Intro to Generative AI.

Agenda

  • Define Generative AI

  • How Generative AI works?

  • Describe Generative AI Model Types.


Generative AI is an exciting field of artificial intelligence that focuses on creating intelligent systems capable of generating diverse types of content, such as text, images, audio, and even synthetic data. By harnessing the power of computer science and machine learning, generative AI enables computers to exhibit a level of creativity and problem-solving ability akin to human intelligence.

Machine learning, a subfield of AI, empowers systems to learn and improve from data without being explicitly programmed. Instead of relying on pre-defined rules, machine learning algorithms leverage training data to create models that can make accurate predictions or decisions on new, unseen data. Deep learning, a subset of machine learning, utilizes artificial neural networks to train and predict data. These neural networks consist of interconnected layers of mathematical functions that simulate the behavior of neurons in the human brain. By analyzing vast amounts of data and identifying intricate patterns, deep learning models can acquire a deep understanding of complex relationships, enabling them to generate high-quality predictions and outputs.

Within the realm of deep learning, generative AI stands out as a fascinating domain. It encompasses the use of artificial neural networks to learn from both labeled and unlabeled data, giving rise to systems that can generate new content. Whether it's generating coherent paragraphs of text, creating realistic images, composing music, or even producing synthetic data for training purposes, generative AI showcases the remarkable capacity of computers to mimic and extend human creativity.

One prominent example of generative AI is the development of large language models (LLMs). These models are trained on vast quantities of text data and possess the ability to generate human-like responses, carry on conversations, and provide insightful information. LLMs, such as OpenAI's GPT-3, have demonstrated their proficiency in various tasks, from writing articles to composing code snippets, revolutionizing the way we interact with language-based technologies.

In this article, we will explore the fascinating world of generative AI, delving into its underlying principles, applications, and the future possibilities it holds. By understanding the concepts and potential of generative AI, we can gain insights into how this rapidly advancing field is reshaping technology and opening new doors to creativity and innovation.

Deep learning models can be broadly categorized into two types: discriminative AI and generative AI.

Discriminative AI models are primarily used for classification or prediction tasks. They learn the relationship between the features of data points and the corresponding labels or outcomes. These models are trained on datasets that are labelled, meaning each data point is associated with a specific class or category. By analyzing the patterns and correlations in the labelled data, discriminative AI models can accurately classify or predict the labels of new, unseen data.
Generative AI models are typically trained on unlabeled or partially labelled datasets. Instead of learning the relationship between features and labels, they learn the patterns and structure within the data itself. These models can then generate new data samples that share similar characteristics to the training data.

How does it work?

  1. Generative AI (GenAI) is a type of artificial intelligence that creates new content based on what it has learned from existing content: Generative AI refers to a branch of artificial intelligence that focuses on the creation of systems capable of generating new content. These systems learn from existing content, such as text, images, or audio, and use that knowledge to generate new and original content. By analyzing patterns, structures, and relationships within the existing data, generative AI models can produce content that resembles the input data but is novel and unique.

  2. The process of learning from existing content is called training and results in the creation of a statistical model: Training in generative AI involves exposing the AI model to a large dataset of existing content. The model analyzes this dataset and learns the statistical patterns and correlations present within it. It captures the underlying distribution of the data, allowing it to understand the characteristics of the content it has been trained on. This training process involves adjusting the parameters and weights of the AI model to minimize the difference between the generated output and the original content.

  3. When given a prompt, GenAI uses this statistical model to predict what an expected response might be and generates new content: Once the generative AI model has been trained on existing content and has learned the statistical patterns, it can generate new content in response to a given prompt or input. By leveraging the statistical model it has developed, the generative AI system predicts what the most likely or expected response would be based on the input. It generates new content that is coherent, relevant, and aligned with the patterns it has learned during the training phase. This ability to generate content in a contextually appropriate manner makes generative AI a powerful tool for tasks such as language generation, image synthesis, and music composition.

Generative AI learns from existing content through a training process, which involves creating a statistical model. When prompted, this model is used to predict and generate new content that aligns with the patterns and structures observed in the training data.

Generative AI harnesses the power of transformers, which are a key component of this technology. A transformer model consists of an encoder and a decoder. The encoder takes an input sequence and encodes it, while the decoder learns how to decode the representation for a specific task. This architecture allows the transformer to effectively generate new content based on the learned patterns.

However, transformers can sometimes produce hallucinations, which are words or phrases that are nonsensical or grammatically incorrect. These hallucinations can arise due to several challenges:

  1. Insufficient training data: If the model is not trained on a sufficient amount of diverse and representative data, it may struggle to generate coherent and meaningful content.

  2. Noisy or dirty training data: If the training data contains errors, inconsistencies, or irrelevant information, it can negatively impact the quality of the generated output and lead to hallucinations.

  3. Lack of context: When the model is not provided with enough context or information about the desired output, it may generate content that is unrelated or nonsensical.

  4. Insufficient constraints: If the model is not given enough constraints or guidelines during the generation process, it may produce output that deviates from the desired output, resulting in hallucinations.

Hallucinations pose a challenge for transformers as they can make the generated text difficult to understand and may introduce incorrect or misleading information.

To mitigate hallucinations and improve the output quality, prompt design plays a crucial role. Prompt design involves crafting a well-constructed input or prompt that guides the large language model (LLM) to generate the desired output. The quality of the input directly impacts the quality of the generated output. By carefully designing the prompt, considering the desired output, and providing relevant context, users can influence and shape the output generated by LLMs.

The power of generative AI lies in transformers, which consist of an encoder and decoder. However, transformers can generate hallucinations, which are nonsensical or grammatically incorrect content. This can be attributed to various factors, including training data quality, contextual information, and constraints. Prompt design plays a crucial role in mitigating hallucinations and ensuring the desired output is generated by the LLM.

Let's discuss different types of models commonly used in generative AI:

  1. Text-to-text models:

    • Text-to-text models take natural language input and generate text output. These models are trained to understand the relationship between pairs of texts and learn to map one text to another.
  2. Text-to-image models:

    • Text-to-image models are a newer development in generative AI. They are trained on a large dataset of images, each paired with a short text description. The models learn to generate images based on textual input, effectively translating text into corresponding visual representations. Techniques like diffusion are employed to achieve this mapping.
  3. Text-to-video/Text-to-3D models:

    • Text-to-video models aim to generate video representations based on textual input. The input text can range from a single sentence to a complete script, and the model generates a video that corresponds to the given text.

    • Similarly, Text-to-3D models generate three-dimensional objects based on a user's text description. These models are utilized in applications such as game development or creating 3D worlds.

  4. Text-to-task models:

    • Text-to-task models are designed to perform specific actions or tasks based on textual input. These models are trained to understand and execute a wide range of tasks, such as answering questions, conducting searches, making predictions, or taking appropriate actions.

Generative AI encompasses various model types. Text-to-text models generate text output, text-to-image models create images based on text input, text-to-video/Text-to-3D models produce videos or 3D objects corresponding to textual descriptions, and text-to-task models are trained to perform specific actions or tasks based on textual input. Each model type serves a distinct purpose and showcases the versatility of generative AI in generating diverse forms of content.