This post is co-written by Arte Merritt, co-founder and CEO of Dashbot. In their own words, "Dashbot is an analytics platform for chatbots and voice skills that enables enterprises to increase engagement, satisfaction, and conversions through actionable insights and tools." After you have deployed a bot, it is critical to analyze bot interactions, learn from this analysis, and use these learnings to improve the end-user experience. Conversational interfaces are easier to analyze than websites and mobile applications. You can infer user behavior directly from conversations instead of guessing what your users want by stitching together page views and choosing events.
In recent times, organizations have been competing with one another to implement chatbots for various reasons, including enhancing customer experience, streamlining processes, and fueling the demand for digital and innovative technologies. Cognitive technologies such as chatbots have become an apt candidate for end-use application as they have high automation feasibility, high potential of accuracy, low complexity and low execution time. Raising the bar through intelligence, virtual assistants have been propelled by advancements of mobile technology. Technology giants are putting their weight on a platform designed to answer ad-hoc queries in real-time and fuel sales as chatbots can remember customer preference and use order history to learn from customer responses to the product advertisements, suggest products, and cross-sell aptly. For instance, if a customer asks for a pizza recommendation with a chatbot, it can remember which pizza the customer ordered and follow up with it when offering a recommendation for another pizza or a restaurant.
This special issue of Media-N gathers perspectives on artistic labor in an increasingly automated global economy, focusing on the impact of artificial intelligence and the role of Amazon.com, Inc. in reshaping how work is defined, valued, and performed. Our contributors are artists, curators, scholars, and critics interested in labor value, emerging forms of exploitation and alienation, as well as new possibilities for collective resistance, solidarity, and critique.
With an increasing number of digital text documents shared across the world for both business and personal reasons, the need for translation capabilities becomes even more critical. There are multiple tools available online that enable people to copy/paste text and get the translated equivalent in the language of their choice. While this is a great way to perform ad hoc translation of a (limited) amount of text, it can be tedious and time-consuming if performed frequently. Your organization may largely depend on content to document your products and services, teach your customers how to interact with you, or just share the cool things you are doing. This content is often text-heavy and mostly written in English.
Last week I spoke to executives from a large AWS customer and had an opportunity to share aspects of the Amazon culture with them. I was able to talk to them about our Leadership Principles and our Working Backwards model. They asked, as customers often do, about where we see the industry in the next 5 or 10 years. This is a hard question to answer, because about 90% of our product roadmap is driven by requests from our customers. I honestly don't know where the future will take us, but I do know that it will help our customers to meet their goals and to deliver on their vision.
Being an early adopter of artificial intelligence and automation, Amazon always had an edge in using AI to improve its business efficiencies. Not only has it been using AI to enhance its customer experience but has been heavily focused internally. From using AI to predict the number of customers willing to buy a new product to running a cashier-less grocery store, Amazon's AI capabilities are designed to provide customised recommendations to its customers. According to a report, Amazon's recommendation engine is driving 35% of its total sales. One of the main areas where Amazon is applying continuous AI is to better understand their customer search queries and what is the reason they are looking for a particular product.
Battlesnake is an AI competition based on the traditional snake game in which multiple AI-powered snakes compete to be the last snake surviving. Battlesnake attracts a community of developers at all levels. Hundreds of snakes compete and rise up in the ranks in the online Battlesnake global arena. Battlesnake also hosts several offline events that are attended by more than a thousand developers and non-developers alike and are streamed on Twitch. Teams of developers build snakes for the competition and learn new tech skills, learn to collaborate, and have fun. Teams can build snakes by using a variety of strategies ranging from state-of-the-art deep reinforcement learning (RL) algorithms to unique heuristics-based strategies. This post shows how to use Amazon SageMaker to build an RL-based snake.
'Ecommerce businesses have a problem - one that causes lost customer revenue, yet has been historically nearly impossible to solve' Geoff Huang, VP of Product at Sift The problem stems from the inability to know their false-positive rate, which is the percentage of orders from legitimate customers that are mistakenly blocked as fraud. According to a survey conducted by CNP, 42% of ecommerce merchants don't know their false-positive rate (also known as customer insult rate). That is a startling statistic--nearly half of online sellers have no visibility into the number of good orders they inadvertently block or the subsequent revenue lost from those orders. And the news, unfortunately, doesn't get much better. Sift polled 1,000 adult consumers and found roughly 25% of insulted online shoppers--those who were falsely declined--will take their business to a competitor.
Amazon SageMaker is a fully managed service that enables developers and data scientists to quickly and easily build, train, and deploy machine learning (ML) models at any scale. In addition to building ML models using more commonly used supervised and unsupervised learning techniques, you can also build reinforcement learning (RL) models using Amazon SageMaker RL. Amazon SageMaker RL includes pre-built RL libraries and algorithms that make it easy to get started with reinforcement learning. For more information, see Amazon SageMaker RL – Managed Reinforcement Learning with Amazon Sagemaker. Amazon SageMaker RL makes it easy to integrate with various simulation environments such as AWS RoboMaker, Open AI Gym, open-source environments, and custom-built environments for training RL models.
WASHINGTON--Amid rising calls for regulation, technology companies are pushing for laws that would restrict use of facial-recognition systems--and head off the more severe prohibitions some cities and states are weighing. Inc. and others stand to profit as government agencies and businesses expand use of the technology, which can require large investments in machine-learning and cloud-computing capacity.