Generative AI and Its Implications for Data and Analytics
Generative artificial intelligence has captured the imaginations of people around the world. Though not entirely new, generative AI is evolving so rapidly that technology professionals need to track its maturation closely to effectively evaluate the associated opportunities and risks. Here are the basics you need to know.
What Is Generative AI?
Put simply, generative AI applications automate the generation of data-driven outputs. The technology uses machine learning models to find statistical patterns within training data and then generate new outputs — such as documents, images, and videos — that are consistent with these patterns.
Popular generative AI use cases include intelligent chatbots, personalized marketing messages, and drug discovery. One of the most noteworthy implementations of generative AI is ChatGPT, an intelligent chatbot released in November 2022 by OpenAI. The widely adopted ChatGPT, powered by a generative AI technology known as a large language model (LLM), can summarize documents, write marketing copy, roll up search results, and support other complex content creation tasks.
How Do Large Language Models Power Generative AI?
An LLM is a vast neural network that operates a bit like “autocorrect on steroids.” It has been trained to predict the next word in a sentence, responding to a query-like “prompt” by considering all the surrounding words within the text database upon which it’s been trained. Through an iterative training process, the LLM learns the probabilities of word sequences appearing together in natural language that may be appropriate in a generated response to that prompt.
Another popular generative AI use case is automated generation of program code. For data and analytics professionals, some LLM-driven generative AI applications support generation of Python code. Some also generate synthetic data for machine learning use cases where authentic, from-the-source data is unavailable, sparse, biased, privacy protected, or too costly to acquire. Generative AI tools, such as LLMs, can also analyze and visualize data, making them a worthy addition to many data professionals’ self-service, no-code business analytics toolkits.
Yet another sophisticated generative AI application is text-to-image generation. Applications such as OpenAI’s DALL-E 2 leverage LLMs to generate realistic images and art in response to text-based prompts. In this use case, the pre-training database generally includes images as well as the text-based captions with which they’re associated.
How Do Foundation Models Enhance the Agility of Generative AI?
Many observers consider LLMs and other generative AI technologies a key stepping stone to what’s often called “artificial general intelligence.” This is because many generative AI applications are built on foundation models. These are machine learning algorithms that have been pre-trained on huge unlabeled data sets and, though they have been trained to address specific core tasks, are easily adaptable to adjacent tasks through additional steps known as “fine-tuning.” For example, ChatGPT-4’s core LLM — the GPT-4 foundation model — is built from a highly adaptable machine learning approach known as transformer-based architecture.
Foundation models are proliferating for a wide range of use cases, with both open-source and closed-source offerings available to anyone who wants to develop new generative applications. For instance, Hugging Face, Meta, and IBM all provide libraries of pre-trained, open-source foundation models for different tasks. A wide range of proprietary foundation models come from OpenAI, Cohere, AI21Labs, Anthropic, DeepMind, Google AI, and others.
Training a foundation model begins with an established data science technique called unsupervised learning, where a large corpus of data is fed into the model, which then algorithmically discovers “parameters” associated with facets of the task to be learned. Regardless of their source or the specific use cases for which they’re suited, foundation models can be adapted for specific enterprise requirements by fine-tuning them through a variety of techniques, such as tweaking a pre-trained model with new weights, training it with labeled task-specific data, and supervising its output with reinforcement learning and human feedback. Through these techniques, a foundation model can be adapted to new tasks similar to its original function, such as when an LLM is adapted to translate English text into many other natural languages.
