May 17, 2023

AI 101 - what marketers and their data friends need to know

Date & Time (GMT):
May 17, 2023 12:47 PM
Date & Time (EST):
May 17, 2023 12:47 PM

With more data swirling around us each day, the demand for smart machines to help sort through it, process it, spot trends, and automate how we turn it into business value has never been higher. This is why we have entered the golden era of data science. And why artificial intelligence (AI) - in all its forms - is truly all around us, in our smart speakers and appliances, in our cars, and on our favorite eCommerce sites. AI has become ubiquitous at home, and in our work too. It’s time for data and insights teams, brand strategists, and even creatives to get on board with AI.

Why has AI gone mainstream now? It’s certainly not a new topic. In fact, scientists gathered for the Dartmouth Summer Research Project on AI in 1956, and the release of the preliminary version of the AI programming language, Prolog, occurred in 1971. Throughout its history, AI has been interdisciplinary, with commercial advances funded by both public and private initiatives, and developments in all corners of the globe. Artificial intelligence has certainly been through boom and bust cycles over the past 70 years. But one constant is the search for smart machines to solve a wide range of businesses’ and society’s challenges, and approaches being inspired by and mirroring the two general types of brain activity: top-down cognitive processes and lower-level or bottom-up perceptual processes. Many readers will be happy to gain the benefits of AI through a social listening or AI-enabled consumer intelligence (AICI) platform. Others may be looking at more general-purpose AI/ML solutions from a cloud provider like Amazon, Google or Microsoft. And a few of you may be inspired to build your own models and start exploring the universe of open source tools, libraries, and communities.

The two primary branches of AI

For marketers, the best way to envision the potential of AI is thinking about your last online shopping experience. Odds are you encountered a virtual assistant powered by AI, saw tailored recommendations and received personalized offers (or extra-personal service) after making a purchase, raving about it on your social channels, filling out a post-purchase survey, etc. These functions are commonly powered by a collection of techniques from the two primary branches of AI: reasoning systems and learning systems. At a high level, reasoning systems offer guidance and help to automate manual processes that teams conduct everyday. They are a type of AI that is programmed or guided, follow a logical process, and map conditions to actions. They may operate autonomously (like a factory robot picking an order) or interactively with humans to gather needs, ask clarifying questions, etc. Examples include chatbots, shopping recommendation engines, and guided “helper” tools in everything from your CRM to your creative design tools.

In contrast, learning systems help teams spot patterns in social or other consumer data and make predictions. Learning systems apply algorithms that are trained vs programmed, and can be supervised or unsupervised. They can be focused on two types of tasks: predicting something that has happened before like seasonal shopping behavior or spotting an anomaly that hasn’t happened before, like a new consumer fashion trend. In general, a system that applies machine learning will “learn” from some combination of training data, prior knowledge, and trial and error reinforcement, with the goal to predict, classify, or cluster new data. Today, a popular (and powerful) flavor of learning is so-called “deep learning.” These methods apply multi-layered neural networks and can be extremely powerful in high-complexity fields like machine vision and bioinformatics, and in fact, are applied in Synthesio’s image and logo recognition capability. At the same time, some of the most popular AI applications like natural language processing apply both reasoning and learning approaches together, to help brand and insights teams better understand what their audience is saying online, track evolving consumer behavior and trends by what they are posting on Twitter or Reddit (or your feedback surveys!), and even identify opportunities for innovation.

Machines are great...but they need human helpers

Most learning systems cannot explain their outcome without a human helper adding context or sharing details on how the system was trained, what performance it achieved, etc. And reasoning systems need to be programmed, and tested, and updated. And all techniques need to be monitored for accuracy as well as underlying biases, so they can be corrected or presented up front to the user. For these reasons, we continue to see the benefits of a “hybrid” approach where humans and machines work together, even when advanced AI or ML does the heavy lifting behind the scenes. This is especially valuable for high-value consumer insights, brand health, and product innovation use cases where there is both data to understand, and a structured process to follow. Marketers and creatives are certainly going to see more innovation and use cases when it comes to AI in the years to come. AI is still the “new, new thing” for many - even though it’s been around in various forms for 70 years! It’s not too late to master AI 101 and get ready to put smart machines to work in your everyday tasks.

This article was originally posted on LinkedIn

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