Did an artificial-intelligence system beat human doctors in warning the world of a severe coronavirus outbreak in China? But what the humans lacked in sheer speed, they more than made up in finesse. Early warnings of disease outbreaks can help people and governments save lives. In the final days of 2019, an AI system in Boston sent out the first global alert about a new viral outbreak in China. But it took human intelligence to recognize the significance of the outbreak and then awaken response from the public health community.

"We're trying to change how people run agronomic research. Instead of establishing a small field plot, running statistics, and publishing the means, what we're trying to do involves the farmer far more directly. We are running experiments with farmers' machinery in their own fields. We can detect site-specific responses to different inputs. And we can see whether there's a response in different parts of the field," says Nicolas Martin, assistant professor in the Department of Crop Sciences at Illinois and co-author of the study.

Microsoft announced the investment plans for Mexico in an official press release. The announcement comes a month after Microsoft CEO Satya Nadela expressed his vision to "power broad economic growth through tech intensity" at Davos WEF 2020. He had said that Microsoft will ensure that this economic growth is inclusive. Mexico is now part of this inclusive global digital revolution. Mexico's digital revolution roadmap includes Microsoft's Cloud Services allocated from the local datacenters.

With bottom-up and top-down approaches, the report predicts the viewpoint of various domestic vendors in the whole market and offers the market size of the Machine Learning in Communication market. The analysts of the report have performed in-depth primary and secondary research to analyze the key players and their market share. Further, different trusted sources were roped in to gather numbers, subdivisions, revenue and shares. The research study encompasses fundamental points of the global Machine Learning in Communication market, from future prospects to the competitive scenario, extensively. The DROT and Porter's Five Forces analyses provides a deep explanation of the factors affecting the growth of Machine Learning in Communication market.

Even though you're reading this article in English, there's a good chance it might not be your mother tongue. Of the billion-strong English speakers in the world, only 33% consider it their native language. The popularity of a language depends greatly on utility and geographic location. Additionally, how we measure the spread of world languages can vary greatly depending on whether you look at total speakers or native speakers. Today's detailed visualization from WordTips illustrates the 100 most spoken languages in the world, the number of native speakers for each language, and the origin tree that each language has branched out from.

Event datasets are sequences of events of various types occurring irregularly over the time-line, and they are increasingly prevalent in numerous domains. Existing work for modeling events using conditional intensities rely on either using some underlying parametric form to capture historical dependencies, or on non-parametric models that focus primarily on tasks such as prediction. We propose a non-parametric deep neural network approach in order to estimate the underlying intensity functions. We use a novel multi-channel RNN that optimally reinforces the negative evidence of no observable events with the introduction of fake event epochs within each consecutive inter-event interval. We evaluate our method against state-of-the-art baselines on model fitting tasks as gauged by log-likelihood. Through experiments on both synthetic and real-world datasets, we find that our proposed approach outperforms existing baselines on most of the datasets studied.

We study generalised linear regression and classification for a synthetically generated dataset encompassing different problems of interest, such as learning with random features, neural networks in the lazy training regime, and the hidden manifold model. We consider the high-dimensional regime and using the replica method from statistical physics, we provide a closed-form expression for the asymptotic generalisation performance in these problems, valid in both the under- and over-parametrised regimes and for a broad choice of generalised linear model loss functions. In particular, we show how to obtain analytically the so-called double descent behaviour for logistic regression with a peak at the interpolation threshold, we illustrate the superiority of orthogonal against random Gaussian projections in learning with random features, and discuss the role played by correlations in the data generated by the hidden manifold model. Beyond the interest in these particular problems, the theoretical formalism introduced in this manuscript provides a path to further extensions to more complex tasks.

BOSTON – Did an artificial-intelligence system beat human doctors in warning the world of a severe coronavirus outbreak in China? But what the humans lacked in sheer speed, they more than made up in finesse. Early warnings of disease outbreaks can help people and governments save lives. In the final days of 2019, an AI system in Boston sent out the first global alert about a new viral outbreak in China. But it took human intelligence to recognize the significance of the outbreak and then awaken response from the public health community.

The main pillar of the plan is focused on accelerating Mexico's digital transformation through democratizing the access to technology. The company announced plans to establish a new cloud datacenter region in Mexico to deliver its intelligent and trusted cloud services to serve Mexico's public entities, organizations and Mexican society, including Microsoft Azure, Office 365, Dynamics 365 and the Power Platform. This datacenter region is an important part of Microsoft's $1.1 billion investment plan in Mexico over the next five years. The plan also includes a robust education and skilling program with different initiatives the first one being the creation of three laboratories and a virtual classroom, in collaboration with public universities to create an education platform for digital skills, to expand employability in future generations. The first initiative of the commitment to apply artificial intelligence to create societal impact is an investment in the project "Artificial Intelligence to Monitor Pelagic Sharks in the Mexican Pacific Ocean" (Shark ID), focused on the conservation of Mako shark species, driven by Mexico Azul, as part of the initiative AI for Earth, creating societal impact.

Publications, citations, conference papers, awards, patents and investment are all indicators of innovation in a given field. While there is no perfect measure, we chose the peer-reviewed publications in AI journals as a compromise between history of data, completeness, reliability and coherence. This video shows the trends of artificial intelligence innovation worldwide by country based on this measure, from the AI Index report 2019. Artificial intelligence, machine learning and deep learning in particular are transforming all industries enabling people to perform tasks better and faster, make better decisions, optimizing processes, or automating tasks among others. With the fast growth in compute power and data availability, complex algorithms can learn and extract information from huge amounts of data - big data - that humans cannot.