Unveiling the Magic of Generative AI: A Journey of Learning and Discovery

Unveiling the Magic of Generative A.I.: A Journey of Learning and Discovery

The world of Artificial Intelligence is captivating, and as it continues to evolve, I'm on a personal mission to share my learning experiences with you. Through this blog, I hope to solidify my understanding of Generative A.I. while empowering others to grasp this revolutionary technology.

The Power of Creation:

Generative A.I., like a skilled artist, can create entirely new content – images, text, and answers – based on the knowledge it has absorbed. This ability is fueled by vast training data, allowing it to understand patterns and replicate them in novel outputs.

Beyond Imitation: The Spark of Human-like Logic:

While Generative A.I. excels at mimicking existing data, Artificial Generative Intelligence (AGI) aspires to push the boundaries further. This next-generation technology aims to incorporate a more human-like approach, incorporating logical reasoning to enhance its capabilities.

The Building Blocks of Generative A.I.:

Let's delve into the key components that power this remarkable technology:

• Data Sources: Imagine the training data as the paint and brushes of an artist. This information comes from various sources like S3, SQL databases,  Data Lakes, and Lake Houses; most can store structured and unstructured data.
• Data Cleaning: Before feeding the data to the A.I., it's vital to ensure it's accurate and unbiased. Tools like Trifecta and Glue help refine the data, making it easier for the A.I. to understand and utilize effectively and de-sensitize the data. Make Data Great Again for LLM.
• Vector Databases: These specialized databases store information in a multidimensional format, allowing efficient retrieval based on similarities. Think of it as organizing your paintbrushes by colour for faster access while creating a masterpiece. MetaData stored in Embeddings. These vectors can be hashed for authorized customers so no other can access the data. These embeddings are sent to LLMs to produce the content (text, image,etc)
• Large Language Models (LLMs): These are the workhorses of Generative A.I., capable of learning and adapting rapidly. They come in various forms, including general-purpose, task-specific, and domain-specific models, each suited for different purposes. Their capabilities are measured by metrics like MMLU and the number of parameters they can process. 1-bit LLMs are a fad now.
• Retrieval-Augmented Generation (RAG): This technique helps ground the A.I.'s outputs in reality. Imagine telling your artist friend to paint a car but specifying it as the latest model navigating a new road system. RAG incorporates such additional context to enhance the accuracy and relevance of the output. RAG helps send only the data as needed to LLM and then clean up the data to prevent LLM Data leaks.
• Fine-Tuning: Similar to an artist refining their technique, Generative A.I. models can be further specialized by exposing them to targeted training data relevant to specific tasks.
• Hallucinations: Just like artists can make mistakes, A.I. models can sometimes produce inaccurate or nonsensical outputs. This can happen due to inadequate training, noisy data, unclear prompts, and a lack of constraints.
• Natural Language Processing (NLP): This field bridges the gap between human language and A.I., allowing us to interact with the models using natural language prompts and questions. Hugging Face Transformers pipleline is a popular platform for accessing pre-trained NLP models.
• Prompt Engineering: Just like giving clear instructions to an artist, crafting effective prompts is crucial for guiding the A.I. towards desired outcomes. This involves providing a transparent thought process and clear instructions to ensure the A.I. generates the intended images, text, or answers.
• TPUs/GPUs - To accelerate LLMs' massive data processing, Tensor processing Units can speed up the training process by optimizing for neural network computations/matrix multiplications. Measured by tokens per second

The Applications of Generative A.I.:

The potential of this technology is vast, with applications spanning:

• Image and video generation: Imagine creating visual content tailored to specific needs.
• Question-answering systems: Get reliable answers to your queries, powered by A.I.'s knowledge.
• Chatbots/Agents: Interact with intelligent bots for information.
• Chain of thought reasoning: Gain insight into the A.I.'s thought process behind its outputs, fostering trust and understanding.
• Summarization and Paraphrasing: Entire customer calls, meetings or Terms and Conditions docs could be efficiently summarized so that others can more easily digest the meeting minutes. LLMs can take large amounts of text and boil it down to the most important bytes.
• Translation between Languages - Context Based (disk in medicine and technology could mean different things), Sentiment Based ( politeness when responding to an irate customer) and relation based ( sending a note to wife/customer/collegue )

This is just a glimpse into the fascinating world of Generative A.I. Stay tuned for future blog posts where we'll delve deeper into each component and explore the exciting possibilities they present.

Thank you for joining me on this exploration of A.I.!

P.S.: Rewording Powered by Gemini(google model)