The financial services industry has been slow to embrace deep learning for fear of this black box, Maynard said. Nobody doubts that advanced techniques, like neural networks, could help financial companies make better decisions. But, for Equifax, regulations force them to give verbal explanations for why people receive the credit scores that they do. Simply saying, "because a neural net said so," isn't good enough. So Maynard and his team have developed a new type of neural network model that gives reason codes along with scores.

The idea is to make it much easier for data scientists to use deep learning on a variety of tasks. For example, IBM said, banks could make better predictions on whether clients might default on loans or detect credit-card fraud. Manufacturers can train models with historical machine data to identify potential failures before they happen. Inventory management could be optimized based not only on point-of-sale data but also weather and other outside data. "The data scientist doesn't have to deal with provisioning or managing the server," Gupta said.

Using Keras to train deep neural networks with multiple GPUs (Photo credit: Nor-Tech.com). Keras is undoubtedly my favorite deep learning Python framework, especially for image classification. I use Keras in production applications, in my personal deep learning projects, and here on the PyImageSearch blog. I've even based over two-thirds of my new book, Deep Learning for Computer Vision with Python on Keras. However, one of my biggest hangups with Keras is that it can be a pain to perform multi-GPU training.

Today, we are excited to announce the beta release of TensorFlow in the Mesosphere DC/OS Service Catalog. Using a single command, you can now deploy distributed TensorFlow on any bare-metal, virtual, or public cloud infrastructure. As with other packages available for DC/OS, the new TensorFlow package also includes the ability to use GPUs to accelerate your machine learning and deep learning applications. In the race to leverage deep learning capabilities, data scientists specializing in deep learning are highly sought after. An efficient data science infrastructure allows you to attract the best data scientists and get the best work out of them, which gives your business a strategic advantage over competitors.

Canada has produced several big breakthroughs in artificial intelligence in recent years, and its government is keen to establish the country as a global epicenter of AI. The country's prime minister, Justin Trudeau, also hopes that the technology will learn Canadian values as it grows up. Speaking at a major AI event in Toronto today, Trudeau demonstrated an impressive enthusiasm for AI and machine learning, at one point even taking a stab at describing the concept of deep reinforcement learning, an approach that lets computers learn to do complex things that can't be programmed manually (see "10 Breakthrough Technologies 2017: Reinforcement Learning"). Both deep reinforcement learning and deep neural networks, which the method exploits, were pioneered by researchers working at Canadian universities. The country's government is now investing in big efforts to spur more AI research.

There is no shortage of neural network frameworks, libraries, and APIs available to anyone interested in getting started with deep learning. Keras is a high-level neural network API, helping lead the way to the commoditization of deep learning and artificial intelligence. It runs on top of a number of lower-level libraries, used as backends, including TensorFlow, Theano, CNTK, and PlaidML. Keras code is portable, meaning that you can implement a neural network in Keras using Theano as a backened and then specify the backend to subsequently run on TensorFlow, and no further changes would be required to your code. We have had access to these algorithms for over 10 years. To me, the most important advances have come with Google Keras, which has commoditized very powerful, modern, AI algos that previous were not only inaccessible bit [sic] thought to be unusable as well.

Demand for Artificial Intelligence (AI) expertise and technologies is being driven by compelling business value from across industries. Right from detecting a breach of privacy to enhancing operational efficiency and thwarting any potential cyber attack to offering better services to end clients, AI's role is undeniably crucial in today's corporate ecosystem. Working in this direction, Teradata, a prominent data and analytics company, is helping clients to capitalize on the power of AI to deliver high value business outcomes in the areas of fraud detection, manufacturing performance optimization, risk modeling, and precision recommendation engines. To help clients accelerate their AI initiatives, Teradata supports with data science acumen and deep learning algorithms that significantly outperform most rules-based and machine learning approaches. For example, Danske Bank worked with Teradata to create and launch a state of-the-art, AI-driven fraud detection platform expected to meet 100 percent ROI in its first year of production.

Vienna, Austria -- (SBWIRE) -- 10/30/2017 -- IFDAQ has gained acceptance into NVIDIA's Inception program, a nurture program for exceptional startups revolutionizing industries with advances in AI and data sciences. The Inception program offers customized resources that range from hardware grants to collaboration with NVIDIA's Deep Learning Institute to raising visibility for the unique value and innovations IFDAQ can bring to market using NVIDIA's deep learning platform. There lation ship also includes access to NVIDIA's latest GPU accelerators as well as a global AI and deep learning ecosystem with a massive network of deep learning experts and opinion leaders. "We are honored to be part of NVIDIA's AI-start up family that is fostered with a number of benefits, including early access to state-of-the-art technology,"commented Daryl de Jori, Head of New Technologies and founding member of IFDAQ. "Asan AI-driven technology that generates billions of data sets daily through a series of high-dimensional algorithms, the IFDAQ could also benefit by building smarter calculation processes to shorten computing times. The collaboration with NVIDIA also boosts our ability to maximize our efficiency and data-driven processes in a manner that was previously thought impossible."

Recently, I have started learning about Artificial Intelligence as it is creating a lot of buzz in industry. Within these diverse fields of AI applications, the area of vision based domain has attracted me a lot. For that, I have been experimenting with deep learning mechanisms primarily involving usage of Convolutional Neural Network(CNN). The primary thing with all the experiments I have done till date has taught me that data which is used during training plays the most important role. In fact, it will not be wrong to state that AI has emerged again (after several AI winters) only because of availability of huge computing power(GPUs) and vast amount of data in Internet.

WASHINGTON: Scientists have developed a new artificial intelligence system that can decode the human mind, and interpret what a person is seeing by analysing brain scans. The advance could aid efforts to improve artificial intelligence (AI) and lead to new insights into brain function. Critical to the research is a type of algorithm called a convolutional neural network, which has been instrumental in enabling computers and smartphones to recognise faces and objects. "That type of network has made an enormous impact in the field of computer vision in recent years," said Zhongming Liu, an assistant professor at Purdue University in the US. "Our technique uses the neural network to understand what you are seeing," Liu said.