As the amount of available consumer data keeps on increasing and consumer profiles get more sophisticated, marketers need the power of predictive data-driven tools to help them create more targeted audience segments, adaptive journeys and hyper-personalized offers,

In the drive to improve productivity and business performance, one approach with great untapped potential is to harness metadata, machine learning, and process automation together in enterprise solutions smart enough to optimize themselves. We already have the necessary components. What we need to do is assemble the components in solutions that can learn, iterate, and improve with each iteration. Such solutions have powerful potential to make people better at their jobs in a multitude of ways -- by automating mundane tasks, reducing errors, and even detecting fraud, to name just a few. Let's start with metadata, or information that describes data to make it easier to find, categorize, and use.

Machine Learning (ML) is a subset of Artificial Intelligence and refers to techniques which enable machines to recognize underlying patterns and learn to make predictions and recommendations by analyzing data and experiences, rather than through traditional explicit programming instructions.

The money for the Allen Institute for Artificial Intelligence will about double the lab's budget over the next three years, helping to fund existing research as well as the new effort, called Project Alexandria.

Artificial Intelligence (AI) has entered our daily lives like never before and we are yet to unravel the many other ways in which it could flourish. All tech giants such as Microsoft, Uber, Google, Facebook, Apple, Amazon, Oracle, Intel, IBM or Twitter are competing in the race to lead the market and acquire the most innovative and promising AI businesses. AI is already being used in everyday life with applications including speech recognition, smart cars, fraud detection, security surveillance, music recommendations and AI-powered personal virtual assistant such as Cortana (Microsoft), Siri (Apple) or Alexa (Amazon). Discussions on AI are generally dappled with the terms, 'Machine Learning' and'Deep Learning'. Moreover, they are often interchangeably used.

Embedded markup of Web pages has seen widespread adoption throughout the past years driven by standards such as RDFa and Microdata and initiatives such as schema.org, where recent studies show an adoption by 39% of all Web pages already in 2016. While this constitutes an important information source for tasks such as Web search, Web page classification or knowledge graph augmentation, individual markup nodes are usually sparsely described and often lack essential information. For instance, from 26 million nodes describing events within the Common Crawl in 2016, 59% of nodes provide less than six statements and only 257,000 nodes (0.96%) are typed with more specific event subtypes. Nevertheless, given the scale and diversity of Web markup data, nodes that provide missing information can be obtained from the Web in large quantities, in particular for categorical properties. Such data constitutes potential training data for inferring missing information to significantly augment sparsely described nodes. In this work, we introduce a supervised approach for inferring missing categorical properties in Web markup. Our experiments, conducted on properties of events and movies, show a performance of 79% and 83% F1 score correspondingly, significantly outperforming existing baselines.

Neural text generation models are often autoregressive language models or seq2seq models. These models generate text by sampling words sequentially, with each word conditioned on the previous word, and are state-of-the-art for several machine translation and summarization benchmarks. These benchmarks are often defined by validation perplexity even though this is not a direct measure of the quality of the generated text. Additionally, these models are typically trained via maxi- mum likelihood and teacher forcing. These methods are well-suited to optimizing perplexity but can result in poor sample quality since generating text requires conditioning on sequences of words that may have never been observed at training time. We propose to improve sample quality using Generative Adversarial Networks (GANs), which explicitly train the generator to produce high quality samples and have shown a lot of success in image generation. GANs were originally designed to output differentiable values, so discrete language generation is challenging for them. We claim that validation perplexity alone is not indicative of the quality of text generated by a model. We introduce an actor-critic conditional GAN that fills in missing text conditioned on the surrounding context. We show qualitatively and quantitatively, evidence that this produces more realistic conditional and unconditional text samples compared to a maximum likelihood trained model.

Thrilling to see more bots coming to the travel industry. This article does a great job outlining exactly what a good Bot Pilot structure should look like: Start with a simple use case/tackling your most FAQ. The purpose of the pilot isn't to get everything right, right off the bat, it's to learn and gather data to improve your bot's knowledge base and NLP. So structure your pilot with the intention of listening and identifying what people are asking it that you didn't anticipate. Be resourceful when it comes to tackling the discovery hurdle.

Welcome to the real "Minority Report". PRE-CRIME is a wake up call for all of us. A science fiction scenario, both fascinating and scary, has arrived in our everyday life. To predict a future crime scene and to prevent a murder seems like something from a sci-fi movie. It is, but it's also real – and happening right now.

Leonardo and David Ryan Polgar were running behind schedule for their show -- technical difficulties with the projector -- then came out for a cold open. "Tonight, we're talking about the future of work," said Polgar. "What are you worried about with the future of work, Joe?" "We're all gonna be replaced with robots," Leonardo shot back, looking half expectantly at the crowd of about 30 people, who looked back. Polgar moved on to the recent Boston Dynamics video, which bears a disturbing resemblance to the Black Mirror episode "Metalhead." A four-legged robot precisely opens a door for another robot, then holds it for the companion to enter. "They're already making me look bad, because there's, like chivalry in robots now," Leonardo said, to murmurs of chuckles.