Complete generative AI glossary for businesses
Get a crash course on the most important terms to understand this widely talked-about business innovation
Time to read: 8 minutes
Generative AI is making waves in the world of business and technology. So if you want to gain an edge in the competitive landscape or stay informed, understanding the terminology related to generative AI is essential.
This glossary presents crucial AI terms and divides them into 3 categories for ease of understanding. Within each category, we present the terms not in alphabetical order, but rather in the order most intuitive for building on previous concepts.
Let’s dive in.
AI is the broad concept of machines performing tasks that typically require human intelligence, such as problem-solving, perception, or reasoning.
ML, a subset of AI, is the practice of using algorithms to parse data, learn from it, and make predictions or decisions without explicitly programming a machine to perform the task.
Deep learning, a subset of ML, models high-level abstractions in data by using multiple processing layers, each refining the interpretation of the data from the preceding layer.
For example, in image recognition, while a traditional program might focus on predefined patterns, a deep learning algorithm will separate the image by features (like shapes, colors, and textures), and learn to recognize patterns in a layered manner—somewhat similar to how our brain recognizes objects.
Neural networks replicate the functioning of the human brain and are a cornerstone of deep learning. Designed to recognize patterns in data, these networks comprise interconnected layers of nodes (or neurons) that feed data into one another, enabling them to learn from and interpret data.
A model in ML is a specific representation learned from data by applying some ML algorithm. Unlike traditional software that follows preset instructions to produce results, an ML model learns from data and makes decisions based on what it has ingested.
For example, if you train a model with data about real estate sales, it could predict house prices in a specific location. But you wouldn't program it with specific rules like "if the house is in this neighborhood, add this much to the price." Instead, it learns the impact of location (and other features) on house prices based on the patterns it found in the training data.
Training refers to the phase in ML where the model learns from a data set. The goal is to achieve a level of knowledge that allows the model to make accurate predictions or decisions.
Supervised learning is a type of ML where the model receives labeled training data. The aim is for the model to use example input-output pairs, then learn a function that maps an input to an output.
For example, let’s consider the design of a system that determines whether an email is spam or not. In a supervised learning approach, this system would receive training with a lot of emails already labeled as spam or not spam. Then, the model would learn the characteristics of spam emails, such as certain phrases or patterns typically used. After sufficient training, the system can then classify new emails correctly as either spam or not spam based on its learning.
Unsupervised learning is an ML technique where the model learns from a data set without explicit labels, often used to discover hidden patterns or intrinsic structures within the input data.
An example of unsupervised learning is customer segmentation in the marketing industry. Suppose a company has a large customer base, and the marketing team wants to design targeted marketing strategies for specific consumers. However, they don't have any preexisting groups or labels for their customers. This is where unsupervised learning would come into play.
By using techniques like clustering, the marketing team can use the customers’ purchasing behavior data to divide them into distinct groups. These groups can represent customers with similar behaviors, preferences, or traits, even though the model doesn’t know what these groups would look like beforehand. The marketing team can then tailor their marketing campaigns to these newly identified customer segments.
A generator is a component that learns to generate new data resembling its training data.
NLP is a branch of AI focusing on the interaction between computers and humans through natural language. The goal is to enable machines to understand and respond to text or voice inputs in a human-like manner.
LMs are types of AI models that can understand and generate human language. Trained on vast amounts of text data, LMs learn the statistical structure of human language to understand it.
LLMs are sophisticated, expansive versions of LMs trained on a considerable volume of text data. These offer a more nuanced understanding and generation of human language.
The primary difference between a LM and a LLM is the scale of training data and model size. A regular LM might receive training on millions of documents and consist of hundreds of millions of parameters, which are the aspects of the model learned from the training data.
In contrast, LLMs get trained on billions of documents and can have hundreds of billions—or even trillions—of parameters. For example, GPT-3, one of the largest language models available, has 175 billion parameters. This massive scale allows LLMs to generate incredibly human-like text and comprehend more complex contexts in the text data.
GPT is a type of LLM developed by OpenAI. These models can generate human-like text by predicting the likelihood of a word given the previous words used in the text.
Transformer models are a type of neural network architecture that have been highly successful in NLP tasks. These models process input data in parallel (as opposed to sequentially), making them more efficient.
Self-attention is a mechanism used in transformer models that allows the model to assign importance (or attention) to different words in an input sequence when generating an output. This lets the model determine which words in a sentence are crucial for understanding the overall context.
For example, consider the following sentence: “I arrived at the bank after crossing the river.” The word "bank" can have multiple meanings, but the presence of "river" in the sentence provides crucial context. A self-attention mechanism allows the model to pay more attention to "river" when trying to understand the meaning of "bank," thereby inferring that "bank" here refers to the edge of the river, not a financial institution. This feature makes it especially powerful for tasks that involve understanding the context of language, such as translation, summarization, and sentiment analysis.
A token in NLP refers to a single unit of language data, typically a word or subword in a text document.
Fine-tuning is the process of training a pretrained model on a new data set to refine its performance. It’s a common practice in deep learning, as it requires less computational resources than training a model from scratch.
Generative AI is a subfield of AI focusing on creating new content, including images, music, voice, or text. It learns from existing data and tries to generate similar content.
Sentiment analysis is an NLP technique used to determine the sentiment expressed in a piece of text.
For example, a researcher may use sentiment analysis to categorize tweets about a recent event in the news, determining whether each tweet sees the event favorably, unfavorably, or neutral.
AI bias can occur when systems reflect and even amplify existing biases in the data trained on, potentially leading to unfair outcomes.
Explainability/interpretability is the ability to understand and interpret the decisions made by an AI system. This is essential for trust and transparency, especially in industries like healthcare and finance.
Hallucination in AI refers to a model generating outputs not grounded in its input data. It's a common concern in generative AI, such as when LLMs produce plausible but incorrect or nonsensical information.
For example, if you ask an LLM a historical question like, "What was the outcome of the battle between Napoleon and aliens?" the LLM might provide a detailed and imaginative answer even though such an event never occurred. This is a form of hallucination because the LLM created a scenario that doesn't exist in its training data or in real history. Awareness of this tendency in generative AI systems is critical, especially when generating accurate and reliable information.
Generalization is the ability of an AI model to apply knowledge learned from training data to unseen data in a relevant and accurate manner.
Robustness refers to the ability of an AI system to continue operating effectively under varying or challenging conditions, including handling new inputs or coping with adversarial attacks.
Data privacy in AI is the practice of ensuring data privacy and protection. This is crucial in AI as models often require large amounts of data for training. The data can include confidential or sensitive information, like health records, financial information, or intellectual property. Breaches of data privacy could occur when not handled correctly. Therefore, strong data privacy practices when collecting, storing, and using data for AI training are essential.
AI governance ensures that AI systems operate ethically and transparently, in the best interests of all stakeholders. This often involves developing guidelines or policies around the use and impact of AI.
AIaaS is the outsourcing of AI capabilities or services through cloud-based platforms, enabling businesses to utilize AI without substantial upfront investment.
AutoML refers to the tools and techniques used to automate the application of machine learning. These might include data preprocessing, model selection, and hyperparameter tuning.
Data augmentation is a technique to increase the amount of training data by adding slightly modified copies of existing data, thereby improving the performance of the model.
Edge AI refers to running AI algorithms on end devices such as smartphones or Internet of Things devices, allowing data processing at the source and ensuring real-time processing and privacy.
An excellent example of edge AI is the self-driving capability of Tesla cars. These vehicles use advanced AI capabilities to interpret sensor data and make driving decisions in real time. This requires significant processing power of data and fast decision-making directly in the vehicle—something not possible if the vehicle had to constantly send data to the cloud for processing.
Reinforcement learning is an ML technique where an agent learns to make decisions by taking actions in an environment to maximize some type of reward or positive feedback.
A chatbot is a software application that simulates human conversation, either spoken or text-based, using predefined rules or AI technologies like NLP.
Employing generative AI to engage customers or run operations may be a game changer for your organization. While this glossary is an excellent starting point for familiarizing yourself with the key concepts, perhaps your business is ready to take it to the next level.
You can leverage the power of AI in your business with Twilio’s CustomerAI, Twilio’s AI-powered tools that help you personalize customer experiences, transform the quality of your customer service, and surface key insights about your customers and their behavior. Contact our experts today to learn more and get started.
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