Adaptive Vision has acquired an online dataset annotation platform, Zillin.io, to further its deep learning image processing offering. The product was developed by QZ Solutions, based in Opole, Poland. The addition of Zillin.io is an important extension of Adaptive Vision's existing portfolio of deep learning products. Zillin will complement the Adaptive Vision deep learning add-on, a set of five ready-made tools for industrial image analysis, along with the Weaver product, an inference engine for anyone's neural networks designed for processing images at high speed. By providing a comprehensive range of products, Adaptive Vision opens itself to wider cooperation with solutions providers who invest in modern technologies and require tools that make their developments more effective, faster and reliable.

Women in Big Data is spotlighting 8 amazing women on March 8th, International Women's Day. Naomi Molefe is WiBD South African Chapter Lead. I recently joined the group talent team at Discovery Holdings, an insurance business headquarted in Johannesburg with 7000 employees, operating in 19 countries with a revenue of just under $700 million (F18). My role is to assist the business to build, source and attract talent pipelines in support of business strategy and talent requirements. I work with the head of Talent Acquisition in executing strategic recruitment for senior and scarce skills.

Women in Big Data is spotlighting 8 amazing women on March 8th, International Women's Day. Naomi Molefe is WiBD South African Chapter Lead. I recently joined the group talent team at Discovery Holdings, an insurance business headquarted in Johannesburg with 7000 employees, operating in 19 countries with a revenue of just under $700 million (F18). My role is to assist the business to build, source and attract talent pipelines in support of business strategy and talent requirements. I work with the head of Talent Acquisition in executing strategic recruitment for senior and scarce skills.

DURBAN - Across the globe, trends in technology, economics and socioeconomics are culminating to disrupt the way entire industries operate and deliver products and services to consumers. When it comes to the impact of technology, there is no industry riper for disruption than the financial services sector, including insurance, which for the longest time remained trapped in outdated product development and delivery models. That has changed, and today we're seeing the pace of change and meaningful innovation in the insurance sector escalating. Not only is the existing insurance model from advice, underwriting, onboarding, risk management, servicing, and claims processing being turned on its head, but new product solutions are now possible for market sectors that have been entirely underserved and marginalised by the formal economy. Until now, most innovation in the insurance sector has been internally focused with little direct value to the customer.

There is a gender gap in artificial intelligence (AI). A study by the World Economic Forum and LinkedIn found that only 22% of AI professionals are women. Research by the AI Now Institute found that women make up only 15% of the AI research staff at Facebook and only 10% at Google. Although the gender gap in AI echoes those in cybersecurity and information technology in general, the repercussions of a lack of diversity in AI broaden because the details of the how the systems work are not fully known. As a result, identifying and correcting bias introduced by the decisions of the development teams or the data they select to train their algorithms is difficult.

This International Women's Day, we have a treat for our Women viewers; A special career advice video that will pave the path for your career transitions into Data Science, AI, Machine Learning and Data Analytics. In this video, our star women mentors from Swiggy, G2 and AIFonic Labs explain how young technology professionals can make successful career transitions into Data Science, AI/ML and Data Analytics while explaining their own inspiring career transition journeys into emerging technologies. They also share what opportunities are available, especially for women, in AI, Machine Learning, Data Science and Analytics fields. Listen to them giving anecdote-led tips on how to crack the hot data job roles along with career advice for making the career transition of your dreams. Subscribe to our channel to get updates on the latest videos.

Did you know TNW Conference has a track fully dedicated to bringing the biggest names in tech to showcase inspiring talks from those driving the future of technology this year? Tim Leberecht, who authored this piece, is one of the speakers. Check out the full'Impact' program here. On July 20, 1969, the first human landed on the moon. Fifty years later we are in desperate need for another "moonshot" to tackle some of the pressing and overwhelmingly big issues of our time -- from the climate crisis to the decline of democracy to the upheavals to our labor markets and societies caused by the rise of exponential digital technology -- especially Artificial Intelligence (AI). For the past decade, we put our faith in technology as the ultimate problem-solver, and any kind of innovation was tied to technological advances.

The future of extreme weather prediction may lie in modernizing a piece of technology from the past. Researchers recently developed a new technique to augment an old-fashioned weather forecasting method with the power of deep learning, a subset of artificial intelligence (AI). Once the deep learning system is fully trained, it is able to predict extreme weather events like heat waves and cold spells with 80% accuracy up to 5 days beforehand. "This is a very inexpensive way of predicting extreme events at least a few days ahead of time," said Ashesh Chattopadhyay, a mechanical engineering graduate student at Rice University in Houston and lead author on the project. The project began when Pedram Hassanzadeh, an assistant professor of mechanical engineering at Rice, realized that extreme weather events like heat waves and cold spells usually arise from very unusual atmospheric circulation patterns that could potentially be taught to a pattern recognition computer program.

The future of extreme weather prediction may lie in modernizing a piece of technology from the past. Researchers recently developed a new technique to augment an old-fashioned weather forecasting method with the power of deep learning, a subset of artificial intelligence (AI). Once the deep learning system is fully trained, it is able to predict extreme weather events like heat waves and cold spells with 80% accuracy up to 5 days beforehand. "This is a very inexpensive way of predicting extreme events at least a few days ahead of time," said Ashesh Chattopadhyay, a mechanical engineering graduate student at Rice University in Houston and lead author on the project. The project began when Pedram Hassanzadeh, an assistant professor of mechanical engineering at Rice, realized that extreme weather events like heat waves and cold spells usually arise from very unusual atmospheric circulation patterns that could potentially be taught to a pattern recognition computer program.

WASHINGTON: Artificial intelligence (AI) may help improve the efficiency and precision in sleep medicine, resulting in more patient-centred care and better outcomes, according to researchers. The data collected during polysomnography - the most comprehensive type of sleep study - is well-positioned for enhanced analysis through AI and machine-assisted learning, according to a statement from the American Academy of Sleep Medicine. "When we typically think of AI in sleep medicine, the obvious use case is for the scoring of sleep and associated events," said lead author and committee Chair Cathy Goldstein. "This would streamline the processes of sleep laboratories and free up sleep technologist time for direct patient care," said Goldstein, an associate professor at the University of Michigan in the US. Because of the vast amounts of data collected by sleep centres, AI and machine learning could advance sleep care, resulting in more accurate diagnoses, according to the statement published in the Journal of Clinical Sleep Medicine.