AI Vocabulary Flashcards
Artificial neural network (ANN)
An Artificial Neural Network (ANN) is a computer program that mimics the way human brains process information. Our brains have billions of neurons connected together, and an ANN (also referred to as a “neural network”) has lots of tiny processing units working together. Think of it like a team all working to solve the same problem. Every team member does their part, then passes their results on. In the end, you get the answer you need.
What it means for customers: Customers benefit in all sorts of ways when ANNs are solving problems and making accurate predictions – like highly personalized recommendations that result in a more tailored, intuitive, and ultimately more satisfying customer experience. Neural networks are excellent at recognizing patterns, which makes them a key tool in detecting unusual behavior that may indicate fraud. This helps protect customers’ personal information and financial transactions.
What it means for teams: Teams benefit, too. They can forecast customer churn, which prompts proactive ways to improve customer retention. ANNs can also help in customer segmentation, allowing for more targeted and effective marketing efforts. In a CRM system, neural networks could be used to predict customer behavior, understand customer feedback, or personalize product recommendations.
Augmented Intelligence
Think of augmented intelligence as a melding of people and computers to get the best of both worlds. Computers are great at handling lots of data and doing complex calculations quickly. Humans are great at understanding context, finding connections between things even with incomplete data, and making decisions on instinct. Augmented intelligence combines these two skill sets. It’s not about computers replacing people or doing all the work for us. It’s more like hiring a really smart, well-organized assistant.
What it means for customers: Augmented intelligence lets a computer crunch the numbers, but then humans can decide what actions to take based on that information. This leads to better service, marketing, and product recommendations for your customers.
What it means for teams: Augmented intelligence can help you make better and more strategic decisions. For example, a CRM system could analyze customer data and suggest the best time for sales or marketing teams to reach out to a prospect, or recommend products a customer might be interested in.
Conversational AI
Conversational AI allows us to use everyday language when interacting with artificial intelligence. Using technologies like natural language processing (reference NLP definition), machine learning, and speech recognition, AI can understand questions and instructions, which helps it provide better responses. Engaging with AI becomes more natural and effortless, requiring no special training. In the past, you’d have to put awkwardly-worded terms into a search engine to find what you were looking for. With conversational AI, you can simply state the request as you would with another person.
What it means for customers: Conversational AI allows for convenient, 24/7 support by chatbots that can resolve queries, provide product information, or guide users through a process, all using natural language. Interactions become smoother and more personalized, enhancing customer satisfaction.
What it means for teams: Conversational AI means your teams can just talk to your CRM naturally and have it take actions for them. So sales reps can simply ask for a status report on a new lead, marketing managers can request for a new campaign to be created, and customer service agents can reroute an order. It’s all done by using the same words with AI as they would use with a colleague.
Deep learning
Deep learning is an advanced form of AI that helps computers become really good at recognizing complex patterns in data. It mimics the way our brain works by using what’s called layered neural networks (see artificial neural network (ANN) above), where each layer is a pattern (like features of an animal) that then lets you make predictions based on the patterns you’ve learned before (ex: identifying new animals based on recognized features). It’s really useful for things like image recognition, speech processing, and natural-language understanding.
What it means for customers: Deep learning-powered CRMs create opportunities for proactive engagement. They can enhance security, make customer service more efficient, and personalize experiences. For example, if you have a tradition of buying new fan gear before each football season, deep learning connected to a CRM could show you ads or marketing emails with your favorite team gear a month before the season starts so you’ll be ready on game day.
What it means for teams: In a CRM system, deep learning can be used to predict customer behavior, understand customer feedback, and personalize product recommendations. For example, if there’s a boom in sales among a particular customer segment, a deep learning-powered CRM could recognize the pattern and recommend increasing marketing spend to reach more of that audience pool.
Discriminator
In a Generative Adversarial Network (GAN), the discriminator is like a detective. When it’s shown pictures (or other data), it has to guess which are real and which are fake. The “real” pictures are from a dataset, while the “fake” ones are created by the other part of the GAN, called the generator (see generator below). The discriminator’s job is to get better at telling real from fake, while the generator tries to get better at creating fakes. This is the software version of continuously building a better mousetrap.
What it means for customers: Discriminators in GANs are an important part of fraud detection. Using them leads to a more secure customer experience.
What it means for teams: Discriminators in GANs helps your team evaluate the quality of synthetic data or content and aid in fraud detection and personalized marketing.
Ethical AI maturity model
An Ethical AI maturity model is a framework that helps organizations assess and enhance their ethical practices in using AI technologies. It maps out the ways organizations can evaluate their current ethical AI practices, then progress toward more responsible and trustworthy AI usage. It covers issues related to transparency, fairness, data privacy, accountability, and bias in predictions.
What it means for customers: Having an ethical AI model in place, and being open about how you use AI, helps build trust and assures your customers that you are using their data in responsible ways.
What it means for teams: Regularly evaluating your AI practices and staying transparent about how you use AI can help you stay aligned to your company’s ethical considerations and societal values.
Explainable AI (XAI)
Remember being asked to show your work in math class? That’s what we’re asking AI to do. Explainable AI (XAI) should provide insight into what influenced the AI’s results, which will help users to interpret (and trust!) its outputs. This kind of transparency is always important, but particularly so when dealing with sensitive systems like healthcare or finance, where explanations are required to ensure fairness, accountability, and in some cases, regulatory compliance.
What it means for customers: If an AI system can explain its decisions in a way that customers understand, it increases reliability and credibility. It also increases user trust, particularly in sensitive areas like healthcare or finance.
What it means for teams: XAI can help employees understand why a model made a certain prediction. Not only does this increase their trust in the system, it also supports better decision-making and can help refine the system.
Spreadsheets
Spreadsheets, such as Microsoft Excele or Google Sheetse, organize data in a flat structure, which means the records are stored as single rows of data.
Relational Databases
Relational databases store data in multiple tables, with each row assigned a unique identifier. Users pull data from different tables together using Structured Query Language (SQL). The “relational” aspect indicates a logical connection between different tables.
Cloud Data
Sometimes, users prefer to store their data in the cloud. This includes data stored in such places as Amazon Web Services or Microsoft Azure.
