Success at Red Ventures is built entirely on our innovative, in-house technology development and the data storage & management capabilities our Fortune 500 partners have come to expect. We're backed by the same people who invested in GoDaddy, Dell, Uber and other tech giants, and we're building prototypes covering everything from online behavioral tracking software to artificial intelligence applications. On a day-to-day basis, our team processes 200 database instances and 5 Petabytes of data in support of our worldwide operations. Oh yeah – And we're expected to grow more than 30% by the end of 2016, which means our team is growing. AJ joined Red Ventures in 2005 as a web developer before catapulting to his current position in senior leadership.

A research team from Beth Israel Deaconess Medical Center (BIDMC) and Harvard Medical School (HMS) recently developed artificial intelligence (AI) methods aimed at training computers to interpret pathology images, with the long-term goal of building AI-powered systems to make pathologic diagnoses more accurate. "Our AI method is based on deep learning, a machine-learning algorithm used for a range of applications including speech recognition and image recognition," explained pathologist Andrew Beck, MD, PhD, Director of Bioinformatics at the Cancer Research Institute at Beth Israel Deaconess Medical Center (BIDMC) and an Associate Professor at Harvard Medical School. "This approach teaches machines to interpret the complex patterns and structure observed in real-life data by building multi-layer artificial neural networks, in a process which is thought to show similarities with the learning process that occurs in layers of neurons in the brain's neocortex, the region where thinking occurs." The Beck lab's approach was recently put to the test in a competition held at the annual meeting of the International Symposium of Biomedical Imaging (ISBI), which involved examining images of lymph nodes to decide whether or not they contained breast cancer. The research team of Beck and his lab's post-doctoral fellows Dayong Wang, PhD and Humayun Irshad, PhD, and student Rishab Gargya, together with Aditya Khosla of the MIT Computer Science and Artificial Intelligence Laboratory, placed first in two separate categories, competing against private companies and academic research institutions from around the world. The research team today posted a technical report describing their approach to the arXiv.org

Twitter has bought London-based AI startup Magic Pony Technology for a reported 150m ( 102m) as the company moves to strengthen its position in image-sharing, video and live video. Founded in 2014, Magic Pony uses machine learning to build improved systems for visual processing. The company said it was excited to be joining forces with Twitter "to improve the visual experiences that are delivered across their apps". Twitter's chief executive, Jack Dorsey, said Magic Pony's technology would be used to enhance live and video offerings and "opens up a whole lot of exciting creative possibilities for Twitter". Dorsey said the team included "11 PhDs with expertise across computer vision, machine learning, high-performance computing and computational neuroscience".

Toyota Motor Corp is targeting developing in the next five years driver assistance systems that integrate artificial intelligence (AI) to improve vehicle safety, the head of its advanced research division said. Gill Pratt, CEO of recently set up Toyota Research Institute (TRI), the Japanese automaker's research and development company that focuses on AI, said it aims to improve car safety by enabling vehicles to anticipate and avoid potential accident situations. Toyota has said the institute will spend 1 billion over the next five years, as competition to develop self-driving cars intensifies. Earlier this month, home rival Honda Motor Co said it was setting up a new research body which would focus on artificial intelligence, joining other global automakers which are investing in robotics research, including Ford and Volkswagen AG . "Some of the things that are in car safety, which is a near-term priority, I'm very confident that we will have some advances come out during the next five years," Pratt told reporters late last week in comments embargoed for Monday.

As in other industries, business leaders in the automotive and financial-services industries have an urgent need for trusted and actionable real-world insights that can help them know and serve their customers better while enabling rapid innovation. Too often, however, executives have had to operate with uncertain, incomplete, and inconsistent information. Now advances in artificial intelligence (AI) have made the construction of data-based real-world models and simulations a reality. A 2015 Tech Pro Research survey indicated that 24 percent of businesses across industries are currently using AI or had plans to do so within the year. While the health-care sector has been among the leading adopters of AI, financial-services and automotive companies are also increasingly turning to assisted, augmented, and autonomous intelligence.

We now have at least three warnings about Apple (NASDAQ:AAPL) over the last few months that have been validated by recent events. In April, before Apple released earnings, we noted that social data aggregator LikeFolio had put out a sell rating on the stock based on their analysis of social media data, and we presented a couple ways for concerned shareholders to hedge if they wanted to stay long (Hedging Apple Ahead Of Earnings). When we plot "purchase intent mentions" for Apple on a 30 day moving average, we see a steady erosion over the past 4-6 months. We also have seen FAR lower purchase intent mentions surrounding Apple's new product releases than prior (even comparably minor) product releases. In an article last month (Apple: The Next BlackBerry?), we shared Marco Arment's warning about Apple slipping behind in AI (artificial intelligence).

We have been hearing predictions for decades of a takeover of the world by artificial intelligence. In 1957, Herbert A. Simon predicted that within 10 years a digital computer would be the world's chess champion. That didn't happen until 1996. And despite Marvin Minsky's 1970 prediction that "in from three to eight years we will have a machine with the general intelligence of an average human being," we still consider that a feat of science fiction. The pioneers of artificial intelligence were surely off on the timing, but they weren't wrong; AI is coming.

Twitter's famous blue bird is getting some equine company. The messaging platform announced today (June 20) that it acquired Magic Pony Technology, a startup based in London that applies machine learning to video, for an undisclosed sum. Its acquisition looks like Twitter's latest attempt to reverse its slumping fortunes by boosting its efforts in the increasingly hot artificial intelligence space. Investors appear relieved: Twitter stock was up 1.5% in early trading. But don't expect this deal to produce yet another digital assistant with an anodyne female moniker.

Twitter has bought London-based AI startup Magic Pony Technology for a reported 150m ( 102m) as the company moves to strengthen its position in image-sharing, video, and live video. Founded in 2014, Magic Pony uses machine learning to build improved systems for visual processing. The company said it was excited to be joining forces with Twitter "to improve the visual experiences that are delivered across their apps". Twitter's chief executive, Jack Dorsey, said Magic Pony's technology would be used to enhance live and video offerings and "opens up a whole lot of exciting creative possibilities for Twitter". Dorsey said the team included "11 PhDs with expertise across computer vision, machine learning, high-performance computing and computational neuroscience".

A pony is joining the flock. Twitter has acquired Magic Pony Technology, a London-based startup focused on machine learning and visual technology, Twitter CEO Jack Dorsey announced Monday. The less than 2-year-old startup has been building and training neural networks to process and improve the visualization of video. The team has created a system to improve low-resolution video and has demonstrated it for live video game streaming, MIT Technology Review reported this year. That type of video conversion technology is key for Twitter as it focuses on live video.