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Tractian Raises $15 Million Series A for Its Machine Operations Platform Led by Next47

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Tractian, a machine intelligence company offering one of the most advanced industrial monitoring systems on the market, announced $15 million in Series A funding led by Next47, a global venture capital firm specializing in building category-defining B2B technology businesses. YCombinator and other previous investors also participated in the round. The new capital will allow the company to consolidate its position in the global market by extending operations from Brazil to Mexico and the U.S. and continuing rapid development of industry-leading products. "We know the industries that empower their frontline workers with best-in-class productivity tools have superpowers compared to others, and Tractian appears as the right arm of maintenance managers to manage their routines around the world" Tractian has developed streamlined hardware-software solutions designed to give maintenance technicians and decision-makers comprehensive oversight of their operations. With ease of installation and quick value generation at the heart of its customer approach, Tractian is democratizing access to sophisticated monitoring and analytics.


How to create a land cover model for South America in 4 steps

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Recently, Radiant Earth Foundation released a land cover dataset for South America, continuing the work they had been doing in other parts of the world and in connection with other areas of interest. In connection with previous posts, this article explains how to train a segmentation model based on this dataset in just 4 steps. Specifically, we will explain in detail how to train a model for classifying the use of cropland, based on the mentioned dataset. The released dataset comprises labels and satellite imagery from Sentinel-1, Sentinel-2 and Landsat 8 missions for classifying the uses of South American land (if you would like to learn more about satellite imagery sources, click here). Each pixel is identified as one of the possible seven land classes: water, natural bare ground, artificial bare ground, woody vegetation, cultivated ground, semi-cultivated ground, and permanent snow/ice.


Scientists Develop a Machine Learning Model to Predict the Evolution of an Epidemic Accurately - CBIRT

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According to a new KAUST study, machine learning approaches can achieve an assumption-free analysis of epidemic case data with amazingly good prediction accuracy and the flexibility to incorporate new data dynamically. Yasminah Alali, an intern in KAUST's 2021 Saudi Summer Internship (SSI) program, developed a proof of concept that reveals a possible alternative to traditional parameter-driven mechanistic models by removing human bias and assumptions from analysis, revealing the underlying story of the data. Using publicly released COVID-19 incidence and recovery data from India and Brazil, Alali leveraged her experience working with artificial intelligence models to design a framework to fit the characteristics and time-evolving nature of epidemic data in collaboration with KAUST's Ying Sun and Fouzi Harrou. To create an effective Gaussian process regression (GPR) based model for forecasting recovered and confirmed COVID-19 cases in two significantly impacted countries, India and Brazil, the researchers first used Bayesian optimization to modify the Gaussian process regression (GPR) hyperparameters. However, the time dependency in the COVID-19 data series is ignored by machine learning models.


Artificial intelligence to understand plant resilience in harsh environments

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The Atacama Desert, located in South America, is one of the driest regions on Earth. Several types of endemic plants are still present at the site. After collecting several species that grow between 2,400 and 4,500 meters above sea level, scientists from INRAE, Purdue University and the Pontifical Catholic University of Santiago in Chile have been able to identify common molecular markers that allow an understanding of the mechanisms of these plants' resilience in the face of a harsh environment. The researchers used an innovative approach using artificial intelligence. The results of their work are detailed in review The new botany.


Can machine learning help predict disease spread?

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Machine learning techniques can provide an assumption-free analysis of epidemic case data with surprisingly good prediction accuracy and the ability to dynamically incorporate the latest data, a new KAUST study has shown. The proof of concept developed by Yasminah Alali, a student in KAUST's 2021 Saudi Summer Internship (SSI) program, demonstrates a promising alternative approach to conventional parameter-driven mechanistic models that removes human bias and assumptions from analysis and shows the underlying story of the data. Working with KAUST's Ying Sun and Fouzi Harrou, Alali leveraged her experience working with artificial intelligence models to develop a framework to fit the characteristics and time-evolving nature of epidemic data using publicly reported COVID-19 incidence and recovery data from India and Brazil. "My major at college was artificial intelligence, and I previously worked on a medical project using various ML algorithms," says Alali. "Working with Professor Sun and Dr Harrou during my internship, we considered whether the Gaussian Process Regression method would be useful for predicting pandemic spread because it gives confidence intervals for the predictions, which can greatly assist decision-makers." Accurate forecasting of cases during a pandemic is essential to help mitigate and slow transmission.


Senior Machine Learning Engineer, Matching

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Beat is one of the most exciting companies to ever come out of the ride-hailing space. One city at a time, all across the globe we make transportation affordable, convenient, and safe for everyone. We also help hundreds of thousands of people earn extra income as drivers. Today we are the fastest-growing ride-hailing service in Latin America. But serving millions of rides every day pales in comparison to what lies ahead.


SQL to SARIMAX: How I navigate the first time-series analysis personal project for my portfolio

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The diagnostics plot for this particular model shows a decently good fit . When being used for prediction, it followed the real trend closely.


'We need to be much more diverse': More than half of data used in health care AI comes from the U.S. and China

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As medicine continues to test automated machine learning tools, many hope that low-cost support tools will help narrow care gaps in countries with constrained resources. But new research suggests it's those countries that are least represented in the data being used to design and test most clinical AI -- potentially making those gaps even wider. Researchers have shown that AI tools often fail to perform when used in real-world hospitals. It's the problem of transferability: An algorithm trained on one patient population with a particular set of characteristics won't necessarily work well on another. Those failures have motivated a growing call for clinical AI to be both trained and validated on diverse patient data, with representation across spectrums of sex, age, race, ethnicity, and more.


Oportunidad de Empleo: Data Scientist

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We are a well-known technology company based in Cordoba since 2004. As experts, we specialize in supporting organizations in the adoption and implementation of technologies. We provide agile and innovative responses to the growing and dynamic market demand. Our Head Office is located in Cordoba and we have landed in Chile since 2020 Industry 4.0 and digital transformation are revolutionizing the way value is created in all industries and this poses a great challenge for all organizations. "We make life easier for organizations with technology" Our technological solutions: -Conversational Virtual Assistants -Whapp (Omnichannel commercial Platform) -KunING Tech (Software Engineering) -Remote DBA (Remote Database Administration) These Tools are developed with the latest technologies such as Big data and analytics, artificial intelligence, machine learning, Software engineering and CRM.


Data Engineer, ML Platform

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Change.org is searching for a Data Engineer to help in the development of our next-generation ML platform to support all of our internal machine learning operational needs at large scale. We're a social impact business (a public benefit company), and the largest tech platform focused on civic action in the world with 80m monthly users, 50,000 campaigns launched on the site every month, 150 staff, and a new revenue model that has grown by 500% in 2 years. We're growing quickly, and our users win campaigns for change once every hour. From strengthening hate crime legislation in South Africa; fighting corruption in Indonesia, Italy, and Brazil; to fighting violence against women in India. We are looking for a Data Engineer who has a passion to learn and build ML workflow orchestration & distributed data processing at scale.