Since artificial neural networks allow modeling of nonlinear processes, they have turned into a very popular and useful tool for solving many problems such as classification, clustering, regression, pattern recognition, dimension reduction, structured prediction, machine translation, anomaly detection, decision making, visualization, computer vision, and others. This wide range of abilities makes it possible to use artificial neural networks in many areas. In this article, we discuss applications of artificial neural networks in Natural Language Processing tasks (NLP). NLP includes a wide set of syntax, semantics, discourse, and speech tasks. We will describe prime tasks in which neural networks demonstrated state-of-the-art performance.

A scientist in the US has developed a "fast" and "accurate" artificial intelligence system that can classify echocardiogram results using deep-learning algorithm. Rima Arnaout, from the University of California San Francisco, said the AI system is capable of analysing heart scans faster and more accurately than human cardiologists. However, speaking to IEEE Spectrum, she warned that the technology won't replace doctors, but could help double-check scans, speed-up evaluation and, perhaps, highlight factors the doctors may have missed. Currently, the tool is limited because it can only evaluate echocardiograms - ultrasounds of the heart. "The best technique is still inside the head of the trained echocardiographer," she said.

A look under the hood of any major search, commerce, or social-networking site today will reveal a profusion of "deep-learning" algorithms. Over the past decade, these powerful artificial intelligence (AI) tools have been increasingly and successfully applied to image analysis, speech recognition, translation, and many other tasks. Indeed, the computational and power requirements of these algorithms now constitute a major and still-growing fraction of datacenter demand. Designers often offload much of the highly parallel calculations to commercial hardware, especially graphics-processing units (GPUs) originally developed for rapid image rendering. These chips are especially well-suited to the computationally intensive "training" phase, which tunes system parameters using many validated examples.

Hewlett Packard Enterprise (HPE) today announced new offerings to help customers ramp up, optimize and scale artificial intelligence (AI) usage across business functions to drive outcomes such as better demand forecasting, improved operational efficiency and increased sales. PricewaterhouseCoopers predicts the global GDP to grow 14 percent – the equivalent of $15.7 trillion – by 2030 as a result of AI, with increased labor productivity and consumer demand being the most impactful business outcomes.(2) However, while AI holds great promise, current adoption rates are low. According to Gartner's 2018 CIO Agenda Survey, four percent of CIOs globally have implemented AI, while a further 46 percent have developed plans to do so.(3) "Global tech giants are investing heavily in AI, but the majority of enterprises are struggling both with finding viable AI use cases and with building technology environments that support their AI workloads. As a result, the gap between leaders and laggards is widening," said Beena Ammanath, Global Vice President, Artificial Intelligence, HPE Pointnext.

Nvidia launched hardware and software improvements to its deep learning computing platform that deliver a 10 times performance boost on deep learning workloads compared with the previous generation six months ago. In the past five years, programmers have made huge advances in AI, first by training deep learning neural networks based on existing data. This allows a neural network to recognize an image of a cat, for instance. The second step is inferencing, or applying the learning capability to new data that has never been seen before, like spotting a cat in a picture that the neural network has never been shown. At the GPU Technology Conference (GTC) event in San Jose, California, Nvidia CEO Jensen Huang didn't announce a new graphics processing unit (GPU).

Nvidia Corp. has advanced deep learning techniques, but now it's looking to take AI technology into new areas: Putting self-driving cars into virtual reality instead of our roads, and setting its sights on Hollywood and hospitals. Over the past few years, Nvidia has made inroads into equipping cars with the computer hardware that gives them self-driving capability. That move has become so crucial that Nvidia NVDA, -7.76% shares fell more than 6% in recent trading as the company kicked off its GPU Technology Conference in San Jose, Calif., after it confirmed that it is suspending real-world testing following a recent fatality in Arizona in one of Uber Technologies Inc.'s self-driving cars. In his keynote address Tuesday morning, Chief Executive Jensen Huang did not mention the halt, but did show off a potential solution to the problem of testing self-driving automobiles on public roads. Huang showed off a simulator that can allow companies to test their self-driving systems in a virtual environment, providing opportunity to drive billions of miles in a year without endangering pedestrians.

In December 2017, a user named "DeepFakes" posted realistic looking explicit videos of famous celebrities on Reddit. He generated these fake videos using deep learning, the latest in AI, to insert celebrities' faces into adult movies. In the following weeks, the internet exploded with articles about the dangers of face swapping technology: harassing innocents, propagating fake news, and hurting the credibility of video evidence forever. It's true that bad actors will use this technology for harm; but given that the genie is out of the bottle, shouldn't we pause to consider what else DeepFakes could be used for? In this post, I explore the capabilities of this tech, describe how it works, and discuss potential applications.

Health care applications of machine learning and AI have been in the news a bit more than usual recently, concurrent with the recent Healthcare Information and Management Systems Society (HIMSS) conference in Las Vegas. HIMSS is a 45,000 attendee conference dedicated to health care IT. Surprising no one, AI was a major theme at this year's event. There was a whole sub-conference focused on ML and AI, plus a ton of AI-focused sessions in the regular conference and a good number of announcements by industry leaders and startups alike. I've only done a couple of health care-focused shows on my podcast so far, but I'm planning to dive into this area more deeply this year.

The recent successes of AI have captured the wildest imagination of both the scientific communities and the general public. Robotics and AI amplify human potentials, increase productivity and are moving from simple reasoning towards human-like cognitive abilities. Current AI technologies are used in a set area of applications, ranging from healthcare, manufacturing, transport, energy, to financial services, banking, advertising, management consulting and government agencies. The global AI market is around 260 billion USD in 2016 and it is estimated to exceed 3 trillion by 2024. To understand the impact of AI, it is important to draw lessons from it's past successes and failures and this white paper provides a comprehensive explanation of the evolution of AI, its current status and future directions.

With super-resolution optical microscopy, it is now possible to observe molecular interactions in living cells. The obtained images have a very high spatial precision but their overall quality can vary a lot depending on the structure of interest and the imaging parameters. Moreover, evaluating this quality is often difficult for non-expert users. In this work, we tackle the problem of learning the quality function of superresolution images from scores provided by experts. More specifically, we are proposing a system based on a deep neural network that can provide a quantitative quality measure of a STED image of neuronal structures given as input. We conduct a user study in order to evaluate the quality of the predictions of the neural network against those of a human expert. Results show the potential while highlighting some of the limits of the proposed approach. In order to understand cellular mechanisms and their related disorders, we need to improve our knowledge of the molecular components making those cells, on their spatial dynamics, and on their signaling interactions inside subcellular compartments. These processes, occurring at the nanoscale, can now be observed in living cells thanks to recent breakthroughs in optical methods which led to the development of optical super-resolution microscopy.