AMAZON: Has opened an AI-powered convenience store in Seattle. The premise of Amazon Go is simple: to eliminate everyone's least-favorite part of the shopping experience, checking out. With ceiling-mounted sensors and cameras backed by artificial intelligence, Amazon is able to track every interaction a customer has with a product. It knows exactly when a product is picked up or put back. Go works like a physical manifestation of Amazon's 1-Click checkout, where you "click" by taking an item off a shelf.

In this thesis, we leverage the neural copy mechanism and memory-augmented neural networks (MANNs) to address existing challenge of neural task-oriented dialogue learning. We show the effectiveness of our strategy by achieving good performance in multi-domain dialogue state tracking, retrieval-based dialogue systems, and generation-based dialogue systems. We first propose a transferable dialogue state generator (TRADE) that leverages its copy mechanism to get rid of dialogue ontology and share knowledge between domains. We also evaluate unseen domain dialogue state tracking and show that TRADE enables zero-shot dialogue state tracking and can adapt to new few-shot domains without forgetting the previous domains. Second, we utilize MANNs to improve retrieval-based dialogue learning. They are able to capture dialogue sequential dependencies and memorize long-term information. We also propose a recorded delexicalization copy strategy to replace real entity values with ordered entity types. Our models are shown to surpass other retrieval baselines, especially when the conversation has a large number of turns. Lastly, we tackle generation-based dialogue learning with two proposed models, the memory-to-sequence (Mem2Seq) and global-to-local memory pointer network (GLMP). Mem2Seq is the first model to combine multi-hop memory attention with the idea of the copy mechanism. GLMP further introduces the concept of response sketching and double pointers copying. We show that GLMP achieves the state-of-the-art performance on human evaluation.

When it came time for Fiverr to analyze the data around user behavior on its platform, the freelance services website did what many modern businesses do in terms of recruiting the right tool for the task: they engaged Amazon Web Services's SageMaker. Amazon SageMaker is an API that enables developers and data scientists to readily build, train, and deploy machine learning models to serve any need and at any scale. "This was a very important milestone for us because, moving forward, we want this to be the standard in the data science deployment and all our predictive modeling," says Eran Abikhzer-Agam, head of data at Fiverr. "We want our data scientists to be completely independent as much as possible, and SageMaker allowed us to do that." Like any online business, Fiverr was interested in streamlining the user experience on its platform, tracking user behavior, and predicting when a customer might need a bit of help.

Shop and save during The Home Depot's Memorial Day sale happening now. If you make a purchase by clicking one of our links, we may earn a small share of the revenue. However, our picks and opinions are independent from USA Today's newsroom and any business incentives. Cookouts, backyard bashes, and getting to relish a hard-earned long weekend--these are just a few of the things that might leap to mind when you think about Memorial Day. But if you're someone who absolutely loves DIY projects and just generally doing stuff around the house, chances are you also think of Home Depot, since their annual Memorial Day sale is one of the best events they run all year.

There might be a lot of hype surrounding artificial intelligence (AI) and machine learning (ML). But communications provider AT&T is a believer -- and is transforming its business with the help of these technologies. "Our mission at the chief data office is to data power AT&T through data insights and evolving technologies such as AI and ML," said Kim Keating, vice president of data science at the company. "This allows us to leverage vast amounts of data to provide insights and answers to critical business questions" affecting the company. One are where the company is leveraging these technologies is in selecting retail store formats.

Punchh, the leader in digital marketing solutions for physical retailers, today announced the launch of Punchh Deep Sentiment Analysis. The new product allows brands to extract valuable insights from customer reviews using Punchh's natural language comprehension engine built with industry-leading deep learning and artificial intelligence. Its natural language processing model achieves human-level performance, defined as more than 93 percent accurate, and features multi-language support. "In today's hyper-competitive climate, brands need to do everything they can to foster and nurture direct customer relationships, and paying attention to customer reviews is an essential part of that," said Shyam Rao, CEO of Punchh. "Manually reading every review is prohibitively time-consuming for most retailers, which leads to slower response times and poor customer experiences. Our solution uses AI and machine learning to help brands analyze reviews at scale and immediately identify critical information so they can focus on high-level insights and make quick decisions to strengthen customer relationships and increase loyalty."

Google continues to update ML Kit, the company's flagship technology to enable developers to create applications for mobile devices that utilize the power of machine learning. The company reported on Tuesday at the Google I/O developer conference that improvements in ML Kit now make it possible for mobile devices to perform machine learning functions even when disconnected from the internet. Related: Google's ML Kit: Machine Learning For Mobile Made Easy ML Kit is Google's set of APIs and code libraries provided to developers for implementing machine learning capabilities on mobile devices. The ML Kit SDKTrack this Framework/Library adds a new layer of simplicity for creating programs for mobile and IoT devices. Using ML Kit developers can take advantage of a mobile device's camera and computing resources to implement vision analysis and natural language processing features.

Amazon frequently receives credit for successfully employing machine learning to engage consumers and drive sales with its well-known recommendation engine, which generates 35% of the company's revenue, according to McKinsey . However, competitor Walmart has a surprising amount of machine learning activity going on behind the scenes. For instance, Walmart created a facial recognition system that allowed the company to pinpoint customers who were unhappy about waiting in line. The system alerted sales associates that new lanes needed to be opened, which increased customer satisfaction and helped the retailer to manage employee workflow more efficiently. While hotels are, in some ways, worlds away from retailers in terms of the scope of operations and product, the hospitality industry can learn from the experience of retailers when it comes to machine learning, positive customer service, and merchandising.

The billionaires are at it again. Jeff Bezos on Thursday unveiled Blue Origin's grand vision to travel to the moon when he took the wraps off of Blue Moon, the spaceflight company's new lunar lander. Not content to let the Amazon CEO have his glory, Elon Musk sent out a late night tweet including an altered image of the lander with the word'Moon' crossed out and replaced with the word'Balls.' Not content to let Jeff Bezos have his glory, Elon Musk sent out a tweet including an altered image of the lander with the word'Moon' crossed out and replaced with the word'Balls' 'Oh stop teasing, Jeff,' Musk wrote, adding a winking emoji at the end of the tweet. Musk and Bezos have been known to spar over Twitter about their accomplishments in the past.

The launch comes after years of research and improvements using artificial intelligence (AI) and natural language generation (NLG). According to Statista 2.14 billion people worldwide will buy goods and services online by 2021. In Canada, PayPal reports that eCommerce businesses are growing 28 times more than those who are not selling online. As more consumers turn to the web to research and purchase items, product content becomes increasingly important in order to drive this level of growth. According to Salsify 51% of the time, a product listing with more bullets will convert at a higher rate and outrank its top competitor.