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"Does it seem like everyone around you is casually tossing around terms like “generative AI,” “large language models,” or “deep learning”? Feeling a little lost on the details? We’ve created a primer on everything you need to know to understand the newest, most impactful technology that’s come along in decades. Let’s dive into the world of generative AI.
We’ve put together a list of the most essential terms that will help everyone in your company — no matter their technical background – understand the power of generative AI. Each term is defined based on how it impacts both your customers and your team."
And to highlight the real-world applications of generative AI, we put it to work for this article. Our experts weighed in on the key terms, and we let a generative AI tool lay the groundwork for this glossary. Each definition needed a human touch to get it ready for publication, but it saved loads of time.
AI is the broad concept of having machines think and act like humans. Generative AI is a specific type of AI (more on that below).
An 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. It’s like a team all working to solve the same problem. Every team member does their part, then passes their results on. At the end, you get the answer you need. With humans and computers, it’s all about the power of teamwork.
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-organised assistant.
CRM is a technology that keeps customer records in one place to serve as the single source of truth for every department, which helps companies manage current and potential customer relationships. Generative AI can make CRM even more powerful — think personalised emails pre-written for sales teams, ecommerce product descriptions written based on images alone, marketing campaign landing pages, contextual customer service ticket replies, and more.
Deep learning is an advanced form of AI that helps computers become really good at recognising complex patterns in data. It mimics the way our brain works by using what’s called layered neural networks, 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 recognised features). It’s really useful for things like image recognition, speech processing, and natural-language understanding.
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. 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.
Your customers expect you to use AI responsibly. You need to implement an ethical AI practice to develop and operationalise principles like transparency, fairness, responsibility, accountability, and reliability. Here’s how.
An Ethical AI maturity model is a framework that helps organisations assess and enhance their ethical practices in using AI technologies. It maps out the ways organisations 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.
Remember being asked to show your work in maths 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 important when dealing with sensitive systems like healthcare or finance, where explanations are required to ensure fairness, accountability, and in some cases, regulatory compliance.
Generative AI is the field of artificial intelligence that focuses on creating new content based on existing data. For a CRM system, generative AI can be used to create a range of helpful things, from writing personalised marketing content, to generating synthetic data to test new features or strategies.
One of two deep learning models, GANs are made up of two neural networks: a generator and a discriminator. The two networks compete with each other, with the generator creating an output based on some input, and the discriminator trying to determine if the output is real or fake. The generator then fine-tunes its output based on the discriminator’s feedback, and the cycle continues until it stumps the discriminator.
GPT is a neural network family that is trained to generate content. GPT models are pre-trained on a large amount of text data, which lets them generate clear and relevant text based on user prompts or queries.
The world’s first generative AI for CRM lets you deliver AI-created content across every sales, marketing, service, commerce, and IT interaction, at scale. It’s a total game-changer for your company.
A generator is an AI-based software tool that creates new content from a request or input. It will learn from any supplied training data, then create new information that mimics those patterns and characteristics. ChatGPT by OpenAI is a well-known example of a text-based generator.
A hallucination happens when generative AI analyses the content we give it, but comes to an erroneous conclusion and produces new content that doesn’t correspond to reality. An example would be an AI model that’s been trained on thousands of photos of animals. When asked to generate a new image of an “animal,” it might combine the head of a giraffe with the trunk of an elephant. While they can be interesting, hallucinations are undesirable outcomes and indicate a problem in the generative model’s outputs.
An LLM is a type of artificial intelligence that has been trained on a lot of text data. It’s like a really smart conversation partner that can create human-sounding text based on a given prompt. Some LLMs can answer questions, write essays, create poetry, and even generate code.
Machine learning is how computers can learn new things without being programmed to do them. For example, when teaching a child to identify animals, you show them pictures and provide feedback. As they see more examples and receive feedback, they learn to classify animals based on unique characteristics. Similarly, machine learning models learn from labelled data to make accurate predictions and decisions. They generalise and apply their knowledge to new examples, just as humans do.
We’ve all heard the phrase “garbage in, garbage out,” right? Machine learning bias is just a turbocharged AI version of that. When computers are fed biassed information, they make biassed decisions. This can be the result of a deliberate decision by the humans feeding the computer data, by accidentally incorporating biassed data, or when the algorithm makes wrong assumptions during the learning process, leading to biassed results.
Example: If a loan approval model is trained on historical data that shows a trend of approving loans for certain demographics (like gender or race), it may learn and perpetuate those biases. This isn’t because of a prejudice in the system, but a bias in the training data. It will have huge implications for the accuracy and effectiveness of the system, and help build equality and trust among customers.
This is a program that’s been trained to recognise patterns in data. You could have a model that predicts the weather, translates languages, identifies pictures of cats, etc. Just like a model aeroplane is a smaller, simpler version of a real aeroplane, an AI model is a mathematical version of a real-world process.
NLP is a field of artificial intelligence that focuses on how computers can understand, interpret, and generate human language. It’s the technology behind things like voice-activated virtual assistants, language translation apps, and chatbots.
You don’t need an engineering degree for this one. Prompt engineering means figuring out how to ask a question to get exactly the answer you need. It’s carefully crafting or choosing the input (prompt) that you give to a machine learning model to get the best possible output.
Sentiment analysis involves determining the emotional tone behind words to gain an understanding of the attitudes, opinions, and emotions of a speaker or writer. It is commonly used in CRM to understand customer feedback or social media conversation about a brand or product.
Supervised learning is when a model learns from examples. It’s like a teacher-student scenario: the teacher provides the student (the model) with questions and the correct answers. The student studies these, and over time, learns to answer similar questions on their own. It’s really helpful to train systems that will recognise images, translate languages, or predict likely outcomes. (Check out unsupervised learning below).
Transformers are a type of deep learning model, and are especially useful for processing language. They’re really good at understanding the context of words in a sentence because they create their outputs based on sequential data (like an ongoing conversation), not just individual data points (like a sentence without context). The name “transformer” comes from the way they can transform input data (like a sentence) into output data (like a translation of the sentence).
Unsupervised learning is letting AI find hidden patterns in your data without any guidance. This is all about allowing the computer to explore and discover interesting things on its own. Imagine you have a big bag of mixed-up puzzle pieces, but you don’t have the picture on the box to refer to, so you don’t know what you’re making. Unsupervised learning is like figuring out how the pieces fit together, looking for similarities or groups without knowing what the final picture will be.
In machine learning, validation is a step used to check how well a model is doing during or after the training process. The model is tested on a subset of data (the validation set) that it hasn’t seen during training, to ensure it’s actually learning and not just memorising answers. It’s like a pop quiz for AI in the middle of the semester.
The Zone of Proximal Development (ZPD) is an education concept. For example, each year students progress their maths skills from adding and subtracting, to multiplication and division, and even up to complex algebra and calculus equations. The key to advancing is progressively learning those skills. In machine learning, ZPD is when models are trained on progressively more difficult tasks, so they will improve their ability to learn.
Generative AI has the power to help all of your teams connect more closely with your customers, unlock creativity, and increase productivity. From a business perspective, there’s almost no part of your organisation that AI can’t make more efficient. Sales, service, marketing, and commerce applications are all able to use the power of generative AI to deliver better, more tailored solutions to your customers, and to do so quickly.
By letting AI assist us with the more routine tasks of helping our customers thrive, we’ll be able to free our human teams to do what they do best — come up with new ideas and new ways to collaborate, all while building those unique connections that only humans can.
Now that you’re up to speed on Generative AI for CRM, see it in action.
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