When you really think about it, what we call "work" hasn't evolved much since the Industrial Revolution: While the technology has made most tasks easier, the name of the game is still mass production--codifying business processes using technology and scaling it across the entire enterprise. But artificial intelligence (AI) changes the game. Rather than fitting people into a repetitive process, AI offers the opportunity to liberate them from it altogether, freeing their time and creative energy for more strategic and meaningful work. For the first time since the Industrial Revolution, technology is being used to bring humanity back to work. This revolution actually began at home, thanks to the smart technology behind Siri, Alexa, and Google Assistant, which have found a place in our daily lives.

Have you ever wondered whom to thank for some of the modern conveniences you might have started taking for granted, like Siri, Cortana or Alexa (assuming you agree these are conveniences)? The people at the Association for Computing Machinery (ACM) decided to thank Geoffrey Hinton, Yoshua Bengio and Yann LeCun in April of this year by honoring them with the Turing Award for their contributions to deep learning and neural networks. These contributions are put to use every time you log into your smartphone using fingerprint or facial recognition or when you use Google Photos or a voice assistant, and likely every time you use Amazon, Netflix, Facebook or Instagram. The advances in automatic language translation and autonomous cars in recent years arguably wouldn't have progressed as rapidly had it not been for the contributions of these three researchers. All of that is still an understatement of their contributions to artificial intelligence (AI).

Artificial Intelligence used to be a fictive narrative from Isaac Asimov's novels and futuristic films. Today it's 2019, and AI is no longer just a product of imagination. Let's take a look at the world's favorite futuristic movies. We already have them in every household. These can be easily found on Amazon and even your local Home Depot.

After breaking all the records related to training computer vision models, NVIDIA now claims that it's AI platform is able to train a natural language neural network model based on one of the largest datasets in a record time. It also claims that the inference time is just 2 milliseconds which translates to an extremely fast response from the model participating in a conversation with a user. After computer vision, natural language processing is one of the top applications of AI. From Siri to Alexa to Cortana to Google Assistant, all conversational user experiences are powered by AI. The advancements in AI research is putting the power of language understanding and conversational interface into the hands of developers.

We start with a brief introduction to reinforcement learning (RL), about its successful stories, basics, an example, issues, the ICML 2019 Workshop on RL for Real Life, how to use it, study material and an outlook. Then we discuss a selection of RL applications, including recommender systems, computer systems, energy, finance, healthcare, robotics, and transportation.

Recently, researchers utilize Knowledge Graph (KG) as side information in recommendation system to address cold start and sparsity issue and improve the recommendation performance. Existing KG-aware recommendation model use the feature of neighboring entities and structural information to update the embedding of currently located entity. Although the fruitful information is beneficial to the following task, the cost of exploring the entire graph is massive and impractical. In order to reduce the computational cost and maintain the pattern of extracting features, KG-aware recommendation model usually utilize fixed-size and random set of neighbors rather than complete information in KG. Nonetheless, there are two critical issues in these approaches: First of all, fixed-size and randomly selected neighbors restrict the view of graph. In addition, as the order of graph feature increases, the growth of parameter dimensionality of the model may lead the training process hard to converge. To solve the aforementioned limitations, we propose GraphSW, a strategy based on stage-wise training framework which would only access to a subset of the entities in KG in every stage. During the following stages, the learned embedding from previous stages is provided to the network in the next stage and the model can learn the information gradually from the KG. We apply stage-wise training on two SOTA recommendation models, RippleNet and Knowledge Graph Convolutional Networks (KGCN). Moreover, we evaluate the performance on six real world datasets, Last.FM 2011, Book-Crossing,movie, LFM-1b 2015, Amazon-book and Yelp 2018. The result of our experiments shows that proposed strategy can help both models to collect more information from the KG and improve the performance. Furthermore, it is observed that GraphSW can assist KGCN to converge effectively in high-order graph feature.

Artificial intelligence (AI) is a science that deals with building intelligent machines and algorithms that can think and respond like a human (that is learning according to human). Artificial intelligence has filled the digital lacuna and summoned reality into utopia. Undoubtedly it has filled every walk of life from airport to home automation and e-commerce is no new story for its ink. It won't be necromancy to add that not only Artificial intelligence is simplifying the E-commerce but also providing unbelievable new horizon for its growth. With every new artificial add on ads to new possibility and a wow factor to E-commerce let us look few of the Artificial intelligence, which is changing the E-commerce spectrum.

Who wouldn't like to get more done in less time? That's the idea behind Siri Shortcuts, a popular Apple app for iPhone and iPad, and built into the upcoming iOS 13 operating system, out in beta release now with a full upgrade due this fall soon after new iPhones hit in September. As the name suggests, Siri Shortcuts link the voice-controlled personal assistant you already know with time-saving shortcuts for tasks you want to perform. By simply asking for it – or tapping the screen if you're not in a place to freely use your voice – your iPhone or iPad can quickly heed your request. In other words, Siri Shortcuts – previously known as Workflow – fuses small actions to yield big results.

The new partnership will help marketing and sales teams maximize the ROI on their event marketing spend. With SummitSync's conference and trade show intelligence paired with automated lead engagement through Conversica's AI Assistants, brands will be able to drive higher quality sales meetings and convert more prospects to customers like never before. For event marketers, the joint solution automates many of the time-consuming tasks associated with event execution such as pre-booking meetings at events, coordinating on-site demos with salespeople and capturing follow-up requests for information. It frees event marketers to focus on delivering an outstanding end-to-end experience. "Conversica is an ideal partner for us because we serve a similar customer base and our combined technologies complement each other and increase the value we deliver," said Al Torres, president and co-founder at SummitSync.

You've seen the TV ads for Amazon's Alexa-enabled devices, and you know you can ask her to do, well, just about anything. What really is Alexa, though, and how can she make your life easier? Alexa is Amazon's own smart assistant and can be found on any Alexa-enabled device. This can be a smart speaker, like the Echo Dot, the Echo Plus, or the Echo; or a smart display device, such as the Echo Show or the Echo Spot. There are a handful of other Amazon devices that use Alexa, such as the Amazon Fire Stick.