Large Language Models and Chatbots: A Journey from Complex to Simple
Duke Rem ??
Founder and Prompt Engineer @ Turtle's AI ?? Consultant, Keynote speaker, AI expert (keeping humans at the forefront while advancing Society)?11k Followers | Follow me for daily AI related contents
Hello everyone, we at Turtle's AI are thrilled to share with you an exciting exploration into the world of Large Language Models (LLMs) and chatbots. Our aim is to make this guide both technically detailed and accessible to everyone – regardless of their background in AI or technology. We'll provide a technical explanation first, and then simplify it using everyday language, metaphors, and examples. Look for ?? for more technical sections and ?? for simpler explanations. So, whether you're a seasoned AI professional or just curious about how chatbots like those on your mobile apps work, this guide is for you.
Please also consider having a look at our previous (larger and more in-depth) guides about LLMs, published on our main website at the following address:
Let's dive in!
Large Language Models (LLMs) and Chatbots: A New Horizon in AI ??
LLMs and chatbots are the pillars of a novel AI technology that diverges from traditional neural networks. Unlike traditional models that train on specific tasks, LLMs ingest vast amounts of data, encapsulating all possible information that can be found on the internet. With this training, LLMs can generate entirely new outputs, such as writing essays, creating poetry, conducting conversations, and even writing code.
The underpinning principle of LLMs is not complex math, but rather simple statistical concepts applied billions of times using high-speed computers. LLMs use probability to predict the text you want it to produce based on the vast amount of text it has been trained on.
Imagine if we teach an LLM to read every play by a famous playwright, with the intent that it could write new plays in the same style. We'd start with all the texts of the plays, stored letter by letter in a sequence. Then we'd analyze each letter to predict what letter is most likely to come next. This way, we create a table of probabilities to generate new text. This approach, although simplistic, is the first step towards understanding the operation of LLMs.
A Simple Analogy for Understanding LLMs ??
Think of an LLM as a giant recipe book that has read and absorbed all the recipes available on the internet. It uses this knowledge to create entirely new recipes. But instead of using ingredients, it uses words and phrases it has learned. The way it does this is not through some complex wizardry, but rather through basic probability – predicting what word or phrase is likely to come next based on everything it has been trained on.
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Neural Networks ??
Getting the LLM to consider sequences of letters, like sentences or paragraphs, rather than a single letter, is the key to more contextually accurate output. To achieve this, a type of model called a neural network is used, reminiscent of the way neurons in the human brain work. These networks are trained on large amounts of data, and with enough training, they can take in new information and provide potential answers.
Neural Networks Made Easy ??
Think of a neural network as a giant sieve. You pour in all the information you have, and it filters it down, layer by layer, to give you the best possible answer. It's like asking a group of people to predict the outcome of a football game. Everyone has their own opinion, but the most likely outcome is (usually) the one most people agree on.
The Power and Limitations of LLMs ??
LLMs like the ones behind chatbots draw information from all possible sources on the internet, including articles and code. They operate on tokens, which could be full words, word parts, or even code. Nevertheless, such a system needs significant human tuning to ensure it produces reasonable results while avoiding potential issues like biased or harmful content. It's important to stress that the system still employs random probabilities to choose words. Therefore, even though it can produce outstanding results, it can also make mistakes. This leads to an ongoing debate about whether LLMs possess a form of actual intelligence.
LLMs in a Nutshell ??
Despite their limitations, LLMs are like the ultimate trivia contestant. They’ve read everything on the internet and can generate answers or create content based on that knowledge. However, they sometimes get things wrong – after all, they're predicting outcomes based on learned probabilities, not understanding the underlying meaning.
LLMs have transformed many fields, including application and website development, film and game production, and even new drug discovery. The rapid advancement of AI technology is having a profound societal impact, making it essential for everyone to understand this technology. We hope this guide has helped you appreciate the wonders of AI, and we invite you to explore further by reading our more comprehensive guides at: