By age 7 or 8, most kids feel comfortable using devices like smartphones -- recent research puts the percentage of American kids between the ages of 10 and 12 who have their own service plans/smartphones at around 45%. Another survey found that 42% of U.S. children aged 8 or younger have their own tablet devices. The age you'll want to introduce kids to devices like these understandably varies from family to family. Developing a "family media use plan" is one way parents can set appropriate limits for screen time -- for every member of the crew (yup, that includes you, Mom and Dad). Tech devices that are custom-tailored to kids, such as the Amazon Echo Dot Kids Edition or an Amazon Fire Kids Edition Tablet, are a perfect introduction to digital devices, and with features like Amazon FreeTime Unlimited, a subscription service that provides access to hundreds of hours of fun and educational content, families can enjoy a breadth of exploration together.
This installment of Research for Practice features a curated selection from Alex Ratner and Chris Ré, who provide an overview of recent developments in Knowledge Base Construction (KBC). While knowledge bases have a long history dating to the expert systems of the 1970s, recent advances in machine learning have led to a knowledge base renaissance, with knowledge bases now powering major product functionality including Google Assistant, Amazon Alexa, Apple Siri, and Wolfram Alpha. Ratner and Re's selections highlight key considerations in the modern KBC process, from interfaces that extract knowledge from domain experts to algorithms and representations that transfer knowledge across tasks.
Amazon has revealed a whole host of new Echo devices, as well as products that you can control by talking to the Alexa assistant that lives inside them. In all, it announced more than 70 updates – which touched on almost every part of its Alexa line-up. The very short version is this: Amazon updated just about every Echo to give it a better-looking grey mesh on the outside and to make it louder and better sounding on the inside. If you want the slightly less short version of each of the updates, then read on. Here's what happened to each of those products in slightly more detail.
Humans are constantly fascinated with auto-operating AI-driven gadgets. The latest trend that is catching the eye of the majority of the tech industry is chatbots. And with so much research and advancement in the field, the programming is winding up more human-like, on top of being automated. The blend of immediate response reaction and consistent connectivity makes them an engaging change to the web applications trend. In general terms, a bot is nothing but a software that will perform automatic tasks.
It's been said that nostalgia isn't what it used to be, and in the world of technology there's a lot to be nostalgic about! Just over twenty years ago client-server was all the rage, and then the internet arrived and suddenly browser-based systems became the new way to do everything. Then in the mid-2000's Apple coined the phrase, "there's an app for that", which then begat a mad rush to build phone-based apps for everything under the sun. And now, in 2018, all of those things have been usurped by a new UI that will change the technology landscape yet again: chatbots. And, for generation Z or millennials, this is the way they expect to be able to interact with everything.
They can identify patterns in voice commands and react to them according to predefined algorithms. In HR these are employed when the initial contact with applicants is being made – often when it comes to answering standard questions about an advertised position. As chatbots they can support an ongoing interaction between recruiter and applicant during the recruiting process, in this case they quite simply increase the recruiters accessibility or the applicant. Second field of application: „Natural Language Processing" (NLP), this technology supports the scanning of letters of application to characterize the applicants range and use vocabulary and his "wording" in general. NLP can assist in writing job advertisements, by using a language which is exactly targeted towards the preferred group of applicants.
Artificial Intelligence (AI) is an important and evolving concept that is having significant impact within the Customer Experience industry -- and it's a topic that is being talked about on a seemingly daily basis at this point. But is AI really ready for prime time in customer care? I sat down with Michael Johnston, Director of Research and Innovation at Interactions, to get answers to some questions that are frequently asked about AI and Machine Learning as they apply to customer care. Artificial Intelligence refers to the capability of a machine to imitate intelligent human behavior. Put another way, AI technologies are algorithms that attempt to mimic things that humans do.
You can thank Facebook for all the buzz about chatbots and the gold rush of developers and venture capital creating an onslaught of bot companies. However, the truth is we are only just starting to realise the potential of artificial intelligence and one of the most promising applications for AI to make a more than welcome impact is in customer experience. It can be debated that for all of the good technology has offered businesses over the last several decades, it has also comprised key elements of the customer experience. Call centres, websites, email, apps, et al, have allowed companies to scale and automate customer engagement while also introducing process efficiencies and cost management systems. But that also comes at a cost.
In this Vlog, Adrian Bowles, PhD provides an overview of MicroStrategy, an enterprise software platform vendor that offers Analytics capabilities and mobile access. MicroStrategy's vision is to make every enterprise an Intelligent Enterprise. For more information on MicroStrategy's approach, watch the rest of the video below: Your email address will not be published.
Video recording of talk given at ODSC West on November 3rd, 2017 "State of Conversational AI" gives a technical overview & review of current state-of-the-art deep learning & NLP tactics for chatbots and conversational interfaces as well as product UX / design tips for crafting customer experiences around current limitations in conversational AI. Talk covers recent research papers, metrics and lessons from live products, and also a review of conversational AI platforms available for enterprise usage. The target audience is product leaders, technical executives, and designers and developers involved in the crafting of customer-facing conversational experiences. Mariya is the CTO and Head of Research & Design at TOPBOTS, a strategy & research firm focused on applied artificial intelligence and machine learning. As an "AI Designer", she combines UX and AI to create intelligent tools and lovable products.