Here's what you should know about Large Language Models

OpenAI's ChatGPT and other Large Language Models are taking the world by storm and, to me, they mark the beginning of the era of AI. Human civilization will never be the same again.

What's different about AI this time?

A large language model is an AI model that can process and understand human language. It uses neural networks to analyze data to learn patterns and relationships in natural language. LLMs emerged around 2018. As opposed to the prior generation of AI, which focused on the natural language processing (NLP) and supervised training models for specific tasks, LLMs can perform well at a wide variety of tasks. OpenAI's GPT-3, a Generative AI tool, is capable of generating text, translation, summarization, question answering, and more. LLMs can also perform tasks that require reasoning and inference, such as answering complex questions and even solving puzzles.

Wait, LLMs can solve puzzles?

Solving puzzles like the wolf, goat and cabbage problem is a piece of cake:

The way LLMs generate a solution to this is by applying logical reasoning and breaking down the problem into smaller, more manageable parts. They identify the constraints and limitations of the problem, such as the fact that the wolf cannot be left alone with the goat and the goat cannot be left alone with the cabbage. From there, an LLM considers different scenarios and possible solutions based on the available information. By reasoning through each step, it is able to determine the sequence of moves that would allow the man to transport all three items safely across the river.

What's so large anyway about large language models?

Large Language Models are trained on a massive corpus of text data such as books, articles, web pages, and they learn to predict the probability of a word or sequence of words based on its context. The larger the corpus of training data, the more sophisticated the model can become.

The largeness of models can also be described in terms of the number parameters they handle. Parameters are a machine learning term for the variables present in the model on which it was trained that can be used to infer new content. While there isn't a universally accepted figure for how large the data set for training needs to be, an LLM typically has at least one billion parameters. GPT-2, was made up of 1.5 billion parameters, while GPT-3 is claimed to be made up of 175 billion parameters.?It's believed that GPT-4 includes a trillion parameters, with some speculating as high as 1.7 trillion.?Google's PaLM2 will probably have a comparable figure.

What can enterprises use large language models for?

The possibilities are literally limitless, but here are just a few examples of how enterprises can use large language models:

Customer service: Large language models can power chatbots and virtual assistants to provide personalized customer service around-the-clock. These AI-powered agents can answer frequently asked questions, resolve simple issues, and escalate complex problems to human agents. (Plug: This is exactly what SingleStore has done with its SQrL bot!)

Content creation: Large language models can be used to generate high-quality content, including product descriptions, marketing copy, and social media posts. This can save time and resources while ensuring that the content is engaging and relevant to the target audience.

Sentiment analysis: Large language models can analyze text data from customer reviews, social media, and other sources to identify sentiment and extract insights. This can help enterprises understand customer needs, preferences, and pain points, and adjust their products and services accordingly.

Translation: Large language models can be used to translate text between languages, helping enterprises expand their reach and tap into new markets.

Fraud detection: Large language models can analyze text data to detect fraud and other types of malicious activity. By identifying patterns and anomalies in text data, enterprises can prevent financial losses and protect their reputation.

Hope you found this post interesting. In my next post, I will dive deeper into the tech landscape of Large Language Models.