What are Generative AI models?
TLDRKate Soule from IBM Research discusses the rise of large language models (LLMs) as a subset of foundation models, highlighting their ability to perform various tasks through unsupervised training on unstructured data. She emphasizes the advantages of these models, such as improved performance and productivity gains, while acknowledging challenges like high compute costs and trustworthiness issues. IBM's efforts to enhance these models' efficiency and reliability for business applications are also mentioned, along with their applications across different domains like vision, code, and chemistry.
Takeaways
- 🌟 Large language models (LLMs) like chatGPT have revolutionized AI by demonstrating significant advancements in performance and potential for enterprise value.
- 🏗️ LLMs are part of a new class of AI models known as 'foundation models', which represent a paradigm shift in the field of AI.
- 🔄 Foundation models are trained on vast amounts of unstructured data, enabling them to perform a wide range of tasks through transfer learning.
- 🔢 The training data is often terabytes in size, with models predicting the next word in sentences based on the context provided by previous words.
- 🎯 These models fall under the category of generative AI because of their ability to generate new content, such as the next word in a sentence.
- 🚀 Foundation models can be fine-tuned with a small amount of labeled data to perform specific NLP tasks, such as classification or named-entity recognition.
- 💡 Even with limited labeled data, foundation models can be effectively utilized in low-labeled data environments through prompting or prompt engineering.
- 🔥 The primary advantage of foundation models is their exceptional performance, outperforming smaller models trained on limited data sets.
- 💼 Another advantage is increased productivity, as less labeled data is required to achieve task-specific models compared to starting from scratch.
- 💰 Disadvantages include high computational costs for training and inference, making them less accessible for smaller enterprises.
- 🔍 Trustworthiness issues arise due to the vast and unvetted nature of the internet-sourced data used for training, potentially leading to biases and inclusion of toxic information.
Q & A
What are Large Language Models (LLMs)?
-Large Language Models (LLMs) are a class of AI models capable of understanding and generating human-like text. They are trained on vast amounts of data and can perform a variety of language-related tasks, such as writing poetry or assisting in planning vacations.
What is the significance of the term 'foundation models' in AI?
-Foundation models refer to a new paradigm in AI where a single, powerful model serves as a foundation for multiple applications and use cases. This concept was first introduced by a team from Stanford, highlighting a shift from task-specific AI models to more versatile, foundational capabilities.
How are foundation models trained?
-Foundation models are trained on large volumes of unstructured data in an unsupervised manner. They learn to predict the next word in a sentence based on the words they have seen, which is why they are part of the generative AI field.
What is the process of tuning in the context of foundation models?
-Tuning is the process of adapting a foundation model to perform specific natural language tasks by introducing a small amount of labeled data. This allows the model to update its parameters and carry out tasks like classification or named-entity recognition.
How can foundation models be used in low-labeled data domains?
-In low-labeled data domains, foundation models can still be effectively utilized through a process called prompting or prompt engineering. This involves providing the model with a sentence and a question, and the model generates the next word in the sentence as the answer to the question.
What are the main advantages of foundation models?
-The main advantages of foundation models include superior performance due to extensive data exposure and increased productivity gains as they require less labeled data for task-specific models compared to starting from scratch.
What are the disadvantages associated with foundation models?
-The disadvantages of foundation models include high compute costs for training and running inference, as well as trustworthiness issues due to the potential presence of biases, hate speech, or toxic information in the unstructured data they were trained on.
How is IBM addressing the challenges associated with foundation models?
-IBM Research is working on innovations to improve the efficiency and trustworthiness of foundation models, making them more suitable 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.
Can you provide an example of a foundation model in the vision domain?
-An example of a foundation model in the vision domain is DALL-E 2, which takes text data as input and generates custom images based on the text descriptions.
What is IBM's approach to the development of foundation models in different domains?
-IBM is innovating across multiple domains by integrating language models into products like Watson Assistant and Watson Discovery, developing vision models for products like Maximo Visual Inspection, and collaborating with Red Hat on Ansible code models under Project Wisdom. They are also working on chemistry and climate change models.
Outlines
🤖 Introduction to Large Language Models and Foundation Models
This paragraph introduces the concept of Large Language Models (LLMs) and their impact on various applications, from creative tasks like writing poetry to practical ones like vacation planning. It highlights the shift in AI performance and its potential to generate enterprise value. Kate Soule, a senior manager of business strategy at IBM Research, provides an overview of this emerging AI field and its business applications. The paragraph explains that LLMs are a part of a broader class known as foundation models, which are trained on vast amounts of unstructured data, enabling them to perform multiple tasks through a process called tuning. The generative capabilities of these models, which involve predicting the next word in a sentence, are emphasized, as well as their ability to perform traditional NLP tasks with minimal labeled data through prompting or prompt engineering.
🚀 Advantages and Challenges of Foundation Models
This paragraph discusses the advantages of foundation models, such as their superior performance due to extensive data exposure and the productivity gains from reduced label data requirements. It contrasts these benefits with the challenges, including high computational costs for training and inference, which may be prohibitive for smaller enterprises. The paragraph also addresses trustworthiness issues, as these models are trained on vast amounts of unfiltered data from the internet, potentially leading to biases, hate speech, or other toxic content. The speaker mentions that IBM is working on innovations to improve the efficiency and trustworthiness of these models for business applications. The paragraph then expands on the versatility of foundation models beyond language, citing examples from vision and code domains, and mentions IBM's efforts in areas like chemistry and climate change through projects like molformer and Earth Science Foundation models.
Mindmap
Keywords
💡Large Language Models (LLMs)
💡Foundation Models
💡Generative AI
💡Tuning
💡Prompting
💡Performance
💡Productivity Gains
💡Compute Cost
💡Trustworthiness
💡IBM Research
💡DALL-E 2
Highlights
Large language models (LLMs) like chatGPT have revolutionized AI performance and enterprise value.
LLMs are part of a new class of models known as foundation models, which represent a paradigm shift in AI.
Foundation models are trained on vast amounts of unstructured data, enabling them to perform multiple tasks.
These models are capable of generative tasks, such as predicting the next word in a sentence.
Foundation models can be fine-tuned with a small amount of labeled data to perform traditional NLP tasks.
Prompting or prompt engineering allows foundation models to perform tasks even with limited labeled data.
Foundation models offer significant performance advantages due to their extensive training on terabytes of data.
Productivity gains are realized as these models require less label data for task-specific models compared to starting from scratch.
Compute costs are a disadvantage of foundation models due to the expense of training and running inference.
Trustworthiness issues arise as these models are trained on unstructured data that may contain biases and toxic information.
IBM Research is working on innovations to improve efficiency and trustworthiness of foundation models for business applications.
Foundation models are not limited to language; they are also applied in vision, code, and other domains.
IBM's Watson Assistant and Watson Discovery leverage language models, while Maximo Visual Inspection uses vision models.
Project Wisdom by IBM and Red Hat partners is focused on Ansible code models.
IBM has released molformer, a foundation model for molecule discovery and targeted therapeutics in chemistry.
Foundation models are being developed for climate change research using geospatial data.
IBM aims to make foundation models more trustworthy and efficient for practical business applications.