Recent advancements in the field of computer vision (CV) have led to new applications that could benefit people globally, and especially those in developing countries. To bring the CV community closer to tasks, data sets, and applications that can have a global impact, Facebook AI launched the Computer Vision for Global Challenges (CV4GC) initiative earlier this year. Through a series of academic programs, mentorships, sponsorships, and events, CV4GC brings together field experts from around the world to discuss potential CV applications to address issues that affect developing regions. One such program is the CV4GC request for proposals, a research award opportunity that launched in February with the goal of supporting research that aligns with CV4GC's mission. We were particularly interested in proposals that extended CV technology to achieve global development priorities, especially those captured in the United Nations' Sustainable Development Goals.

We address tracking and prediction of multiple moving objects in visual data streams as inference and sampling in a disentangled latent state-space model. By encoding objects separately and including explicit position information in the latent state space, we perform tracking via amortized variational Bayesian inference of the respective latent positions. Inference is implemented in a modular neural framework tailored towards our disentangled latent space. Generative and inference model are jointly learned from observations only. Comparing to related prior work, we empirically show that our Markovian state-space assumption enables faithful and much improved long-term prediction well beyond the training horizon. Further, our inference model correctly decomposes frames into objects, even in the presence of occlusions. Tracking performance is increased significantly over prior art.

Sign in to report inappropriate content. Santiago Siri, the founder of Democracy Earth, speaks about possible ways of formalizing humans on blockchain. He shows how complex this problem is and gives an overview of various approaches tackling the problem.

The Cognitive Computing Market report includes the leading advancements and technological up-gradation that engages the user to inhabit with fine business selections, define their future-based priority growth plans, and to implement the necessary actions. The global Cognitive Computing Market report also offers a detailed summary of key players and their manufacturing procedure with statistical data and profound analysis of the products, contribution, and revenue. Every information given in the report is sourced and verified by our expert team and is collated with precision. To give a broad overview of the current global market trends and strategies led by key businesses, we present the information in a graphical format such as graphs, pie-charts with the superior illustration.

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.