Broadly speaking, there are three types of artificial intelligence models: reinforcement learning, unsupervised learning and supervised learning.

In a reinforcement learning model, trainers set a series of algorithms against a given problem, watch to see which perform the best and then continue to refine the programs as they get closer and closer to the target. In an unsupervised learning model, a large set of data — typically unlabeled — is fed into an algorithm, and the model begins sorting things into different categories on its own.

Neural networks are an example of a supervised AI model. Trainers feed labeled data into an algorithm, teaching it to recognize the subtle changes that distinguish, for instance, a dog from a cat in a photograph. Neural networks are designed to continually learn over time, gradually reducing their “loss” (or miss rate).

Neural networks are already creating value across numerous industries.

Since March 2020, retailers have made Herculean efforts to meet the moment of the COVID-19 crisis, implementing new tools and processes to help customers stay safe and still get what they need, when they need it.

Even as we signs that the pandemic is beginning to wind down, retailers will need to continue to innovate and build on these systems that customers have come to expect. Many customers have spent the past year largely avoiding physical stores, so the retailers that can make shoppers feel welcome and safe — in short, those that make customers feel like they want to come back even if they don’t strictly need to — will be the ones to succeed in the future. Here are several ways they can achieve this objective.

Retailers are using data, powered by artificial intelligence, to achieve an impressive list of objectives: loss prevention, operational efficiencies, customer insights and buying behavior enhancements.

One of retail’s most versatile data solutions is computer vision, a form of AI that enables computers to glean insights from the visual information flowing to digital cameras. Recommendation engines are also on the rise, letting retailers leverage data to deliver timely, customized experiences. These advances support easier, more personalized shopping for consumers — exactly what they’re looking for.

New technologies get a lot of attention. In fact, you probably hear a nearly constant stream of discussion about technologies such as artificial intelligence, virtual reality, machine learning and the Internet of Things. Sometimes, it even seems impossible to avoid the acronyms for these technologies — AI, VR, ML and IoT.

By comparison, radio frequency identification is largely ignored outside of certain trade journals. Retailers, however, should pay attention to RFID, which has provided the retail industry with a way to identify and track inventory for the past 15 years.

Retailers are looking for technology solutions that can meet a particular need, whether it’s improving the customer experience, enhancing productivity or increasing revenue. I was excited to see some of the latest entries in the field in January at the National Retail Federation’s annual convention.

Retailers, vendors and industry experts at the event demonstrated an array of solutions and shared their perspectives on how technology will affect the industry this year. Here are four takeaways from Retail’s Big Show.

Practically the entire retail industry may be using AI by the end of 2020. And yet, AI is still in its infancy in most deployments, with many retailers uncertain about exactly how the technology can improve their businesses.

That’s why I’m looking forward to a number of AI-focused sessions at this year’s annual convention of the National Retail Federation.