"Many researchers … speculate that the information-processing abilities of biological neural systems must follow from highly parallel processes operating on representations that are distributed over many neurons. [Artificial neural networks] capture this kind of highly parallel computation based on distributed representations"
– from Machine Learning (Section 4.1.1; page 82) by Tom M. Mitchell, McGraw Hill Companies, Inc. (1997).]
Analytics leader SAS is helping customers gain more value from data with SAS Viya products, extending the value from the SAS Platform. These newest advances, such as embedded artificial intelligence (AI) capabilities, will further address the needs of organisations that are making analytics core to their business. A variety of industries, countries and organisation sizes have embraced SAS Viya products. With SAS, data scientists, analysts, developers, IT, domain experts and executives can all generate data-driven insights – from the same, consistent data, fostering greater collaboration and driving innovations faster. SAS continues to deliver new capabilities, such as image recognition, deep learning and natural language understanding into the SAS Platform.
Intel's hardware for accelerating AI computation is finally on its way to customers. The company announced today that its first-generation Neural Network Processor, code named "Lake Crest," will be rolling out to a small set of partners in the near future to help them drastically accelerate how much machine learning work they can do. The NNPs are designed to very quickly tackle the math that underpins artificial intelligence applications, specifically neural networks, a currently popular branch of machine learning. One of the big problems with the large, deep neural networks that are popular right now is that they can be very computationally intensive, which makes them harder to test and deploy rapidly. At first, the NNPs will only get released to a small number of intel partners who the company plans to begin outfitting before the end of this year.
One of the major factors that will have a positive impact on the growth of this market includes the rising usage of deep learning technology among various industries such as automotive, advertisement, medical and others. Moreover, increasing acceptance of cloud based technology, high usage of deep learning in big data analytics, high R&D expansions for enhanced processing hardware for deep learning and rising applicability in healthcare and autonomous vehicles are fueling the market growth. Moreover, the market has tremendous growth opportunity such as utilization of deep learning technology in smartphones and medical image analysis. Depending on application, data mining segment is anticipated to grow at a highest CAGR during the forecast period attributed to growing utilization of deep learning in cybersecurity and database systems and data analytics.
BCS Technology, a Global IT company headquartered in Australia providing end to end solutions in big data and analytics, announced the launch of their chatbot solution -- Interactive Social Airline Automated Companion (ISAAC) built on Cloudera's modern platform for machine learning and analytics optimized for the cloud -- Cloudera Enterprise. The solution combines the use of modern big data analytics technologies and natural language processing (NLP) by leveraging Microsoft's LUIS framework and the Cloudera Enterprise platform. "With Cloudera's machine learning and advanced analytics technology at the core of ISAAC, businesses can now use data to gain valuable insights, make accurate business decisions faster and deliver better products and services to enhance their customers' experiences. With the exponential growth of BCS and big data, a new subsidiary named ML Labs has formed, specialising in providing machine learning and deep learning algorithm solutions to clients looking to begin their journey through big data and analytics.
The main point is to combine mathematical operation together to form a workflow of choice. The graph takes care of evaluating the gradient of all the inputs to ease up setting up the minimizer. I have aimed for the library to be simple and transparent so that it would be easy to understand and modify to fit individual needs. Currently, supports most of the useful matrix operations, the Adam stochastic minimizer as well as modules for simplified deployment of dense, convolution and recurrent (vanilla and LSTM) networks.
Instead of preprogramming software to complete a specific task, as narrow AI does, machine learning uses algorithms that allow a computer to learn from the vast amounts of data it receives so it can complete a task on its own. International Business Machines uses deep learning powered by NVIDIA's graphics processing units (GPUs) to comb through medical images to find cancer cells. The company makes the graphics processors that are integral in AI, machine learning, and deep learning, and lots of companies already look to NVIDIA's hardware to make their AI software a reality. The Motley Fool owns shares of and recommends Alphabet (A shares), Alphabet (C shares), Amazon, Facebook, and Nvidia.
Recently, analyst Trip Chowdhry of Global Equities Research wrote in an investor note that Wal-Mart Stores (NYSE:WMT) will ramp up its focus on deep neural networks for its OneOps cloud business and that the retailer will tap NVIDIA's (NASDAQ:NVDA) graphics processing units (GPUs) to make this happen. Deep neural networks, and the broader deep learning segment, are part of a growing artificial intelligence market. Additionally, NVIDIA said in its second-quarter fiscal 2018 report that it forged new partnerships with Microsoft, Google, Tencent, IBM, Baidu, and Facebook to help them bring new deep learning and artificial intelligence services online. Aside from NVIDIA's deep-learning total addressable market, adding more of these customers is important, because the company's data center revenue segment (which includes GPU sales for deep-learning technologies) is becoming a larger part of the business.
These were then shown actual images of new gravitational lenses, and it was able to analyze the distortions 10 million times faster than traditional methods. This is significant because while the traditional method weeks or a month just to analyze one lens using computer simulations and mathematical models, the same can be done by AI in less than half a second. The SLAC study isn't the first time researchers have turned to AI to study gravitational lensing. Previous works included having a neural network identify if an image showed gravitational lensing or not.
They talk less often about one of the most profitable, and more mundane, uses for recent improvements in machine learning: boosting ad revenue. A recent research paper from Microsoft's Bing search unit notes that "even a 0.1 percent accuracy improvement in our production would yield hundreds of millions of dollars in additional earnings." Google reported $22.7 billion in ad revenue for its most recent quarter, comprising 87 percent of parent company Alphabet's revenue. Google has reported steady growth in ad revenue for many years; Microsoft has called out strong growth in Bing search ad revenue and in average revenue per search in its past five quarterly earnings releases.