Seattle is one of my favorite tech-friendly cities and I always look forward to heading out to the Pacific Northwest for a conference. Sometimes I take off on foot from my favorite downtown hotel to take in the feel of the city. Yes, Seattle is that cool. This time, I stumbled upon a real gem – the new Amazon Go store located at 2131 7th Ave., just a few blocks from my hotel. I soon found out this experimental retail outlet is a bold new move to transform retail that's powered by deep learning technology, specifically image recognition algorithms.

UK-based Dixons Carphone - a multinational electrical and telecommunications retailer and services company - uses artificial intelligence in the form of a bot named Cami to connect online and in-store shopping experiences. Cami is a product expert who can recommend items, give advice, and anticipate customer's needs and future purchases. If someone buys a new mobile device, Cami can automatically recommend cases or insurance. Cami can also easily check inventory, so in-store associates can stay with the customers. Employees can spend more time interacting with customers in the front of the store instead of sorting through inventory in the back of the store.

When you have millions of choices, shopping isn't easy. That's why Trendage is using artificial intelligence, visual search, and crowdfunded fashion expertise to help retailers make the right recommendations for shoppers. Trendage is a data-driven style platform that is coming out of stealth today with Automated Product Recommendations for retailers. The company has raised $1.5 million in angel funding. It draws on a combination of AI, visual search, and a community of trendsetters to generate more than 10 million style recommendations a month for apparel, accessories, and footwear retailers.

The .NET core chatbot application code allows you to order flowers using a Chatbot powered by Amazon Lex, AWS Lambda, and Amazon Cognito Identity. The chatbot uses Amazon Lex to build a text based conversational interface for a web application. AWS Lambda is used as code hooks for this bot to validate user responses. You can also create AWS Lambda functions to perform initialization and fulfillment, or both, in your Lex intent configuration. A blueprint for this Lex bot will be provided and preconfigured for you in Module 2. The chatbot uses Amazon Cognito Identity to control user access to the AWS resources supporting your chatbot.

Amazon wants to cut the lag time between your asking Alexa a question and the virtual assistant giving you an answer. According to a report by The Information, the online retailer is developing its own artificial intelligence chips to be used in Echo devices and other hardware. If successfully created and deployed, these AI chips would allow more voice-based requests to be processed on-device rather than going to the cloud. Currently, Alexa needs to contact the cloud to interpret commands. That's why there's a short delay after you ask the virtual assistant a question--she needs to analyze the command and gather an answer with help from the cloud.

Two weeks ago, the e-commerce retailer Amazon opened its first offline convenience store, Amazon Go – without a cashier. On January 22, the first visitors of the Seattle store were tracked in the shop using image recognition and machine learning algorithms. The technology finds out what the visitors have bought and charges it automatically to their account. After the customer has scanned their smartphone to enter the store, cameras throughout the store track them as a 3D object without facial recognition. The biggest challenge for image recognition is to differentiate between similar-looking products and customers hands, which often cover the products and their labels.

Lidl has introduced an artificial intelligence (AI) chatbot to help improve the wine-buying experience for shoppers, as the food retailer continues its efforts to wrest market share away from the big four supermarket chains. Every conference this year contains a dead human genius reincarnated as software system or a robot. Yes, there is a lot of hype, but there is real worth in AI and Machine Learning. Read our counseling on how to avoid adopting "black box" approach. You forgot to provide an Email Address.

No one wants to go grocery shopping. It's all heavy carts, complex layouts, and unfathomable product placement decisions – why is the sherry vinegar next to the olive bar? The daunting, sisyphean task takes up enough time to make you resentful. Making you hate it less, and helping you find exactly what you're looking for, is Instacart's raison d'etre, says Jeremy Stanley, the company's VP of data science, and chief wielder of the machine learned algorthims behind the scenes. See the full line-up and grab last-minute tickets.] Instacart represents an advancement in online grocery shopping service: It gives you millions of products from hundreds of retail partners, and then hooks you up with a personal shopper who makes that grocery list land on your doorstep.

My primary phone is an iPhone 7 Plus, although I use Android phones all week as part of my job. In my house, I use the Apple TV and quite a few HomeKit devices, so I'm already using Siri to turn my lights on and off and even to activate a sprinkler system. Product success can depend greatly on an established audience. When the Apple HomePod ships this December, I'll buy one or two or six. I'll probably even try to score one of the first ones at my local Apple store, as I did with an iPad long, long ago.

Do you remember Hollywood movies Terminator: Rise of Machines or Ex Machina where facial recognition technologies are used in several ways? Today with digital technological advances, face recognition has become very important for businesses, to know who the customer is and send hyper-personalized offers to generate more revenues. Facebook has used facial recognition technology since 2011, to speed up the process of tagging friends or people in photos. When a user uploads an image to Facebook, the site's algorithms recognize the faces of friends and asks users if they would like to tag them. Security agencies were also early adopters of face recognition to identify suspicious behavior & threats to any big event or crowded areas like airports.