SCOTTSDALE, AZ--(Marketwired - September 21, 2016) - ReplyBuy, a next-generation sales & commerce platform that combines mobile messaging, payments & instant gratification for buyers revealed new functionality earlier today as the first company to introduce a form of Artificial Intelligence to the sports and entertainment vertical. The new V.I.P. style concierge service called ReplyBuy.ai Already enjoyed by organizations within the NBA, NHL, MLS, NFL and major universities, ReplyBuy wants to help industry partners and their affiliates meet consumers where they are communicating. We're also firm believers in what the future holds for Artificial Intelligence and concierge based services," says Josh Manley, ReplyBuy Founder & CEO. "We're laser-focused on introducing products with a DNA of increased speed, efficiency and consumer engagement from a B2B perspective.

It's difficult to predict how artificial intelligence technology will change over the next 10 to 20 years, but there are plenty of gains to be made. By 2018, robots will supervise more than 3 million human workers; by 2020, smart machines will be a top investment priority for more than 30 percent of CIOs. Everything from journalism to customer service is already being replaced by AI that's increasingly able to replicate the experience and ability of humans. What was once seen as the future of technology is already here, and the only question left is how it will be implemented in the mass market. Over time, the insights gleaned from the industries currently taking advantage of AI -- and improving the technology along the way -- will make it ever more robust and useful within a growing range of applications.

Now envision a million of these pixels-a megapixel's worth-in an array that covers a thumbnail. Take one more mental trip: dive down onto the surface of the semiconductor hosting all of these pixels and marvel at each pixel's associated tech-mesh of more than 1,000 integrated transistors, which provide each and every pixel with a tiny reprogrammable brain of its own. That is the vision for DARPA's new Reconfigurable Imaging (ReImagine) program. "What we are aiming for," said Jay Lewis, program manager for ReImagine, "is a single, multi-talented camera sensor that can detect visual scenes as familiar still and video imagers do, but that also can adapt and change their personality and effectively morph into the type of imager that provides the most useful information for a given situation." This could mean selecting between different thermal (infrared) emissions or different resolutions or frame rates, or even collecting 3-D LIDAR data for mapping and other jobs that increase situational awareness.

Diego Oppenheimer is worried that the Googles and the Facebooks will dominate the world of artificial intelligence. Elon Musk and Sam Altman are worried about the same thing. That's why they created a startup called OpenAI. In recent years, Google and Facebook have snapped up so many researchers at the heart of the deep learning movement, an AI movement that's rapidly reinventing everything from speech recognition to security. So, Musk and Altman grabbed several top AI researchers from Google and Facebook and vowed to share their work with the world at large. Now, Oppenheimer and his startup, Algorithmia, are doing their part in the battle against AI hegemony.

Green and Hogan had hypothesized that pauses in children's speech, as they struggled to either find a word or string together the motor controls required to produce it, were a source of useful diagnostic data. So that's what Gong and Guttag concentrated on. They identified a set of 13 acoustic features of children's speech that their machine-learning system could search, seeking patterns that correlated with particular diagnoses. These were things like the number of short and long pauses, the average length of the pauses, the variability of their length, and similar statistics on uninterrupted utterances.

Modern technology has brought about an explosion of data. We now produce about 2.5 quintillion bytes of it every day, with 90pc of all the data in the world estimated to have been created in the last two years. The internet, of course, has been the major catalyst for this development, enabling us to create, share and store information on a massive scale. And, as mobile phone ownership continues to increase (70pc of the world's population is forecast to have one by next year – up from 61pc in 2013) and the internet of things evolves as everything from fitness trackers to fridges come online, there's only going to be more of it. But having this wealth of data is only part of the equation.

Are you good at hiding your feelings? No issues, your Wi-Fi router may soon be able to tell how you feel, even if you have a good poker face. A team of researchers at MIT's Computer Science and Artificial Intelligence Laboratory (CSAIL) have developed a device that can measure human inner emotional states using wireless signals. Dubbed EQ-Radio, the new device measures heartbeat, and breath to determine whether a person is happy, excited, sad, or angry. Using EQ-Radio, which emits and captures reflected radio frequency (RF) waves, the team bounced waves off a person's body to measure subtle changes in breathing patterns and heart rates.

A few weeks ago, Moz CEO Rand Fishkin approached MonkeyLearn team with a question which later turned into a project. The goal was to build an online tool that provides great value to the SEO industry. Also, we wanted to showcase what can be developed with machine learning technologies by using MonkeyLearn. Basically, SEOs can use this tool to compare their website's keywords to those on the Google search results for a related term. Randy presented this Keyword Comparison Extractor on his keynote at Mozcon 2015, the largest SEO conference out there, with more than 1,500 attendees and speakers from companies like Google, Buffer, Optimizely, Unbounce, Basecamp and others.

Government agencies are tasked with the challenge of providing citizens with more efficient, effective, and transparent services with strict and often decreasing budgets. Government agencies can use machine learning to increase operational efficiencies by analyzing datasets, finding patterns and anomalies, and making predictions about future events. Skytree's state-of-the art machine learning software can analyze both structured and unstructured data sets in real-time to produce fast, accurate and scalable results that are up to 10,000 times faster than previous approaches. Skytree comes with a breadth of advanced machine learning methods that utilize the research available to you to make predictions with the highest accuracy available, far surpassing what's possible with basic analytics. Detect and prevent fraudulent transactions, accounts and vendors.

In developing algorithms to differentiate between suspicious nodules in the thyroid gland, researchers in China have found that their machine-learning computations separate malignant from benign properties more accurately than an inexperienced radiologist--but not as accurately as the experienced radiologist whose know-how was used to create the algorithms. Their research is running in the October edition of the American Journal of Roentgenology. Dr. Hongxun Wu of Jiangyuan Hospital in the province of Jiangsu and colleagues worked with 970 histopathologically proven thyroid nodules in 970 patients. They had two radiologists retrospectively review ultrasound images of the nodules, grading them according to a five-tier scoring system. One of the rads--the one whose clinical interpretations would feed the computations--had 17 years of experience.