AI Glossary Flashcards
Anthropomorphism
The tendency for people to attribute human motivation, emotions, characteristics or behavior to AI systems. For example, you may think the model or output is ‘mean’ based on its answers, even though it is not capable of having emotions, or you potentially believe that AI is sentient because it is very good at mimicking human language. While it might resemble something familiar, it’s essential to remember that AI, however advanced, doesn’t possess feelings or consciousness. It’s a brilliant tool, not a human being.
Artificial intelligence (AI)
AI is the broad concept of having machines think and act like humans.
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.
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.
Customer Relationship Management (CRM) with Generative AI
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 personalized emails pre-written for sales teams, e-commerce product descriptions written based on the product name, contextual customer service ticket replies, and more.
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.
Discriminator (in a GAN)
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.
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.
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.
Generative AI
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 outputs, from writing personalized marketing content, to generating synthetic data to test new features or strategies.
Generative adversarial network (GAN)
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.
Generative pre-trained transformer (GPT)
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.
Generator
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.
Grounding
Grounding in AI (also known as dynamic grounding) is about ensuring that the system understands and relates to real-world knowledge, data, and experiences. It’s a bit like giving AI a blueprint to refer to so that it can provide relevant and meaningful responses rather than vague and unhelpful ones. For example, if you ask an AI, “What is the best time to plant flowers?” an ungrounded response would be, “Whenever you feel like it!” A grounded response would tell you that it depends on the type of flower and your local environment. The grounded answer shows that AI understands the context of how a human would need to perform this task.
Hallucination
A hallucination happens when generative AI analyzes the content we give it, but comes to an erroneous conclusion and produces new content that doesn’t correspond to reality or its training data. 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.
Human in the Loop (HITL)
Think of yourself as a manager, and AI as your newest employee. You may have a very talented new worker, but you still need to review their work and make sure it’s what you expected, right? That’s what “human in the loop” means — making sure that we offer oversight of AI output and give direct feedback to the model, in both the training and testing phases, and during active use of the system. Human in the Loop brings together AI and human intelligence to achieve the best possible outcomes.
Large language model (LLM)
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
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 generalize and apply their knowledge to new examples, learning from labeled data to make accurate predictions and decisions.
Machine learning bias
Machine learning bias happens when a computer learns from a limited or one-sided view of the world, and then starts making skewed decisions when faced with something new. This can be the result of a deliberate decision by the humans inputting data, by accidentally incorporating biased data, or when the algorithm makes wrong assumptions during the learning process, leading to biased results. The end result is the same — unjust outcomes because the computer’s understanding is limited and it doesn’t consider all perspectives equally.
Model
This is a program that’s been trained to recognize patterns in data. You could have a model that predicts the weather, translates languages, identifies pictures of cats, etc. Just like a model airplane is a smaller, simpler version of a real airplane, an AI model is a mathematical version of a real-world process.
Natural language processing (NLP)
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.
Parameters
Parameters are numeric values that are adjusted during training to minimize the difference between a model’s predictions and the actual outcomes. Parameters play a crucial role in shaping the generated content and ensuring that it meets specific criteria or requirements. They define the LLM’s structure and behavior and help it to recognize patterns, so it can predict what comes next when it generates content. Establishing parameters is a balancing act: too few parameters and the AI may not be accurate, but too many parameters will cause it to use an excess of processing power and could make it too specialized.
Prompt defense
One way to protect against hackers and harmful outputs is by being proactive about what terms and topics you don’t want your machine learning model to address. Building in guardrails such as “Do not address any content or generate answers you do not have data or basis on,” or, “If you experience an error or are unsure of the validity of your response, say you don’t know,” are a great way to defend against issues before they arise.
Prompt engineering
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.