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A Quick and Relatively Painless Explanation of Large Language Models
A Quick and Relatively Painless Explanation of Large Language Models
Today I'd like to introduce the topic of Large Language Models through a quick online presentation and course i'm putting together: "A Quick and Relatively Painless Explanation of Large Language Models".
This will be the initial offering in a series of discussions on Data Science, Artificial Intelligence, and Machine Learning, with an intended audience of Humans. Humans who are fairly "with it" and tech savvy but need a bit deeper understanding of these often over-hyped and heavily hyperboled "magical black boxes" of AI oracular wisdom. Maybe some of us didn't take so much math and calculus and advanced game theory and programming and computer science at University but still need a pretty solid understanding of modern tech that is everywhere. All around us.
No matter what field you work in today, if you touch a keyboard or talk to a screen, you are impacted by, and likely already working with, AI, ML, and Data Science models and applications. Perhaps without even realizing it. Or maybe you do realize it but just can't make time to dig into deep understanding of, say, Transformer Architecture, because you bloody well have more important things to focus on as an IT Project Manager with HR focus at work but need to understand AI because your office is going ape-poopies over it and demanding everyone embrace! Now!
So... take a breath.... I get excited sometimes. Join me for this initial dive into the world of Data Science, AI, and ML for Humans. The magic block box for the rest of us. This is not only the first blog in this series, but part of an entire new, very exciting learning and discussion platform idea I'll be sharing as we journey down the path of Data Science, AI, and ML enlightenment together.
So let's take a look at some basic concepts and a brief history of LLMs since around 2017. The year they were born crazy enough! The hype seems like it's been around forever, but remember people, the initial paper introducing the whole Transformer concept for LLMs was only 2017. My how time flies! How the hyperbole machine does spin!
Here you go, let's journey together shall we? Gather round, manifest abundance, practice gratitude, and bask and glow in the wonderful peachy light of learnin'
click link below to view in Google Slides in a new window:
A Quick and Relatively Painless Explanation of LLMs
View PDF version at:
https://drive.google.com/file/d/1ITPYX_Lp2EXyw2DqJMZYlmnJNEYpW_Mg/view?usp=sharing
EARLY EVOLUTION OF NATURAL LANGUAGE PROCESSING
Natural language processing (NLP) is a branch of artificial intelligence that enables computers to comprehend and utilize human languages. The concept of language as a scientific discipline traces back to Swiss linguist Ferdinand de Saussure, who, in the early 1900s, proposed that language consists of systems where sounds represent shifting concepts based on context. Saussure's ideas laid the groundwork for structuralism in linguistics and influenced later developments in computer languages.
In 1950, Alan Turing introduced the notion of a "thinking" machine, suggesting that if a machine could engage in conversation indistinguishably from a human, it could be considered intelligent. This idea, alongside advancements in neuroscience, helped catalyze the fields of AI and NLP.
NLP encompasses various tasks such as:
Content Categorization: Summarizing documents and detecting duplicates.
Topic Discovery: Identifying themes within text collections.
Contextual Extraction: Extracting structured data from unstructured text.
Sentiment Analysis: Gauging opinions from large text datasets.
Text-to-Speech and Speech-to-Text Conversion: Converting spoken language into text and vice versa.
Document Summarization: Creating concise summaries of extensive texts.
Machine Translation: Translating text between languages automatically.
The evolution of NLP faced challenges, particularly after Noam Chomsky's 1957 publication of Syntactic Structures, which emphasized the need for computers to understand sentence structure. Following initial enthusiasm, funding for NLP research was halted in 1966 due to underwhelming results.
However, by the 1980s, renewed interest emerged with advancements in machine learning and statistical models, leading to significant improvements in NLP capabilities.
The late 1990s saw a surge in statistical methods for analyzing language, culminating in the introduction of recurrent neural networks (RNNs) for voice and text processing. In 2001, the first neural language model was proposed, paving the way for modern NLP applications.
By 2011, Apple's Siri exemplified successful NLP integration, utilizing automated speech recognition to interpret user commands effectively. Machine learning techniques enable these systems to adapt and improve over time by understanding variations in user speech patterns.
NLP has evolved from theoretical linguistics to practical applications that enhance human-computer interaction through advanced understanding and processing of natural language.
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