Today I am happy to announce the public release of the Topical Chat Dataset, a text-based collection of more than 235,000 utterances (over 4,700,000 words) that will help support high-quality, repeatable research in the field of dialogue systems. The goal of Topical Chat is to enable innovative research in knowledge-grounded neural response-generation systems by tackling hard challenges that are not addressed by other publicly available datasets. Those challenges, which we have seen universities begin to tackle in the Alexa Prize Socialbot Grand Challenge, include transitioning between topics in a natural manner, knowledge selection and enrichment, and integration of fact and opinion into dialogue. Each conversation in the data set refers to a group of three related entities, and every turn of conversation is supported by an extract from a collection of unstructured or loosely structured text resources. To our knowledge, Topical Chat is the largest social-conversation and knowledge dataset available publicly to the research community.

Q: Where is AWS Innovate hosted? AWS Innovate is an online conference. After completing the online registration process, you will receive a confirmation email containing the login link that you will need in order to access the platform. Q: Can I watch the sessions after the event? All sessions will be available on demand from the day after the event until the end of November.

In India, during the festive season, online retail witnesses a great jump in the number of consumers each year around August to January, with an expected 40 percent rise in consumer traffic. This also implies serious competition between e-commerce players. Their marketing and sales team do everything to ensure that their deals and campaigns are better than the others. Due to aggressive marketing campaigns, offers and discounts by brands, 2018's festive season saw a 71 percent rise in the number of online customers. Amid this, the question arises, what method is applied by these companies to stay ahead in the competition?

Amazon is opening its doors wide to Prime customers in search of faster shipping in an effort to eat up more of the traditional retail market. According to a report from Recode, the e-commerce giant has removed restrictions on its products that forbade customers from utilizing one-day shipping on items less than $5. Prime customers will be now able to select one-day shipping on products that cost as little as $1, making routine trips to convenience stores that much less convenient. While Amazon has long-sold products like deodorant, dental floss, and other household items on its platform, the restriction on price meant its users were usually required to buy those items in a larger set or tack them onto orders with other items through the company's'add-on' program. Recode notes, however, that Amazon's'add-on' program has been slowly phased out in recent months, essentially paving the way for a new ere of single-use shopping. Now, with the restrictions lifted, customers will be able to not only buy those items individually, but have them delivered imminently to their doorstep.

As Artificial Intelligence, Machine Learning is evolving at a rapid speed in India and across the globe as well, and these techniques are particularly getting into every field. Retailers are using Artificial Intelligence, social media analytics to understand a targeted set of customer's shopping behavior and preferences. Tribetailing is the strategy adopted by Retail firms to identify and target customers who would most benefit from the offerings. Instead of marketing to everyone for a particular product or an offer Retailers are using this technique to focus on only potential customers. A report "Know your customers: What you see is what you get" said, "There is a dynamic change in customers preferences, Tribetailing helps retailers connect to the customers and understand them better. Artificial Intelligence, Social media analytics, and machine learning are the technologies used by retailers to get information about their customer's online activity, preferences, shopping behavior, history of the customer. With these advanced technologies, retailers can categorize customers in different categories and then coming up with the best solution, offer different offers accordingly such as promotional programs such as membership and discount offers according to the characterization. This info, it stated, is helping retailers to group customers in categories that are different and think of bespoke solutions, and devise personalized advertising and promotional programs like membership as well as commitment programs and discounts to focus on a particular category. With the growth of a tech-savvy and informed more customer base, retailers are more and more replacing the conventional mass-market approach with specific techniques created using insights driven from customer information, stated the report, that had been unveiled in connection with Mapic India. Anil Talreja, partner, Deloitte India said, "with the advent of technology customers trends are evolving at a rapid pace and customers are evolving to proactive seeker of products from a reactive entity.

Amazon Textract is a machine learning service that makes it easy to extract text and data from virtually any document. Textract goes beyond simple optical character recognition (OCR) to also identify the contents of fields in forms and information stored in tables. This allows you to use Amazon Textract to instantly "read" virtually any type of document and accurately extract text and data without the need for any manual effort or custom code. The blog post Automatically extract text and structured data from documents with Amazon Textract shows how to use Amazon Textract to automatically extract text and data from scanned documents without any machine learning (ML) experience. One of the use cases covered in the post is search and discovery.

Global annual spending on artificial intelligence by retailers will top $7.3 billion by 2022. Paradoxically, only 1% of AI initiatives reach full-scale deployment as seen from our recent research. In this episode, Nisheeth Srivastava, Chief Technology and Innovation Officer for Capgemini India discusses how emerging trends in AI are reshaping the retail value chain.

The rise of cashless and cardless retail transactions suggests consumers are growing comfortable with paying for products from accounts linked to portable devices. How quickly these commerce modalities go mainstream is an open question, but Mastercard is leaving nothing to chance. The global payment network is not only preparing for digital commerce ruled by recommendation systems fueled by machine learning systems but also war-gaming for the arrival of 5G, as well as the proliferation of IoT and AI, says Jorn Lambert, Mastercard's executive vice president of digital solutions. Get the latest insights with our CIO Daily newsletter. This short- and long-term view sketches out a vision of payments revolutionized by machine-to-machine commerce and hyperpersonalized transactions, a reality Lambert expects will become mainstream in the next three to five years.

Despite applying multiple filters and typing in carefully crafted keywords, you still can't find what you're looking for online. Overwhelmed by choice, you scroll the page for the fourth time trying to choose one of the 15 shades of white paint. Do you want Paper White or Chalk White? Maybe you want beige instead. You start to wish you never undertook this do-it-yourself (DIY) project; surely, shopping should not be this hard. Ensuring a smooth and stress-free experience is critical in retaining and attracting new customers.

Here are five things in technology that happened this past week and how they affect your business. Picnic--a Seattle startup, --announced this past week that it has created an assembly platform that can make 300, 12-inch pizzas per hour (and 180 18-inch pizzas per hour), making this robot a first-of-its-kind. When a pizza is ordered, the order goes into a digital queue, prompting the robot to begin making the pie the moment that the dough is placed in the appropriate spot in the machine. Data is then sent back to Picnic through the internet in order for developers to help the robot to improve upon any errors made. The A.I. driven pizza platform is currently being used at 3 different establishments in Seattle.