The purpose of this article is to explain Artificial Neural Network (ANN) and Long Short-Term Memory Recurrent Neural Network (LSTM RNN) and enable you to use them in real life and build the simplest ANN and LSTM recurrent neural network for the time series data. The CBOE Volatility Index, known by its ticker symbol VIX, is a popular measure of the stock market's expectation of volatility implied by S&P 500 index options. It is calculated and disseminated on a real-time basis by the Chicago Board Options Exchange (CBOE). The VOLATILITY S&P 500 data set can be downloaded from here, I set the date range from Feb 11, 2011 to Feb 11, 2019. Our goal is to predict VOLATILITY S&P 500 time series using ANN & LSTM. And load the data into a Pandas dataframe.

The creators of a revolutionary AI system that can write news stories and works of fiction – dubbed "deepfakes for text" – have taken the unusual step of not releasing their research publicly, for fear of potential misuse. OpenAI, an nonprofit research company backed by Elon Musk, says its new AI model, called GPT2 is so good and the risk of malicious use so high that it is breaking from its normal practice of releasing the full research to the public in order to allow more time to discuss the ramifications of the technological breakthrough. At its core, GPT2 is a text generator. The AI system is fed text, anything from a few words to a whole page, and asked to write the next few sentences based on its predictions of what should come next. The system is pushing the boundaries of what was thought possible, both in terms of the quality of the output, and the wide variety of potential uses.

In 2015, car-and-rocket man Elon Musk joined with influential startup backer Sam Altman to put artificial intelligence on a new, more open course. They cofounded a research institute called OpenAI to make new AI discoveries and give them away for the common good. Now, the institute's researchers are sufficiently worried by something they built that they won't release it to the public. The AI system that gave its creators pause was designed to learn the patterns of language. It does that very well--scoring better on some reading-comprehension tests than any other automated system.

The ever-growing field of autonomous driving constantly yields fresh research to explore, innovative technology to test, and new skills to learn. With so much ground to cover, just figuring out where to begin can be a daunting task. Developers at GTC Silicon Valley can learn the latest skills in AI at our expert-led DLI labs.NVIDIA That's why at this year's NVIDIA GPU Technology Conference in San Jose, experts will be on-site to provide industry-leading expertise on the foremost topics in self-driving vehicle development. Attendees can learn how to build AI applications for autonomous vehicles in hands-on, instructor-led training offered by the NVIDIA Deep Learning Institute (DLI). Developers can explore how to build on NVIDIA DRIVE AGX and NVIDIA DriveWorks with the guidance of a DLI certified instructor.

If you've noticed an uptick in product recommendations based on your Amazon purchases, or GPS services that are increasingly accurate in displaying congested traffic areas, it's because artificial intelligence (AI) is everywhere. AI adoption in organizations has tripled in the past year, and AI is a top priority for CIOs. Yet early AI initiatives have a high probability of failure due to misalignment with business requirements and lack of agility. "Although the potential for success is enormous, delivering business impact from AI initiatives takes much longer than anticipated," says Chirag Dekate, senior director analyst at Gartner. "IT leaders should plan early and use agile techniques to increase relevance and success rates."

Can those memories serve to enhance retention of learnings and help-built connections or patterns we could not do in a conscious state? What is also intriguing is the work being done in projects like "Deep Dream" which uses a convolutional neural network to find and enhance patterns in images via algorithmic pareidolia, thus creating a dream-like hallucinogenic appearance in the deliberately over-processed images. We may indeed be in the cusp of the "next big thing". But this is not new, AI pretty much has been at the forefront of all major conversations pertaining to technology over the past few years. From self-driving cars to intuitive virtual assistants, AI has been turning every science-fiction trope into reality.

Sage, a purveyor of business and accounting software, conducted several artificial intelligence (AI) surveys where 43% of respondents in a US survey, and 47% of respondents in a UK survey said that they had no idea about what AI was about. Vendors are rushing myriad AI solutions to market before the ultimate decision-makers and buyers are up to speed on what they need. For CIOs and technology leaders, the plot further thickens when they are asked by the CEO about the specifics of what AI can accomplish--and how AI differs from machine learning and deep learning. Very quickly, these technology leaders recognize that they need to put AI and its various subcategories (e.g., machine learning, deep learning) into practice--and into a common business vocabulary that everyone can understand. The first step is communicating what the definitions are for AI, machine learning (ML), and deep learning.

"Many a true word hath been spoken in jest." "O, beware, my lord, of jealousy; It is the green-ey'd monster, which doth mock The meat it feeds on." "There was a star danced, and under that was I born." Who can write like Shakespeare? Or even just spell like Shakespeare?

When it comes to deep learning frameworks, TensorFlow is one of the most preferred toolkits. However, one framework that is fast becoming the favorite of developers and data scientists is PyTorch. PyTorch is an open source project from Facebook which is used extensively within the company. For a long time, Facebook developers used another homegrown framework called Caffe2, which was adopted by academia and researchers. Last year, Facebook announced that it is merging the efforts of developing Caffe2 and PyTorch to focus on creating a unified framework that is accessible to the community.

Uber continues its spree of deep learning technology releases. Since last year, the Uber AI Labs team has open sourced different frameworks that enable many of the fundamental building blocks of deep learning solutions. The productivity of the Uber engineering team is nothing short of impressive: Pyro is a framework for probabilistic programming built on top of PyTorch, Horovod is a Tensor-Flow based framework for distributed learning, Manifold focused on visual debugging and interpretability and, of course, Michelangelo is a reference architecture for large scale machine learning solutions. The latest creation of Uber AI Labs is Ludwig, a toolbox for training deep learning models without writing any code. Training is one of the most developer intensive aspects of deep learning applications.