Today, many companies use AI and location technology to drive more accurate predictions and strengthen decision-making. Hear Joseph Sirosh, Corporate Vice President of the Cloud AI Platform at Microsoft, talk about how AI combined with location intelligence magnifies understanding and improves business. To learn more about the power of location technology for better insights and better decisions, download our free e-books Making Sense of Digital Transformation and Making the Most of the Internet of Things. We talk to business and technology leaders who share analysis, insights, and stories on data science, the Internet of Things, Smart Communities and other forces driving digital transformation and leveraging the power of location intelligence.
AICAN is a program designed by Rutgers' Art & AI Lab that is pretty much an AI artist. After looking at nearly 500 years of art history, it learned the in and outs of artistic aesthetic, and created some pieces of its own. Who knows, maybe AI art will be hanging in museums around the world one day. Subscribe for more tech & culture videos: http://on.mash.to/subscribe
The aim is clear: To help the community digitally record information to cut costs and increase yields -- with just a smartphone in their hands as AI leveraged Cloud computing to make sense of the data for farmers. India has now embarked on a journey to bring AI sensors into the fields. For Anant Maheshwari, the company's India President, Microsoft has begun empowering small-holder farmers in India to increase their income through higher crop yield and greater price control. "We are working with farmers, state governments, the Ministry of Electronics and Information Technology (MeitY) and the Ministry of Agriculture and Farmers Welfare to create an ecosystem for AI into farming," Maheshwari told IANS. In some villages in Telangana, Maharashtra and Madhya Pradesh, farmers are receiving automated voice calls that tell them whether their cotton crops are at risk of a pest attack, based on weather conditions and crop stage.
This is an incredible moment in time for medicine. We've reached a tipping point in the convergence of biomedical and digital innovation. More data was created in the last two years than the previous 5,000 years of human history. Computing power has expanded, and data architecture and quality has reached a place where we can extract meaningful insights to impact human health. Researchers and doctors have access to increasingly sophisticated forms of artificial intelligence and machine learning that have augmented their ability to decode disease.
There was a time when automation used to only work with image name or property and that was the easiest and only way to check whether assertion is true or false. Mostly in older times and using selenium Webdriver it used to be some code like this....does it sound familiar? But times are changing and there are some smart tools that can help you work swiftly with images and you do not have to use older ways. Sometimes the scripts were written covering the Alt tag case or only checking the height or width or both of the image and that used to tell whether image exists on a web form or not. But here is the changing world.
For many years, China has been struggling to tackle high pollution levels that are crippling its major cities. Indeed, a recent study by researchers at Chinese Hong Kong University has found that air pollution in the country causes an average of 1.1 million premature deaths each year and costs its economy $38 billion. Now researchers at MIT have discovered that air pollution in China's cities may be contributing to low levels of happiness amongst the country's urban population. In a paper published today in the journal Nature Human Behaviour, a research team led by Siqi Zheng, the Samuel Tak Lee Associate Professor in MIT's Department of Urban Studies and Planning and Center for Real Estate, and the Faculty Director of MIT China Future City Lab, reveals that higher levels of pollution are associated with a decrease in people's happiness levels. The paper also includes co-first author Jianghao Wang of the Chinese Academy of Sciences, Matthew Kahn of the University of Southern California, Cong Sun of the Shanghai University of Finance and Economics, and Xiaonan Zhang of Tsinghua University in Beijing.
Gradient-based optimization of absolute errors is tricky, since the gradient is "never" zero. In theory, adaptive methods should be able to damp oscillations so that it converges to the minimum. However, I found none of the'standard' methods were able to do this "out of the box". Learning rate decay could alleviate the problem, but needs manual tuning which I would rather avoid. Does anyone know of a method that can do this?
As part of the latest funding into AI with ethics, Facebook would promote the application of smart technology and data science across all industry and societal planes. After a tumultuous year in 2018, Facebook has announced a series of new policies to shake off its position on the ethical application of AI and Data Science. While the #10YearChallenge is taking its viral flight on Instagram, Facebook has decided to move an inch close to bringing'ethics' to Artificial Intelligence and related research. Yesterday, the global social media giant and personal data aggregator announced that it is partnering with the Technical University of Munich to support the incubation of an independent AI ethics research center. In the hindsight, the official blog by Facebook also revealed their ongoing efforts in bringing AI to the center of socio-economic infrastructure at a global scale.