The following article has been republished with permission from Process Excellence (PEX) Network. Have you ever wondered what the other people in the industry are saying? The PEX Network often publishes surveys, and sometimes the results deserve a wider audience. The Technology Excellence (Europe) Survey 2018 was designed to get a snapshot of the industry, and while responses were weighted towards banking, financial services and insurance (24%) and manufacturing (16%), plenty of other industries were represented: gaming, logistics, and software development to name but three. Occasionally, the PEX Network throws out a broad question to get the sort of answers that you can't put on a spreadsheet -- and in particular, it likes to ask the Network to make bold predictions.
I'm trying to train an LSTM to generate song lyrics. For my input data, I downloaded a bunch of song lyrics (1-D list where each entry is one line of a song) and used Keras Tokenization w one-hot. This is is where I'm having trouble setting up the structure. Once I have converted the lyric-lines to one-hot, what should the input to the LSTM look like? When I fit the model, what should I use for my target?
Has anyone tried to use Stable-Baselines? How does it compare to the official Baselines from OpenAI in your experience? Stable Baselines is a set of improved implementations of reinforcement learning algorithms based on OpenAI Baselines. You can read a detailed presentation of Stable Baselines in the Medium article. These algorithms will make it easier for the research community and industry to replicate, refine, and identify new ideas, and will create good baselines to build projects on top of.
On September 20, 2018 at The Economist Events' Innovation Summit took place in London. The summit gathered over 150 leading thinkers and practitioners to explore insights and strategies for successfully embracing AI and machine learning to build a truly intelligent company. Artificial intelligence and machine learning have become one of the hottest topics in business and seemed to be the focus of this year's innovation summit. Watch the video broadcasted by the Economist Events to get a "big picture look at AI and what companies need to do to make the most of it with top executives" . OPENING PLENARY: DEFINING THE INTELLIGENT COMPANYA big picture look at AI and what companies need to do to make the most of it with top executives.
A/B testing is a standard step in many e-commerce companies' marketing process. With well designed A/B tests, marketers can obtain insights on where and how to maximize their marketing efforts and drive a successful campaign. However, practically speaking, standard A/B tests leave money on the table, when compared to more advanced machine-learning approaches. In this post, we will discuss the current state of A/B testing, define a few common machine-learning algorithms (Multi-Armed Bandits) used for optimizing A/B tests, and finally describe the performance of those algorithms in a few typical marketing use cases. A/B testing: How does it work?
Mention data governance to many corporate executives and you might get eye rolling and a quick change of subject. Business leaders are more focused on revenue rather than meeting regulatory demands. But the rise in enterprise data has also brought government-driven demands for privacy and protections that are forcing companies across the world to adopt tools to meet new requirements. "Governance is a very abstract concept. Most people want to run away from anything close to governance," said Sanjay Saxena (pictured), senior vice president of enterprise data governance at Northern Trust Corp. Saxena visited theCUBE, SiliconANGLE's mobile live-streaming studio, and answered questions from hosts Dave Vellante (@dvellante) and James Kobielus (@jameskobielus) during IBM Fast Track Your Data in Munich, Germany.
It took mankind untold eons to learn how to fly, but now artificial intelligence is doing something similar and in a fraction of the time. No, there's no robots constructing planes like the Wright brothers, but some AI-powered gliders are indeed learning how to cruise through the air just like birds, and they're getting pretty good at it. Researchers equipped a glider with an advanced algorithm and control system that allows it to navigate wind currents in the same way that birds to. By finding updrafts which help it stay aloft, the glider can slip through the air indefinitely, much like birds to when trying to minimize their energy output. The research, which was published in Nature, describes how these wind currents are used by birds.
Of course, artificial intelligence also has the potential to kill jobs, and Morich's role, however new, is not immune. Bowery hasn't yet figured out how to automate everything that needs to get done in the farm, but since she was hired less than two years ago, the company has made progress: Such processes as seeding, once done by hand, are now completed by machines. Morich says she doesn't worry about job security, but economist Erik Brynjolfsson is more skeptical. "If a task doesn't draw on human creativity or other human strengths like interpersonal skills, then it's a candidate for automation," says Brynjolfsson, a professor at the MIT Sloan School of Management and co-author of "The Second Machine Age: Work, Progress and Prosperity in a Time of Brilliant Technologies." "This could be profitable in the short and medium term," he says, but as robots become more mobile and dexterous, "I would not count on having a job like that in 10 or 15 years."
The customer-centric organizations of today are already keenly aware of the digital revolution, which has transformed conventional business models while empowering customers. Over the last decade, customer experience (CX) has drastically changed with the introduction of several opportunities for customers to interact, engage and transact with brands at their convenience across multiple channels. Digital 2.0 is the next phase, where the plain and simple customer experience of old will make space for intuitive, contextual and practical engagement across different customer touchpoints. By 2020, digital technologies like AI, biometrics, machine and deep learning and robotic automation will revolutionize the way consumers interact with organizations and brands. According to Gartner, 2020 is going to witness 20 billion'things' connected to the Internet.