Glossary of Gen AI terms every enterprise AI adopters must know

Ever wondered how computers can do things that seem super smart, like recognising images or translating languages? Well, it's all thanks to artificial intelligence (AI). AI is about creating machines that can be as smart as humans, and it has come a long way!

It all started with something called deep learning, which is a fancy term for teaching computers using special brain-inspired networks. Deep learning has been responsible for some mind-blowing advances in AI. But hold on, the excitement doesn't stop there!

Enter generative AI, the latest game-changer. Generative AI is like a whole new level of smarts. It's powered by super-duper models called foundation models, which are inspired by the way our own brains work with billions of interconnected neurons.

Unlike earlier models, these foundation models can handle all sorts of messy, unstructured data and do more than one task at a time. They're like AI superheroes! In this blog post, we'll dive into the world of generative AI and explore some cool terms that every business using AI should know.

So, let's get ready to unravel the mysteries of generative AI and discover how it's revolutionising the way machines think and learn!

Let’s get started with the terms!

1) Artificial neural networks (ANNs) also known as neural networks are composed of interconnected layers of software-based calculators known as “neurons.” These networks can absorb vast amounts of input data and process that data through multiple layers that extract and learn the data’s features.

2) Deep learning is a subset of machine learning that uses deep neural networks, which are layers of connected “neurons” whose connections have parameters or weights that can be trained. It is especially effective at learning from unstructured data such as images, text, and audio.

3) Fine-tuning is the process of adapting a pretrained foundation model to perform better in a specific task. This entails a relatively short period of training on a labeled data set, which is much smaller than the data set the model was initially trained on. This additional training allows the model to learn and adapt to the nuances, terminology, and specific patterns found in the smaller data set.

4) Foundation models (FM) are deep learning models trained on vast quantities of unstructured, unlabeled data that can be used for a wide range of tasks out of the box or adapted to specific tasks through fine-tuning. Examples of these models are GPT-4, PaLM, DALL·E 2, and Stable Diffusion.

5) Generative AI is AI that is typically built using foundation models and has capabilities that earlier AI did not have, such as the ability to generate content. Foundation models can also be used for non generative purposes (for example, classifying user sentiment as negative or positive based on call transcripts) while offering significant improvement over earlier models. For simplicity, when we refer to generative AI in this article, we include all foundation model use cases.

6) Labeled dataset is a collection of data points or examples where each data point is associated with one or more pre-defined labels or categories. In the context of supervised machine learning, a labeled dataset is crucial for training algorithms to make accurate predictions or classifications.

7) Large language models (LLMs) make up a class of foundation models that can process massive amounts of unstructured text and learn the relationships between words or portions of words, known as tokens. This enables LLMs to generate natural-language text, performing tasks such as summarization or knowledge extraction. GPT-4 (which underlies ChatGPT) and LaMDA (the model behind Bard) are examples of LLMs.

8) Machine learning (ML) is a subset of AI in which a model gains capabilities after it is trained on, or shown, many example data points. Machine learning algorithms detect patterns and learn how to make predictions and recommendations by processing data and experiences, rather than by receiving explicit programming instruction. The algorithms also adapt and can become more effective in response to new data and experiences.

9) Modality is a high-level data category such as numbers, text, images, video, and audio.

10) Natural Language Processing (NLP) is a branch of AI that focuses on enabling computers to understand, interpret, and generate human language. NLP enables tasks such as language translation, sentiment analysis, and chatbot interactions.

11) Neural Network also known as Artificial neural networks (ANNs) is a computational model inspired by the structure and functioning of the human brain. Neural networks consist of interconnected nodes (neurons) that process and transmit information to make predictions or decisions.

12) Prompt engineering refers to the process of designing, refining, and optimising input prompts to guide a generative AI model toward producing desired (that is, accurate) outputs. Self-attention, sometimes called intra-attention, is a mechanism that aims to mimic cognitive attention, relating different positions of a single sequence to compute a representation of the sequence.

13) Reinforcement Learning is a type of ML where an agent learns to make decisions and take actions in an environment to maximize rewards. The agent receives feedback in the form of rewards or penalties, guiding its learning process.

14) Structured data are tabular data (for example, organised in tables, databases, or spreadsheets) that can be used to train some machine learning models effectively.

15) Training Data is the data used to train an AI model or system. High-quality and representative training data is crucial for the performance and accuracy of AI algorithms.

16) Transformers are a modern neural network design that uses self-attention mechanisms to convert input sequences into output sequences. They pay special attention to important parts of the context surrounding the inputs. Unlike traditional methods, Transformers don't use convolutions or recurrent neural networks.

17) Unstructured data lack a consistent format or structure (for example, text, images, and audio files) and typically require more advanced techniques to extract insights.

Conclusion:

So there you have it! We've explored the fascinating world of Generative AI and its key terms, taking you from the basics of AI and deep learning to the game-changing power of foundation models and Transformers. With these innovative advancements, machines are becoming smarter than ever, capable of understanding and generating content like never before. As AI continues to revolutionise various industries, understanding these terms is essential for any enterprise embracing AI technologies. So, get ready to unleash the potential of Generative AI and witness the incredible ways it's transforming the world of AI! Keep exploring, and happy learning!