactions. Often these interactions form a multigraph, where we might have multiple interactions between two entities. Such multigraphs tend to be sparse yet structured, and their distribution often evolves over time. Existing statistical models with interpretable parameters can capture some, but not all, of these properties. We propose a dynamic nonparametric model for interaction multigraphs that combines the sparsity of edge-exchangeable multigraphs with dynamic clustering patterns that tend to reinforce recent behavioral patterns. We show that our method yields improved held-out likelihood over stationary variants, and impressive predictive performance against a range of state-of-the-art dynamic graph models.

This Computer Vision report contains a chapter on the global market and all its associated companies with their profiles, which gives valuable data pertaining to their outlook in terms of finances, product portfolios, investment plans, and marketing and business strategies. The report helps you achieve your dream of an outshining and winning business. This Computer Vision market research report helps in answering many business challenges more quickly and saves your lot of time. Moreover, the report consists of all the detailed profiles for the Computer Vision market's major manufacturers and importers who are influencing the market. Market research report improves your professional reputation and adds integrity to the work you do such as refining your business plan, preparing a presentation for a key client, or making recommendations to an executive.

The adoption of AI could take a toll on women's employment, over the next ten years, says a recent report by McKinsey global institute McKinsey Global Institute report "The future of women at work: Transitions in the age of automation, published earlier this year says the adoption of AI could take a toll on women's employment. It has found that the world's ten economies, – Canada, France, Germany, Japan, the U.K., the U.S., China, India, Mexico, and South America- that collectively contribute over 60% of GDP of the world, will be severely affected by AI adoption, especially for women's employment. The report says, "an average of 20% of women working today (107 million) could find their jobs displaced by 2030. That's compared with 21% of men (163 million) in the same period". As she spoke about the research at MIT Technology Review's EmTech MIT conference at the MIT Media Lab, Krishnan said that almost 90% of the jobs that are repetitive can be automated, in about 10% of occupations.