Interpretability is one of the most difficult challenges in modern machine learning solutions. While building sophisticated machine learning models is getting easier, understanding how models develop knowledge and arrive to conclusions remains a very difficult challenge. Typically, the more accurate the models the harder they are to interpret. The release of AI Explainability 360 is the first practical implementation of ideas outlined in dozens of research papers in the last few years. In the same way traditional software applications incorporate instrumentation code to help its runtime monitoring, machine learning models need to add interpretability techniques to facilitate debugging, troubleshooting and versioning.

From Shakespeare to Austen, many writers will go down in history for their unique and engaging storytelling. But writers in the future may have competition when it comes to their stories, poems and articles - in the form of an artificial intelligence sytem. Tech firm OpenAI has developed a text generator, which many claim is now almost as good as a human writer. The system, dubbed GPT-2, was trained using a dataset of eight million web pages. This rigorous training means the AI can churn out text in different styles, ranging from Shakespeare poems to news articles.

Reinforcement learning, the AI training technique that's brought to fruition systems capable of defeating world poker champions and guiding self-driving cars, isn't the simplest thing in the world to wrangle. That's particularly true in the gaming domain, where cutting-edge approaches sometimes require bespoke tools that aren't publicly available. In a paper recently published on the preprint server Arxiv.org, At its core, it's a collection of environments and algorithms for research in general reinforcement learning and search and planning in games, with tools to analyze learning dynamics and other common evaluation metrics. "The purpose of OpenSpiel is to promote general multiagent reinforcement learning across many different game types, in a similar way as general game-playing but with a heavy emphasis on learning and not in competition form," wrote the researchers.

People who call companies to ask questions about their cable bills or complain about their Internet service being out are increasingly talking to artificial intelligence. Natural language processing, a subset of A.I. that helps computers understand speech, has become good enough that it's being used to listen and respond to basic customer questions. Over the past year, Google, Amazon and business software firm Twilio have all ramped up their marketing of A.I.-powered software for call centers. Their sales pitch is that the technology can answer basic customer questions, leaving more complicated questions and complaints to human agents. The tech firms say this A.I. software can also help existing call center staff quickly retrieve relevant customer information when they are chatting with customers, but it should be noted that the technology will likely result in companies eliminating human workers as they look for ways to automate customer service.

Automation has been at the forefront of global accountancy for decades, and professionals are constantly hungry for new tech innovations that will enable them to improve effectiveness and efficiency levels for clients. Yet while demand has continued to grow for the availability of smarter and more dynamic accounting systems, adoption has been pretty slow – particularly when it comes to artificial intelligence (AI). It looks like that's finally starting to change. Computer scientists have been tinkering with AI for more than half a century, and the concept itself is quite simple. By utilising a series of defined algorithms, AI-powered applications are able to draw from big data sources and use deep, machine learning functionality to process data, perform high-volume tasks, recognise patterns and even make decisions – and while that may sound like space-age science fiction to some practitioners, AI-powered apps and modules have already started to make a serious impact across the global accounting sector.

More than ever before, the most consequential question an IT organization must answer about every new data center workload is where to run it. The newest enterprise computing workloads today are variants of machine learning, or AI, be it deep learning-model training or inference (putting the trained model to use), and there are already so many options for AI infrastructure that finding the best one is hardly straight-forward for an enterprise. There's a variety of AI hardware options on the market, a wide and quickly growing range of AI cloud services, and various data center options for hosting AI hardware. One company that's in the thick of it across this entire machine learning infrastructure ecosystem is Nvidia, which not only makes and sells most processors for the world's AI workloads (the Nvidia GPUs), it also builds a lot of the software that runs on those chips, sells its own AI supercomputers, and, more recently, prescreens data center providers to help customers find ones that are able to host their AI machines. Related: Is Your Data Center Ready for Machine Learning Hardware? We recently sat down with Charlie Boyle, senior director of marketing for Nvidia's DGX AI supercomputer product line, to talk about the latest trends in AI hardware and the broader world of AI infrastructure.

Sex and age have long been known to affect the ECG. Several biologic variables and anatomic factors may contribute to sex and age-related differences on the ECG. We hypothesized that a convolutional neural network (CNN) could be trained through a process called deep learning to predict a person's age and self-reported sex using only 12-lead ECG signals. We further hypothesized that discrepancies between CNN-predicted age and chronological age may serve as a physiological measure of health. We trained CNNs using 10-second samples of 12-lead ECG signals from 499 727 patients to predict sex and age.

The human brain has been the main inspiration for artificial intelligence(AI) researchers for decades. The quest for recreating cognitive capabilities of the brain in deep neural networks remains one of the elusive goals of AI. From that perspective, the intersection of AI and neuroscience could be considered one of the most fascinating areas of research in modern science. However, most of the current work in neural networks has been constrained to emulate the synaptic connections between neurons in the different cortex of the brain. As neural network evolves, other human cognitive skills that are becoming increasingly relevant.

Daniel heads up Product Management for RingCentral in EMEA. He has spent his career at RingCentral driving the expansion of cloud Business Communication solutions in the UK and EMEA via the adoption of exciting, innovative features and services as well as their integration into customers' workflows. He has spent the last 8 years in the cloud software and communication industries and his background is in software development and telecoms infrastructure.

AI for Health will be the first of many meetings, as we are launching our cluster: a think tank reflecting on health, AI and its future applications. The think tank will be divided into multiple working groups tackling various topics ranging from big data, blockchain, deep learning to ethics or best practices in health.