Developments in the field of artificial intelligence are incredible, almost as if they are from a different world. Investors spend millions in the development of AI. The most active technology is used in the field of Internet search, helping to shape the Google search engine and handle voice assistant requests. AI becomes more perfect with each passing day. Therefore, there is nothing surprising in the fact that this technology is increasingly being used for online retailers.

Online shopping doesn't follow a single path. Instead, there's an abundance of ways to make an online purchase -- apps, email, social media. These multiple options can be disorienting to customers if there isn't one clear route for reaching businesses. Enter "conversational commerce," or businesses and buyers connecting through messaging apps. Companies today can use chatbots to instantly communicate with customers and resolve their issues on multiple platforms, such as Facebook or their online store. These round-the-clock bots use AI to infer customers' preferences and create a valuable, individualized shopping experience.

In this paper we apply active learning algorithms for dynamic pricing in a prominent e-commerce website. Dynamic pricing involves changing the price of items on a regular basis, and uses the feedback from the pricing decisions to update prices of the items. Most popular approaches to dynamic pricing use a passive learning approach, where the algorithm uses historical data to learn various parameters of the pricing problem, and uses the updated parameters to generate a new set of prices. We show that one can use active learning algorithms such as Thompson sampling to more efficiently learn the underlying parameters in a pricing problem. We apply our algorithms to a real e-commerce system and show that the algorithms indeed improve revenue compared to pricing algorithms that use passive learning.

The idea that machines can make intelligent decisions has been around since the 1950s when the first learning programme was built. At the time, the machine itself was groundbreaking, improving at the game of checkers the more it played. Since then, the idea of such machines has become more and more prevalent, particularly in pop culture. But over the last few years, the concept has moved from the realm of fiction, such as Iron Man's JARVIS (Just A Rather Very Intelligent System), the highly advanced computer system supporting Tony Stark, to our phones and even our living rooms. We're growing more and more comfortable with AI services, such as Apple's Siri and Amazon's Alexa, the latter of which Amazon recently announced would make its way to Australia and New Zealand in 2018.

It is a whole new game in the consumer-packaged goods (CPG) and retail industry. The combination of digital technologies, new competitors with smarter business models and changing consumer behaviour is disrupting, disaggregating and dislocating the industry which once operated on a simple business model. The dynamics of the CPG industry are a far cry from the good old days, when consumer behaviour was reasonably predictable and the retail environment was competitive but orderly. Consumers are no longer online or offline. They integrate multiple channels all along the purchasing pathway, and they do so in new and different ways as their specific needs and real-time circumstances dictate.

Digitally native retailers are setting new standards for the customer journey by creating highly curated experiences through the use of AI. This has enabled them to cater to consumers' desire to interact with mobile apps and websites as they would with an in-store sales representative. By mimicking the use of AI among e-commerce pureplays, brick-and-mortars can implement similar levels of personalization. AI can be used to provide personalized websites, tailored product recommendations, more relevant product search results, as well as immediate and useful customer service. However, there are several barriers to AI adoption that may make implementation difficult. By and large, these hurdles stem from a general unpreparedness of legacy retailers' systems and organizational structures to handle the huge troves of data AI solutions need to be effective.

The rise of artificial intelligence may be proving a boon to marketers, but the older consumers are, the less comfortable they feel about it, according to new research into online fashion buying. The survey, conducted by YouGov for Swedish eCommerce company Apptus, found that only a quarter (24%) of UK adults aged 55 and over would like to see online fashion retailers adopt online systems to tailor their shopping experience. In contrast, 56% of those aged between 18 and 24 - the first to have grown up with smartphones, social media and Google - report they would like to see fashion retailers adopt online systems to tailor their shopping experience. Apptus UK country manager Andrew Fowler said: "The older generation has grown up in a world where there were no computers on desks at their first place of employment, they have seen technology replace jobs and, culturally, films like 2001 A Space Odyssey and Terminator have shown artificial or synthetic intelligence in a worrying light as machines go'rogue'. "The digitally immersed Generation Z, on the other hand, has grown up with technology that, arguably, enhances their social lives, entertains them and is comfortingly omnipresent - try telling a Generation Z that there is no WiFi.

At its simplest, machine learning (ML) refers to the capacity for a program to automatically improve, or "learn," as it ingests data to accomplish a specific task or set of tasks. While ML is a subset of artificial intelligence, it's often used interchangeably with AI and conflated with predictive analytics or algorithms. The many uses and applications of machine learning create a lot of confusion about what the term really means, especially at a time when complex algorithms have been able to generate seemingly "intelligent" outcomes for quite some time. Machine learning goes a step beyond algorithms or predictive analytics, and its applications are growing exponentially in tandem with the number of new and existing companies investing in its development. Despite machine learning's advanced nature, it may come as a surprise to smaller and midmarket retailers that ML is by no means out of reach.

Artificial Intelligence or AI is expected to be in major demand by retail consumers due to its ability to make interactions in retail as flawless and seamless as possible. Many of us do realize the potential of AI and all that it is capable of, along with the support of Machine Learning or ML, but don't realize that the implementation of AI in certain segments has already begun. The future for AI and the complicated computer processes involved behind it is really bright in the field of retail. AI currently has numerous data sets working along with computer visualization methods to ensure that the users get the most seamless experience when it comes to AI in the workplace. There are some interesting facts that pertain to the use of AI in retail.

Product search is one of the key components in an online retail store. Essentially, you need a system that matches a text query with a set of products in your store. A good product search can understand user's query in any language, retrieve as many relevant products as possible, and finally present the result as a list, in which the preferred products should be at the top, and the irrelevant products should be at the bottom. Google web search), products are structured data. A product is often described by a list of key-value pairs, a set of pictures and some free text. In the developers' world, Apache Solr and Elasticsearch are known as de-facto solutions for full-text search, making them a top contender for building e-commerce product search. At the core, Solr/Elasticsearch is a symbolic information retrieval (IR) system.