Beginners Generative AI Listicle

Explore the vast artificial intelligence and machine learning field with this alphabetized guide below. From Agents and AGI to Zero-shot Learning and everything in between, explore the intricate language of AI with concise explanations and vivid examples. Whether you’re a seasoned practitioner or a curious novice, these Generative AI terms aim to be your go-to reference, unraveling the intricacies of the AI realm.

A

1. AGENTS

Software robots that can independently perceive and act within their environment to achieve goals, like booking flights or navigating virtual worlds. Imagine a tiny AI assistant helping you manage your online life.

2. AGI (ARTIFICIAL GENERAL INTELLIGENCE)

A hypothetical AI capable of understanding and learning any intellectual task a human can, achieving human-level performance across various domains, not just one specific task. Think of a super-intelligent machine that can write poetry, diagnose diseases and compose symphonies.

3. ALIGNMENT

Ensuring AI goals and values are compatible with human values, preventing harmful or unintended consequences. It’s like training a puppy to understand what behavior is good and bad for the household.

4. ATTENTION

Mechanisms in neural networks that selectively focus on important parts of the input data, similar to how you might pay attention to a specific speaker during a crowded conversation.

5. AUTOENCODERS

Neural networks that learn compressed representations of data and then reconstruct the original data from those representations, like a secret code for images or music.

B

6. BACK PROPAGATION

An algorithm that lets neural networks learn by figuring out how much to adjust their internal connections based on how well they perform on a task, like a student correcting their mistakes based on feedback.

7. BIAS

Assumptions baked into AI models, often unintentionally, that can lead to unfair or discriminatory outcomes. It’s like a faulty scale always tipping slightly to one side, skewing the results.

8. BIGGAN

A powerful type of GAN known for generating incredibly realistic and high-resolution images, like painting you a picture so real it feels like you can step into it.

C

9. CAPSULE NETWORKS:

Networks using capsules instead of neurons to capture spatial relationships and parts of objects, like recognizing a chair with legs and back, not just a blob of pixels.

10. CHAIN OF THOUGHT

A proposed way for AI models to explain their reasoning process by showing a sequence of intermediate conclusions leading to the final decision, like tracing the steps in a mathematical proof.

11. CHATBOT

Computer programs designed to simulate conversation with humans, like your friendly virtual assistant answering your questions and booking appointments.

12. CHATGPT

OpenAI’s large language model known for its ability to generate human-quality text and engage in open-ended conversations, like chatting with a witty and knowledgeable friend.

13. CLIP (CONTRASTIVE LANGUAGE –IMAGE PRETRAINING)

An AI model that can connect text and images, understanding what a picture is about and describing it accurately, like a multilingual translator for visual language.

14. CNN (CONVOLUTIONAL NEURAL NETWORK)

Models specialized in processing data arranged in grids, like images, by identifying patterns and features within them, similar to how your eyes scan a picture to recognize objects.

15. CONDITIONAL GAN (CGAN)

A GAN that can generate data based on specific additional information, like creating faces that fit a certain age or mood, like a fashion designer with unlimited fabric and imagination.

16. CYCLEGAN

A model that translates images from one style to another without needing paired examples, like transforming a cityscape into a watercolor painting.

D

17. DATA AUGMENTATION

Artificially increasing the amount and diversity of training data to make AI models more robust and generalizable, like giving a student a variety of practice problems to prepare for a test.

18. DEEPSPEED

DeepSpeed is a system for training large language models on distributed systems. It uses a variety of techniques to improve the efficiency and scalability of training, such as ZeRO-Offloading, Megatron-Turing NLG, and mixed precision training. DeepSpeed has been shown to significantly reduce the time and cost of training large language models.

19. DIFFUSION MODELS

A new technique for generating data by gradually adding and then reversing noise, like slowly revealing a hidden image by erasing random brushstrokes.

20. DOUBLE DESCENT

A phenomenon where increasing the complexity of an AI model can initially hurt its performance before eventually improving it, like a rollercoaster with dips and rises before the final climb.

E

21. EMERGENCE/EMERGENT BEHAVIOR Complex and unexpected behavior arising from the interaction of simple rules in an AI system, like ants forming intricate patterns while following individual instructions.

22. EXPERT SYSTEMS

AI applications built with deep knowledge of a specific domain, like a medical diagnosis system drawing on vast medical databases.

F

23. FEW-SHOT LEARNING

This method trains models on a very small amount of data, typically a few examples per class. It’s designed to quickly adapt to new tasks with limited information, balancing the need for accuracy with the challenge of minimal data.

24. FINE-TUNING

Adapting a pre-trained AI model to a specific task by further training it on smaller amounts of relevant data, like customizing a general tool to tackle a particular job.

25. FORWARD PROPAGATION

The process in neural networks where input data flows through the network layers, transforming and generating the final output, like a recipe where ingredients go through different steps to create a dish.

26. FOUNDATION MODEL

A large and adaptable AI model serving as a base for developing various specialized applications, like a versatile building block for different kinds of tools.

G

27. GAN (GENERAL ADVERSARIAL NETWORK)

A type of AI where two models compete, one generating data and the other trying to distinguish it from real data, leading to increasingly realistic and sophisticated outputs, like two artists pushing each other to create better work.

28. GENERATIVE AI

Machine learning models capable of autonomously creating new content, such as images, text, music, or code. Unlike traditional AI models that analyze or classify data, generative AI focuses on creative exploration and output.

29. GPT (GENERATIVE PRETRAINED TRANSFORMER)

A large language model developed by OpenAI, known for its ability to generate human-quality text, translate languages, and write different kinds of creative content. It is trained on a massive dataset of text and code, allowing it to learn complex patterns and relationships within language.

30. GPU (GRAPHICS PROCESSING UNIT)

Specialized microprocessors designed for parallel processing, making them ideal for handling the computationally intensive tasks involved in AI training and inference. GPUs excel at computations involving arrays of data, which are common in image and video processing, data mining, and scientific computing.

31. GRADIENT DESCENT

An optimization algorithm used to improve the performance of machine learning models. It works by iteratively adjusting the model’s internal parameters in the direction that minimizes a loss function, which measures the difference between the model’s predictions and the true values.

32. HALLUCINATION

When AI models generate unrealistic or nonsensical content due to limitations in their training data, biases, or incomplete understanding of the task. This can include generating images of objects that don’t exist, writing text that doesn’t make sense, or making predictions that are factually incorrect.

33. HIDDEN LAYER

Layers in neural networks that are not directly connected to the input or output. These layers perform complex transformations on the data by learning internal representations that capture hidden patterns and relationships within the data. The number and structure of hidden layers play a crucial role in the capabilities and performance of neural networks.

34. HYPERPARAMETER TUNING

Adjusting settings in a machine learning model, such as the learning rate, number of hidden layers, or regularization parameters, to achieve optimal performance. Tuning these hyperparameters is crucial for finding the right balance between model complexity and generalizability.

I

35.INSTRUCTION TUNING

Fine-tuning a pre-trained machine learning model by further training it on a smaller dataset that includes specific instructions or guidelines. This can be used to adapt the model to a new task or improve its performance on a specific aspect of the original task.

L

36. LARGE LANGUAGE MODEL (LLM)

A machine learning model trained on a massive dataset of text and code, capable of generating human-quality text, translating languages, writing different kinds of creative content, and answering your questions in an informative way. LLMs are pushing the boundaries of natural language processing and are opening up new possibilities for human-computer interaction.

37. LATENT SPACE

A low-dimensional representation of data learned by a machine learning model. This compressed representation captures the essential features and relationships within the data, allowing the model to efficiently perform tasks such as image generation, translation, and anomaly detection.

37. LATENT DIFFUSION

Latent diffusion is a generative modeling technique that uses a diffusion process to gradually add noise to a latent representation of the data. By reversing this process, the model can learn to denoise the data and generate new samples that are similar to the training data. Latent diffusion models have been shown to be effective for generating high-quality images, text, and other types of data.

38. LLAMAINDEX

An indexing method specifically designed for large language models (LLMs). It improves the retrieval capabilities of LLMs by efficiently searching through large amounts of text and identifying relevant passages based on the user’s query.

39. LANGCHAIN

A framework for chaining together different language models with complementary capabilities. This enables the creation of more complex and versatile language processing systems that can handle diverse tasks requiring different skills and knowledge.

40. LLMOPS (LARGE LANGUAGE MODEL OPERATIONS)

This is a term used to describe the practices and tools involved in developing, deploying, and managing large language models. This includes tasks such as model training, inference, monitoring, and governance. As LLMs become increasingly complex and are used in more mission-critical applications, LLMOps is becoming an increasingly important area of focus.

CONCLUSION

Now equipped with a robust vocabulary, you’re ready to navigate the world of Generative AI with confidence. Keep these terms close to heart as you explore and contribute to the ever-evolving landscape of generative technologies. Whether you’re creating realistic images with GANs or delving into the nuances of Latent Space, this glossary serves as your go-to resource. Elevate your expertise and stay ahead in the dynamic field of Generative AI with these essential terms as your guide.