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In this part of the course, we delve into the intricacies of Large Language Models (LLMs). We start off by exploring the historical context and fundamental concepts of artificial intelligence (AI), machine learning (ML), neural networks (NNs), and generative AI (GenAI). We then examine the core attributes of LLMs, focusing on their scale, extensive training on diverse datasets, and the role of model parameters. Then we go over the types of challenges associated with using LLMs.
In the next section, we explore practical applications of LLMs across various domains, emphasizing their versatility in areas like content generation, language translation, text summarization, question answering etc. The section concludes with an analysis of the challenges encountered in deploying LLMs, covering essential aspects such as scalability, latency, monitoring etc.
In summary, this part of the course provides a practical and informative exploration of Large Language Models, offering insights into their evolution, functionality, applications, challenges, and real-world impact.
Image Source: [<https://medium.com/womenintechnology/ai-c3412c5aa0ac>](<https://medium.com/womenintechnology/ai-c3412c5aa0ac>)
The terms mentioned in the image above have likely come up in conversations about ChatGPT. The visual representation offers a broad overview of how they fit into a hierarchy. AI is a comprehensive domain, where LLMs constitute a specific subdomain, and ChatGPT exemplifies an LLM in this context.
In summary, Artificial Intelligence (AI) is a branch of computer science that involves creating machines with human-like thinking and behavior. Machine Learning(ML), a subfield of AI, allows computers to learn patterns from data and make predictions without explicit programming. Neural Networks (NNs), a subset of ML, mimic the human brain's structure and are crucial in deep learning algorithms. Deep Learning (DL), a subset of NN, is effective for complex problem-solving, as seen in image recognition and language translation technologies. Generative AI (GenAI), a subset of DL, can create diverse content based on learned patterns. Large Language Models (LLMs), a form of GenAI, specialize in generating human-like text by learning from extensive textual data.
Generative AI and Large Language Models (LLMs) have revolutionized the field of artificial intelligence, allowing machines to create diverse content such as text, images, music, audio, and videos. Unlike discriminative models that classify, generative AI models generate new content by learning patterns and relationships from human-created datasets.
At the core of generative AI are foundation models which essentially refer to large AI models capable of multi-tasking, performing tasks like summarization, Q&A, and classification out-of-the-box. These models, like the popular one that everyone’s heard of-ChatGPT, can adapt to specific use cases with minimal training and generate content with minimal example data.
The training of generative AI often involves supervised learning, where the model is provided with human-created content and corresponding labels. By learning from this data, the model becomes proficient in generating content similar to the training set.
Generative AI is not a new concept. One notable example of early generative AI is the Markov chain, a statistical model introduced by Russian mathematician Andrey Markov in 1906. Markov models were initially used for tasks like next-word prediction, but their simplicity limited their ability to generate plausible text.
The landscape has significantly changed over the years with the advent of more powerful architectures and larger datasets. In 2014, generative adversarial networks (GANs) emerged, using two models working together—one generating output and the other discriminating real data from the generated output. This approach, exemplified by models like StyleGAN, significantly improved the realism of generated content.
A year later, diffusion models were introduced, refining their output iteratively to generate new data samples resembling the training dataset. This innovation, as seen in Stable Diffusion, contributed to the creation of realistic-looking images.
In 2017, Google introduced the transformer architecture, a breakthrough in natural language processing. Transformers encode each word as a token, generating an attention map that captures relationships between tokens. This attention to context enhances the model's ability to generate coherent text, exemplified by large language models like ChatGPT.
The generative AI boom owes its momentum not only to larger datasets but also to diverse research advances. These approaches, including GANs, diffusion models, and transformers, showcase the breadth of methods contributing to the exciting field of generative AI.