The first-ever image of a supermassive black hole needed approximately 4.5 petabytes of astronomy data. Researchers connected a series of highly-sophisticated telescopes around the world to create one virtual, Earth-sized telescope.
What does the future of autonomous vehicles truly look like? While no one can predict the future, we turned to three industry experts steeped in the data and technology around autonomous vehicles to understand the trends they’re seeing in the industry today — and the implications for the future.
With edge computing, machine learning and IoT sensors, the autonomous vehicle of the future will be able to move at standard speeds and make decisions in seconds rather than minutes or hours.
Engineers and environmentalists at a Boston-based company have been working since 2015 to tackle the challenges of efficiency and sustainability plaguing data centers today.
Disappearances of plant species are part of a global trend: A decrease in plant and animal biodiversity. Biodiversity is a critical component of the survival of any ecosystem. The growing availability of data storage and increasingly sophisticated machine learning techniques might be able to help.
We know we have to give up some information to companies in exchange for a service—like apps that help us navigate or find the perfect restaurant. In the digital world, personal data can be valuable for companies wanting to target these types of need-based services. After all, data powers the convenience we love and the more people using a service, the (potentially) better it can get by leveraging data.
Data produced by IoT devices is projected to grow exponentially as the number of devices increases. Each new data point creates valuable insights. The critical missing piece is interoperability, the seamless and secure exchange of data between connected devices.
In facial recognition technology, Yaron Gurovich, CTO at the biotechnology firm FDNA, sees the potential to identify rare genetic disorders using FDNA’s facial analysis framework, marrying machine learning with these medical diagnoses which can be very difficult.
To combat future devastation, scientists across the public and private sectors are turning to data-driven technologies and machine learning to better detect wildfires before they spread.
According to a new report, pedestrian deaths have increased 35% since 2009, and occur at a rate of 13 incidents daily. Luckily, AI-powered advanced sensor networks, traffic management technology and enhanced automobile safety systems could equip cities with cost-effective and implementable solutions for this problem.