In recent months, the Internet has been set ablaze with the introduction for the public beta of ChatGPT. People across the world shared their thoughts on such an incredible development. ChatGPT relies on a subsection of machine learning, called large language models, that have already shown to be both immensely useful and potentially dangerous. I have sat down with an artificial intelligence and machine learning expert, Martynas Juravičius, from Oxylabs, a premium proxy and public web data-acquisition solution provider, and members of the company's AI advisory board, Adi Andrei and Ali Chaudhry, to discuss the importance of such models and how they may shape our future. Gary Drenik: What is a "large language model" and why are they so important going forward?

The insurance industry is one of the largest in the world and has been around for hundreds of years, making it ripe for transformation by newcomers in the space. Over the past five to ten years, technology has pushed the frontier of what's possible, making way for a new breed of digital-first insurance companies to come to life. The use of technology, data, artificial intelligence (AI), and modern design has created a powerful combination, changing what was once a very policy-centric industry to one that's customer-centric. I recently had the chance to speak to Daniel Schreiber, co-founder CEO of Lemonade, the digital insurance company powered by social impact with a mission dedicated to building the "most loveable insurance" available. During our conversation, he spoke about the company's use of AI and big data, how it impacts the customer experience, and the opportunities (and, at times, challenges) it brings to the industry.

Multiple times over the last decade, this column has covered the issue of the importance of data quality in decision making, both by executives as well as machines. Back in 2014, when the "big data" craze was mesmerizing the C-Suite, the warning was issued in Big Data and the Madness of Crowds. More recently in How Bad Data Is Undermining Big Data Analytics from December 2020. Since then, more and more news has emerged regarding the failures of AI and Machine Learning initiatives with the blame given to faulty data as the reason. The recent demise of IBM Watson Health is the latest example.

You might recall around the end of 2016, when seemingly overnight, the business media went from never having talked about artificial intelligence in business to ubiquitous coverage of its applications in marketing. A small Israeli-born artificial intelligence company named Albert had suddenly emerged as a driving force behind this conversation, getting in before Salesforce had a chance to utter the word "Einstein" at its annual Dreamforce conference and AI was still a sparkle in Oracle's eye. Behind Albert was New York City-based PR and Communications agency Channel V Media. I spoke with Kieran Powell, partner at Channel V Media, about how Channel V Media has carved out a niche by focusing on companies' narratives as a first step to transforming them into vocal market influencers. Gary Drenik: How can the messages a company puts out into the market have such a large influence on its standing in its industry?

When the pandemic required that retailers shut their doors, consumers still needed a way to reach the brands they use and interact with every day. Due to the need to go remote, the contact center quickly took the place of in-person retailing, enabling consumers to connect to brands by phone, chat, email and more. Contact centers had long been run on-premise, but these resources also had to change to accommodate the remote nature of daily life, and many retailers quickly shifted those systems to the cloud. In addition to enabling agents to service customers remotely, the cloud-enabled contact center also allows brands to harness data from all those interactions and make them smarter and more effective. I recently connected with Genefa Murphy, CMO of Five9, an intelligent cloud contact center solution, who spoke to me about how brands are reimagining the customer experience and the technology they use to facilitate it.

One can't read any news today without a barrage of articles about data science and machine learning and artificial intelligence. To better assess all of this talk and hype, I recently had the opportunity to sit down with Brian Sampsel, VP and Chief Analytics Innovator for the Columbus Collaboratory. Brian, when you look at all of this talk about machine learning, how are companies responding? Brian Sampsel: Given all the talk and hype, it is surprising to find that so many large enterprises are doing things the way they have always been done, especially as it pertains to back office functions. One glaring example of this is forecasting.