It’s no secret that AI is revolutionizing every aspect of marketing. From automated customer service to more data-driven approaches to marketing to chatbots, there is no area of marketing that is not seeing a seismic shift thanks to the rising prevalence of AI. According to Hubspot, 63% of consumers are already using AI without even realizing it.
But for those of us who don’t have a PhD in Data Science, the language of AI is dense and difficult to understand. When we think of AI, we might imagine an apocalyptic sci-fi robot vs. human scenario, or a future where AI has infiltrated every facet of human life. But given its explosive potential, and its growing dominance, marketers can benefit enormously from having a working understanding of AI and what it can do.
That’s why we’ve come up with eight AI terms every marketer should know.
1. Artificial Intelligence
Even as you read this, you might not know what Artificial Intelligence is. That’s okay; we’re here to explain it to you! AI generally refers to a computer or machine that is programmed to exhibit human-like qualities of learning and problem solving. That means the machine is programmed to become progressively smarter and better able to “think” like a human without being reprogrammed.
2. Machine Learning
Machine learning is a type of AI technology where the machines are able learn for themselves, instead of being specifically programmed for each and every task. The machine adapts and learns from observed data instead of relying on explicitly hard-coded rules. A common example of machine learning is the recommendation engines that drive e-commerce sites. Your recommendations are never static because these engines factor in your recent browsing history, including products you’ve viewed, purchase habits of similar users, and product statuses.
3. Deep Learning
Deep learning is a subset of machine learning that programs computers to learn by example, like humans do. It processes large amounts of data and breaks it down into specific characteristics. Unlike other subsets of machine learning, deep learning uses raw data as opposed to structured data. Deep learning enables the computer to recognize speech, identify images, or make predictions. Self-driving cars, for instance, utilize deep learning to recognize stop signs, detect lanes and pedestrians, and learn how to interact with other drivers on the road.
4. Natural Language Processing (NLP)
Natural Language Processing, or NLP, is a branch of Artificial Intelligence that helps computers understand, digest, and parse human languages. NLP enables computers to detect structure such as grammar, semantics, and even meaning in human text. While NLP will undoubtedly enable chatbots to have sophisticated adult conversations in the future, we’re simply not there yet.
Chatbots might be everywhere in the marketing world right now, but many marketers are still not sure exactly what chatbots are. A chatbot is a computer program that mimics human conversation using artificial intelligence. Chatbots enable businesses to engage customers and gain their loyalty, automate routine tasks, provide customer service, and more. While robot chat might sound particularly futuristic and War of the Worlds like, it was actually developed in the 1950s.
6. Computer Vision
Computer vision concerns computers’ ability to see, identify, and process images and videos by extracting high-level information. For example, if you’re a makeup company deploying a chatbot, your computer vision can discern and process your skin color and facial features, and use them to make specific product recommendations.
7. Training Data
Training data is the initial data set used to build a machine-learning model. The computer needs to learn this data before it begins to generalize. When you first build a chatbot, for example, the bot has little understanding or information about your customers. But as people begin to interact with the bot, your bot learns more about this particular group of people, and understands how they behave and talk.
Features are the specific parameters to consider when creating a model. For example, if you are predicting the sale price of a house, the features you might consider could include size, location, number of bedrooms, season, or the sale prices of houses in the area. But of those five features, only three might be needed to create a model that accurately predicts the house sale.
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