In a world-first, a team of Griffith University researchers has used an artificial intelligence method to better predict RNA secondary structures, with the hope it can be developed into a tool to better understand how RNAs are implicated in various diseases such as cancer. Professor Yaoqi Zhou, Professor Kuldip Paliwal, Ph.D. student Jaswinder Singh and Dr. Jack Hanson from Griffith's Institute for Glycomics and Signal Processing Laboratory led the research, which has been published in Nature Communications. In all forms of life, ribonucleic acid (RNA) is essential for the coding, decoding, regulation and expression of genes. RNA and DNA are among the four major macromolecules in lifeforms. The team employed the use of deep learning--a subset of artificial intelligence used to create complex, numerical functions to approximate specific tasks automatically without explicit human instructions--to build a more accurate model of the relationship between RNA sequence and structure.

It was recently reported that Google's AI system can accurately diagnose 26 skin conditions on par with US board-certified dermatologists. Results were published in the paper titled "A Deep Learning System for Differential Diagnosis of Skin Disease." The British Skin Foundation presents an equally extensive list. Google's AI System can only detect 37 percent of skin conditions using the list of 70 referenced above. So, if AI is so smart, why can't it detect all the skin conditions?

Lee Se-dol, a former international champion of the game Go, has announced his retirement from competitive play. He became world-famous after losing a set of matches to AlphaGo, the matchplaying software developed by Google's DeepMind artificial intelligence unit. Until 2016, Go was considered so complex that software might be incapable of mastering it. There are almost an infinite number of gameplay combinations, giving humans -- with their instincts and gut feel for where the game is going -- an advantage over machines. But, "with the debut of AI in Go games, I've realized that I'm not at the top even if I become the No. 1 through frantic efforts," Lee told the Yonhap News Agency. "Even if I become the No. 1, there is an entity that cannot be defeated."

The new HPE Apollo 6500 Gen10 is a groundbreaking server designed to tackle the most compute-intensive HPC and deep learning workloads. With superior speed, density, and performance, HPE is reinventing what it means to compute. A major transformation is happening now, as technological advancements and escalating volumes of diverse data drive change across all industries. Cutting-edge innovations are fueling digital transformation on a global scale, and organizations are leveraging faster, more powerful machines to operate more intelligently and effectively than ever. Today, HPE announced the new HPE Apollo 6500 Gen10 server, a groundbreaking platform designed to tackle the most compute-intensive high performance computing (HPC) and deep learning workloads.

Fonixa uses artificial intelligence, deep learning and voice-emotion technology to transcribe and analyse up to 5,000 hours of customer calls and scans them for emotional patterns to identify happy, sad and angry customers, get insights and improve their customer's experience. This solution can help you eliminate the time and money needed to screen calls manually, and fully automate the process with usable data insights Ubility uses predictive analytics, artificial intelligence and machine learning to help customer support agents quickly find the knowledge base articles and methods they need to support a customer reducing resolution times from minute/hours to seconds, what's more, is it even suggest how you should be talking to each customer in a way that relates to them Dynamics 365 Field Service uses the power of predictive analytics and machine learning to Automate and improve scheduling to dispatch the right technician and get the most value out of your resources Dynamics 365 Field Service uses predictive analytics and machine learning to help empower technicians with a 360-degree view of your customers and real-time guidance to improve resolution time and earn customer trust. Fonixa uses artificial intelligence, deep learning and voice-emotion technology to transcribe and analyse up to 5,000 hours of customer calls and scans them for emotional patterns to identify happy, sad and angry customers, get insights and improve their customer's experience. Ubility uses predictive analytics, artificial intelligence and machine learning to help customer support agents quickly find the knowledge base articles and methods they need to support a customer reducing resolution times from minute/hours to seconds, what's more, is it even suggest how you should be talking to each customer in a way that relates to them Dynamics 365 Field Service uses predictive analytics and machine learning to help empower technicians with a 360-degree view of your customers and real-time guidance to improve resolution time and earn customer trust.

The automatic algorithms for perform at least as good as the average embryologist for blastocyst grading and indirectly for predicting fetal heart beat as described above. Developing the algorithm based on time-lapse sequences lead to an improved accuracy compared to using only still images. Training of deep learning algorithms is only based on raw image sequences and requires no prior knowledge of embryology. Thus, the algorithm learns by itself to extract the temporal and the morphological features that are most important for prediction of blastocyst grading. It is important to note that in order to design and train a deep neural network, a substantial amount of data (in this case image sequences) is required.

It feels like this man needs no introduction, but for anyone who doesn't know who Demis Hassabis is, here's the lowdown. He's the cofounder and chief executive of the London-headquartered DeepMind AI lab, which was acquired by Google in 2014 for £400m. Prior to DeepMind, Hassabis had his own computer games company called Elixir Studios, but his passion for games goes way back. He was a chess master at the age of 13 and the second-highest-rated under 14 player in the world at one time. Catherine Breslin is a machine learning scientist and consultant based in Cambridge.

It has been observed that customers end up abandoning their purchase because often the product results displayed turns out irrelevant. Through AI organizations are trying to display customer-centric search results that are relevant to their desired ask. Yandex, a popular search engine, successfully implemented some advanced applications of NLP and Deep Learning to optimize future searches with the help of the data of previous searches. This turned out to be a massive success as they were able to increase their click-through rates by almost ten percent. Clarifai is trying to improve ecommerce by building smarter applications which can see the world as people would.