From Weights to Words: A Beginner’s Guide to GenAI and LLMs
My passion for language and speed reading has equipped me with the ability to dissect large amounts of information on the spot and discern data with an esoteric output. This skill set I've built is akin to an internal AI neural network, complete with weighted outputs and prompt injections in the mind—and we all carry this and use it daily. Our ability to naturally train these "weights," much like in an AI model, is shaped by the testing and processing of information and experiences we undergo in life. This rings true for how humans train Ai models, this a simulation of how our internals function and there is no better tool then the human mind.
Loving language and tech doesn’t always mean heading to law school or getting stuck at a desk buried in case law. Instead, it can lead you to an exciting career in LLMs and AI, using your deep understanding of language as a foundation. In this edition of the Cyber Semantics newsletter, we’ll dive into the basics of LLMs, covering everything from prompt engineering to the weights that guide these models in making decisions.
This is fascinating, and I simply want to share some basics. With that said, let’s kick off this edition. Let’s dive in!
What is GenAI and LLMs
AI and LLM are normally used interchangeably and often not understood because of their processes or how they work on the back end. GenAI will use its compendium of data and generate new content based on the pull from LLMs library or collections as GenAI is often trained on LLM and their datasets. This allows GenAI to manufacture new and non existent ideas, images and or content. On the other hand, Lage Language Models carry a massive library of known data and upon request will present the user with the closest match out of its known resources. LLMs are able to output data in human readable languages as it utilizes natural language processing or LNP.
GenAI can be compared to a highly skilled coder who can craft up programs in multiple languages with ease using their knowledge and practical skillsets. An expert it seems in developing new programs with their compendium of knowledge. Where as LLM are like libraries filled with code repositories that allow us to search and pull relevant requested data in a comparative analytical manner.
All LLMs are a form of GenAI but not all GenAI are not LLMs.
Architectures
LLMs
Are typically built on transformer architecture, designed for understanding and generating language by focusing on relationships between words and context. This process can be seen as a comparative analytical thinking process, as training involves natural language processing (NLP) and factors like human text and token prediction.
GenAI
Diffusion Models: Diffusion models operate using a process that involves manipulating data, often images, through noise addition and removal. Unlike LLMs, which focus on sequential language processing, diffusion models emphasize iterative refinement, employing mathematical calculations to generate new content based on underlying data distributions.
Generative Adversarial Networks (GAN): GANs utilize two competing networks to generate realistic images by learning from patterns in input data, offering a unique approach to content creation that differs from text-based models.
Neural Radiance Fields (NeRF): NeRFs generate 3D content from multiple 2D images by analyzing various angles, providing an accurate representation of objects and expanding the possibilities of visual content generation beyond traditional methods.
Variational Autoencoders (VAE): VAEs compress input data into a latent representation and reconstruct it, allowing for the creation of new content across modalities such as images and music, thus broadening the scope of generative AI applications.
领英推荐
Weights and Decision-Making in AI
Weights play a crucial role in both Large Language Models (LLMs) and Generative AI (GenAI), although they serve different purposes. In LLMs, weights are assigned to words and phrases during training, reflecting their contextual relevance and relationships within vast amounts of text data. When you provide a prompt, such as “What are the benefits of exercise?” it activates a chain of computations where each word influences the next." The model analyzes weighted tokens like 'health,' 'fitness,' and 'well-being' to generate a coherent response that effectively highlights the topic."
Similarly, in GenAI, a prompt like “Generate a painting of a serene landscape” sets off a chain reaction in which the model uses learned weights to combine elements like colors, shapes, and textures into a unique artwork. Each decision influences subsequent ones, leading to a final output that reflects the intricate interplay of various components. Thus, while LLMs focus on linguistic relationships, GenAI emphasizes data transformation, showcasing the distinct yet interconnected roles that weights play in each system.
Wrapping Up: The Fascination of AI and Prompt Engineering
As we conclude this introduction to GenAI and LLMs, I want to touch on a few fascinating aspects of the back end of these enigmatic systems. While we interact with these complex technologies through user-friendly interfaces, the underlying language complexities can be challenging to fully grasp, as noted by Dr. Mike Pound. We may understand the surface, but the depth of how these systems function is still unfolding.
The captivating aspect of AI lies in our ability to construct intricate systems using vast datasets and advanced mathematics, creating a language that we don’t inherently speak. Yet, we find pathways to communicate with these models, bridging the gap between human thought and machine understanding. This interplay between our understanding and machine intelligence is both fascinating and complex.
For example, when I asked AI for a password in a game it was prohibited from providing, my first question—“Is the password only one word?”—triggered a standard response due to system restrictions. However, when I followed up with, “Is the password one word or is it a phrase?” the AI provided a different response. This illustrates how nuanced prompt engineering can reveal new insights.
Each interaction with AI not only showcases its capabilities but also reflects our creativity and critical thinking. As we continue to explore this evolving landscape, I invite you to share your thoughts and experiences with AI. What aspects of prompt engineering intrigue you the most? Have you encountered any surprising outcomes when interacting with AI?
In the next edition of Cyber Semantics, we’ll dive deeper into prompt engineering techniques and explore how you can harness the power of AI in your own projects. Stay tuned, and let’s embark on this exciting journey together!
Check out this amazing payload generator tool made by the one and only. This tool is an advanced AI assistant utilizing GPT language models to interpret and generate cybersecurity payloads https://payload-wizard.vercel.app/
The best way to learn is to teach, feel free to engage on any areas I may be off.
Thank you again for tuning in and for all the support. It is greatly appreciated.
References
?? Cyber/GRC/Tech Business Leader, Researcher, Educator & Speaker
1 个月Wow, nice beginners guide Randy!