What are Generative AI models?

Kate Soule, a senior manager of business strategy at IBM Research, provides an insightful overview of generative AI and foundation models, which are large language models (LLMs) capable of performing a wide range of tasks after being trained on vast amounts of unstructured data. These models, such as chatGPT, have demonstrated significant advancements in AI capabilities, from creative writing to enterprise solutions. Foundation models are part of a new paradigm in AI, where a single model can be adapted for multiple applications through tuning with a small amount of labeled data or prompting. While offering advantages in performance and productivity, these models also present challenges in terms of high computational costs and trustworthiness due to their training on potentially biased or toxic internet data. IBM is actively innovating to improve the efficiency and reliability of these models for business applications, with applications extending beyond language to vision, code, chemistry, and climate research.

Takeaways

• 🌐 Large language models (LLMs) like chatGPT are revolutionizing AI performance and enterprise value through diverse applications.
• 🔍 LLMs are part of a new class called 'foundation models', which represent a paradigm shift in AI development.
• 📚 Foundation models are trained on vast amounts of unstructured data, which allows them to perform a wide range of tasks.
• 🔮 These models have a 'generative' capability, predicting and generating the next word in a sentence based on previous context.
• 🛠️ Foundation models can be 'tuned' with a small amount of labeled data to perform specific NLP tasks like classification or named-entity recognition.
• 📉 Even with limited data, 'prompting' or 'prompt engineering' allows these models to be applied to various tasks effectively.
• 🚀 The main advantage of foundation models is their superior performance due to extensive data exposure.
• ⚙️ They offer significant productivity gains by reducing the need for labeled data, leveraging pre-trained generative capabilities.
• 💰 A notable disadvantage is the high compute cost associated with training and running these large, parameter-rich models.
• 🤔 Trustworthiness is a concern as these models are trained on vast, uncurated data, which may include biases and inappropriate content.
• 🌟 IBM is actively innovating to improve the efficiency and trustworthiness of foundation models for business applications.
• 🎨 Foundation models are not limited to language; they're also applied in vision, coding, and other domains like chemistry and climate science.

Q & A

What is the term 'large language models' (LLMs) referring to in the context of AI?

Large language models (LLMs) refer to advanced AI models that are capable of understanding and generating human-like text based on vast amounts of data. They have been used for various tasks, such as writing poetry or assisting with travel planning, demonstrating a significant leap in AI performance and potential for enterprise value.

Who coined the term 'foundation models' and why?

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 using a foundational capability or model that could drive a wide range of use cases and applications, as opposed to training multiple task-specific AI models.

What is the key feature of generative AI models that allows them to perform multiple tasks?

The key feature of generative AI models is their ability to be trained on a massive amount of unstructured data in an unsupervised manner. This training enables the model to develop a generative capability, allowing it to predict and generate the next word in a sentence based on the context, which can then be transferred to perform various tasks.

How can foundation models be fine-tuned for specific natural language processing (NLP) tasks?

Foundation models can be fine-tuned for specific NLP tasks by introducing a small amount of labeled data into the model. This process updates the model's parameters, allowing it to perform tasks such as classification or named-entity recognition that are traditionally not associated with generative models.

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

Prompting or prompt engineering is a technique where a model is given a specific input or 'prompt' and then asked a question related to that input. The model generates a response based on the prompt, which can be used to perform tasks such as sentiment analysis without the need for labeled data.

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

The advantages of using foundation models in business settings include superior performance due to extensive data exposure and significant productivity gains, as they require less labeled data to achieve task-specific models compared to starting from scratch.

What are some of the disadvantages associated with the use of foundation models?

Disadvantages of foundation models include high compute costs due to their size and complexity, making them expensive to train and run. Additionally, there are trustworthiness concerns as these models are trained on vast amounts of unstructured data, which may contain biases, hate speech, or other toxic information.

How is IBM addressing the disadvantages of foundation models?

IBM Research is working on innovations to improve the efficiency and trustworthiness of foundation models. They aim to make these models more relevant for business settings by focusing on reducing computational costs and enhancing the reliability and accuracy of the models.

What are some other domains where foundation models are being applied?

Besides language models, foundation models are being applied in vision, as seen with models like DALL-E 2 for generating custom images from text, in code with products like Copilot for code completion, and in domains such as chemistry with models like molformer for molecule discovery and climate change with Earth Science Foundation models.

How does IBM's Watson Assistant utilize foundation models?

IBM's Watson Assistant is an example of a product that incorporates language models, likely leveraging the capabilities of foundation models to enhance its natural language understanding and interaction capabilities.

What is the significance of IBM's work on Earth Science Foundation models?

IBM's work on Earth Science Foundation models is significant as it aims to improve climate research by using geospatial data to create models that can better predict and understand climate change patterns and their impacts.

How can interested individuals learn more about IBM's efforts to improve foundation models?

Interested individuals can learn more about IBM's efforts to improve foundation models by visiting the links provided in the video transcript, which likely contain additional resources and information on the company's research and innovations in this area.

Outlines

00:00

🚀 Introduction to Large Language Models and Foundation Models

Kate Soule, a senior manager of business strategy at IBM Research, introduces the concept of large language models (LLMs) and their role in the new paradigm of AI. She explains that these models are part of a class known as foundation models, which represent a shift from task-specific AI models to a foundational capability that can drive a variety of applications. Foundation models are trained on vast amounts of unstructured data, enabling them to perform a wide range of tasks, including traditional NLP tasks when fine-tuned with a small amount of labeled data. The generative capability of these models allows them to predict and generate the next word in a sentence, which is a core function in their training. Prompt engineering is another method to apply these models to specific tasks even with low-labeled data. The advantages of these models include superior performance due to extensive data exposure and productivity gains through reduced need for labeled data. However, they also come with disadvantages such as high compute costs for training and running inference, and trustworthiness issues due to the vast and unvetted data sources from which they learn.

05:05

💡 Addressing the Challenges and Broad Applications of Foundation Models

The second paragraph discusses the challenges associated with foundation models, such as the high computational cost of training and running these models, which can be a barrier for smaller enterprises. There are also concerns about trustworthiness due to the models being trained on unstructured data from the internet, which may contain biases, hate speech, and other toxic information. IBM is actively working on innovations to improve the efficiency and trustworthiness of these models for better business relevance. The applications of foundation models extend beyond language, with examples given in the fields of vision, as seen with DALL-E 2, and coding with tools like Copilot. IBM is innovating across various domains, including language, vision, and Ansible code models, as well as exploring new areas like chemistry with the molformer model and climate change with Earth Science Foundation models. The video concludes by encouraging viewers to learn more about IBM's efforts to enhance foundation models, making them more efficient and trustworthy for business applications.

Keywords

Generative AI models

Generative AI models are a class of artificial intelligence systems that have the ability to create new content, such as text, images, or music, that is similar to the data they were trained on. In the context of the video, these models are used to generate new sentences or predict the next word in a given context, which is a key aspect of their application in language processing.

Large Language Models (LLMs)

Large Language Models, or LLMs, refer to advanced AI models that are designed to process and understand large volumes of language data. They are capable of performing a variety of language-related tasks, such as writing, translation, and sentiment analysis. The video discusses how LLMs like chatGPT have significantly improved AI performance and their potential for enterprise value.

Foundation Models

Foundation Models are a term used to describe a new paradigm in AI where a single model serves as a foundational capability to drive multiple applications and use cases. This concept was first introduced by a team from Stanford and is central to the video's discussion on how these models can be transferred and tuned for various tasks beyond their initial training.

Unsupervised Learning

Unsupervised Learning is a type of machine learning where the model is trained on data without any explicit guidance on the desired output. It is used to find patterns or structures in the data. In the video, it is mentioned that foundation models are trained on unstructured data in an unsupervised manner, which gives them the ability to transfer to multiple tasks.

Transfer Learning

Transfer Learning is a technique in machine learning where a model developed for one task is reused as the starting point for a model on a second task. The video explains that foundation models have the 'super power' to transfer to multiple different tasks due to their extensive training on unstructured data.

Tuning

Tuning in the context of AI refers to the process of adjusting a model's parameters to optimize its performance for a specific task. The video describes how foundation models can be tuned by introducing a small amount of labeled data, allowing them to perform traditional NLP tasks like classification or named-entity recognition.

Prompt Engineering

Prompt Engineering is a technique used with AI models where the model is given a prompt, or a specific input, to guide its output. In the video, it is used as an example of how a model can be applied to classification tasks by asking it to generate the sentiment of a given sentence.

Compute Cost

Compute Cost refers to the expenses associated with the computational resources required to train and run AI models. The video highlights that foundation models are expensive to train and run due to their large size and the vast amount of data they process, which can be a barrier for smaller enterprises.

Trustworthiness

Trustworthiness in AI models pertains to their reliability, accuracy, and the absence of biases. The video discusses the challenges of ensuring trustworthiness in foundation models, given that they are trained on vast amounts of unstructured data which may include biases or inappropriate content.

IBM Research

IBM Research is IBM's research and development division that works on various innovations in technology. In the video, it is mentioned that IBM Research is actively working on improving the efficiency and trustworthiness of foundation models to make them more applicable in business settings.

Domains of Application

Domains of Application refer to the various fields or areas where a technology can be effectively utilized. The video outlines different domains such as language, vision, code, chemistry, and climate change where foundation models are being applied, showcasing the broad potential of generative AI.

Highlights

Large language models (LLMs) are revolutionizing AI performance and enterprise value.

LLMs are part of a new class of models called foundation models.

Foundation models are predicted to drive all use cases and applications previously envisioned with conventional AI.

These models can be transferred to any number of tasks due to their training on unstructured data.

Foundation models have a generative capability, predicting and generating the next word based on previous words.

Tuning involves introducing labeled data to perform traditional NLP tasks.

Prompting or prompt engineering allows models to perform tasks with low-labeled data.

Foundation models outperform models trained on few data points due to their extensive data exposure.

Productivity gains are achieved through less label data needed for task-specific models.

Compute cost is a significant disadvantage due to the expensive training and inference of these models.

Trustworthiness issues arise from the unvetted unstructured data these models are trained on.

IBM Research is innovating to improve the efficiency and trustworthiness of foundation models.

Foundation models are not limited to language but can be applied to vision, code, and other domains.

IBM is integrating language models into products like Watson Assistant and Watson Discovery.

IBM is also working on vision models for Maximo Visual Inspection and Ansible code models with Red Hat.

IBM has released molformer, a foundation model for molecule discovery and targeted therapeutics.

IBM is building Earth Science Foundation models to enhance climate research.

For more on IBM's efforts to improve foundation models, see the provided links.