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.
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.
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.
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.
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.
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.
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.
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