If you Google “AI terms marketers should know” you’ll find quite a few posts that include many of the same AI terms. Common AI terms include machine learning (ML), algorithm, supervised learning, unsupervised learning, and weak AI. Today we thought we would highlight a few less common, but just as important, AI terms marketers should know.
Machine learning is a subset of AI. Automated machine learning (AutoML) is a rising trend within the field of machine learning. There is currently no universally agreed upon definition for AutoML. However, a recent Google Research article explains that “the goal of automating machine learning is to develop techniques for computers to solve new machine learning problems automatically, without the need for human-machine learning experts to intervene on every new problem.”
Companies in nearly every industry are using machine learning, and AutoML allows repetitive machine learning tasks to be automated. Our DRIVE-R Framework, the analytics layer of our platform, is powered by AutoML.
Most machine learning and AI techniques involve algorithms that are fed labeled training data. A machine learning algorithm is fed training data, and once the algorithm has been trained, a model of the patterns and values the algorithm has learned is saved. Organizations build ML models to solve specific problems. For example, a marketing company could build an ML model to predict customer purchasing patterns or to generate a churn score.
Python is a general-purpose programming language that is used for a wide range of applications and use cases. Python is one of the top tools used for data science and machine learning. One reason Python is so popular is the sheer number of Python-supported libraries available (more than 150,000). Many AI and ML Python libraries are available such as TensorFlow, NLTK (Natural Language Toolkit), and Scikit-learn. Companies developing AI-driven platforms often use Python (our data scientists and engineers use Python). Python may soon be the most popular language in marketing so it’s a term every marketer should know.
Conversational interfaces are extremely popular these days and across a wide range of industries including marketing, healthcare, banking, and travel. Conversational AI is an umbrella term for technologies that allow computers to simulate human-like, natural sounding conversations. Chatbots and virtual assistants are examples of conversational AI. Conversational AI is a term that marketers should know as conversational AI technologies offer great potential for marketing. For example, a chatbot could be programmed for a wide variety of marketing use cases such as recommend marketing materials or to answer questions about specific products.
Deep learning is the broad term for a class of algorithms that are a learning method in neural networks. A neural network is a computer system that is modeled after the functionality of the human brain. Deep learning algorithms reduce, and in some cases eliminate, the amount of time spent on feature engineering. This is because deep learning algorithms learn feature representations automatically. Deep learning algorithms are often used to enhance applications of machine learning such as computer vision and speech recognition. Deep learning can also be used to enhance analytics techniques including customer analytics and video analytics.
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There are many AI terms and concepts marketers should learn. You can find blog posts about AI and customer intelligence on our blog.
Janet Wagner is a Zylotech contributing writer.
If you liked this post, check out our recent blog post: Python many soon be the most popular language in marketing.