What are Generative AI models?

Summary

In this video, Kate Soule, a senior manager of business strategy at IBM Research, explains the emergence and potential of large language models (LLMs) and foundation models in AI. She discusses how these models, trained on vast amounts of data, can perform a wide range of tasks with high performance and productivity gains. Despite their advantages, challenges like high compute costs and trustworthiness issues persist. IBM is innovating to improve these models' efficiency and reliability across various domains, including language, vision, code, chemistry, and climate change. Soule encourages viewers to explore IBM's efforts to enhance AI technologies.

Takeaways

• 🌐 Large language models (LLMs) like chatGPT are revolutionizing AI capabilities, demonstrating significant advancements in performance and enterprise value.
• 📚 LLMs are part of a broader category known as foundation models, which represent a paradigm shift in AI from task-specific models to more versatile foundational capabilities.
• 🔍 The term 'foundation models' was introduced by Stanford researchers to describe AI models that can be applied to a wide range of tasks beyond their initial training.
• 📈 Foundation models are trained on vast amounts of unstructured data, enabling them to predict and generate text based on previous inputs, a feature that falls under the umbrella of generative AI.
• 🔧 These models can be fine-tuned for specific tasks by introducing a small amount of labeled data, a process known as tuning, which enhances their versatility.
• 🔎 Foundation models can also be used in low-labeled data scenarios through prompting or prompt engineering, where they infer tasks from given cues or questions.
• 🏆 The primary advantage of foundation models is their superior performance, stemming from their extensive training on terabytes of data, which allows them to outperform models trained on limited data.
• 🚀 Foundation models offer significant productivity gains by reducing the need for extensive labeled data, leveraging their pre-training on unlabeled data for various tasks.
• 💰 A major disadvantage is the high computational cost associated with training and running these models, which can be prohibitive for smaller enterprises.
• 🤔 Trustworthiness is another concern, as these models are often trained on unvetted internet data, potentially leading to biases and inclusion of inappropriate content.
• 🌳 IBM is actively innovating to improve the efficiency and trustworthiness of foundation models, aiming to make them more viable for business applications.
• 🖼️ Foundation models are not limited to language; they are also being developed for vision (e.g., DALL-E 2), coding (e.g., Copilot), and other domains like chemistry and climate science.

Q & A

What is the significance of large language models (LLMs) in the context of AI advancements?

Large language models (LLMs) have revolutionized AI by demonstrating a significant leap in performance across various tasks such as writing poetry and planning vacations. They represent a shift in AI capabilities, potentially driving substantial enterprise value.

What are foundation models and how do they differ from traditional AI models?

Foundation models are a new class of AI models that serve as a foundational capability for multiple applications. Unlike traditional AI models that are trained for specific tasks with task-specific data, foundation models are trained on a vast amount of unstructured data, enabling them to be transferred to various tasks.

How did the term 'foundation models' originate?

The term 'foundation models' was first coined by a team from Stanford University. They observed a paradigm shift in AI where the field was converging towards models that could serve as a base for various applications.

What is the process of training a foundation model?

Foundation models are trained in an unsupervised manner on unstructured data. For instance, in the language domain, they are fed with terabytes of data, learning to predict the next word in a sentence based on the preceding words.

Why are foundation models considered part of generative AI?

Foundation models are part of generative AI because they have the capability to generate something new, such as the next word in a sentence, based on the words they have seen before.

How can foundation models be tuned to perform specific NLP tasks?

Foundation models can be tuned by introducing a small amount of labeled data. This process adjusts the model's parameters, allowing it to perform specific natural language tasks like classification or named-entity recognition.

What is the concept of prompting or prompt engineering in the context of foundation models?

Prompting or prompt engineering is a method where foundation models are applied to tasks by providing them with a prompt or a question. The model then generates a response based on the prompt, which can be used for tasks like sentiment analysis.

What are the advantages of using foundation models in business settings?

The advantages of foundation models include superior performance due to extensive data exposure and significant productivity gains. They require less labeled data for task-specific training compared to models trained from scratch.

What are some of the disadvantages associated with foundation models?

Disadvantages of foundation models include high compute costs due to their extensive training and inference requirements, and issues with trustworthiness, as they are trained on unstructured data that may contain biases or toxic information.

How is IBM contributing to the development and improvement of foundation models?

IBM is working on innovations to improve the efficiency and trustworthiness of foundation models, making them more relevant for business applications. They are also exploring the application of foundation models in various domains beyond language, such as vision, code, chemistry, and climate change.

What are some examples of foundation models being applied in different domains?

Examples include DALL-E 2 for vision, generating custom images from text data, Copilot for code completion, and IBM's molformer for molecule discovery. IBM is also innovating in areas like Earth Science Foundation models for climate research.