It was probably the HAL 9000 from the movie 2001: A Space Odyssey, that introduced the concept of AIs to the general public. But that was almost 40 years ago, and examining the more recent times, we have to look no further than in our own pockets to find the AIs that paved the way for the current hype: smartphones' digital assistants. Sure, there has been things like Cleverbot around earlier, but nothing has been as widely spread as these digital assistants. The main difference between a chatbot and a digital assistant is that former responds (be default) only to written queries, and the latter is capable to understand (at least to some extend) more natural, spoken queries. Things like voice activated searches and speech recognition softwares have been around for quite some time, but these digital assistants take the concept a step further by engaging in dialogue, performing tasks such as booking flights or setting up location based reminders, and they can even tell you a joke if you ask one.
When is milk not milk? This is no trick question -- it's a distinction that artificial intelligence (AI) is going to have to learn to make in order for eMerchants to fully leverage the potential of machine learning. To one customer, "buy milk" means buy a gallon of whole milk; to another, a 1.4-liter jug of unsweetened vanilla almond milk. Digital shopping lists, apps and virtual assistants must understand this and not force the customer to spell it out each time before these platforms can successfully become the new normal. "We think about things in shorthand, not in terms of specifics," Dave Barrowman, Skava VP of Innovation, told PYMNTS' Karen Webster in a recent webinar.
We live in a world where consumer attention span is getting shorter and shorter: 40 percent of people abandon a website that takes more than three seconds to load, and the average shopping cart is abandoned more than 68 percent of the time. Software platforms that drive ecommerce websites are creating visual search capabilities which allow consumers to upload an image and find similar/complementary products. The offline to online experience requires minimal steps to shop and purchase, providing a sense of autonomy to the consumer. Brands are creating more interactive shopping experiences to provide product recommendations based on natural conversation and cognitive data derived from AI.
We describe a user study evaluating two critiquing-based recommender agents based on three criteria: decision accuracy, decision effort, and user confidence. Results show that user-motivated critiques were more frequently applied and the example critiquing system employing only this type of critiques achieved the best results. In particular, the example critiquing agent significantly improves users' decision accuracy with less cognitive effort consumed than the dynamic critiquing recommender with system-proposed critiques. Additionally, the former is more likely to inspire users' confidence of their choice and promote their intention to purchase and return to the agent for future use.
Rules promise to be widely useful in Internet electronic commerce. Declarative prioritized default rule knowledge representations offer the advantage of handling conflicts that arise in updating rule sets, but have as yet had little practical deployment. DIPLOMAT is a Java library that embodies a new approach to the implementation of such prioritized default rules: to compile them into ordinary logic programs (LP's) cf. We apply the approach to a newly generalized version of courteous LP's, a semantically attractive and computationally tractable form of prioritized default rules. Compilation enables courteous LP's functionality to be added modularly to ordinary LP rule engines, via a preprocessor, with tractable computational overhead.