What are Generative AI models?
TLDRKate Soule from IBM Research discusses the rise of large language models (LLMs) and their role as foundation models in AI, emphasizing their ability to perform various tasks after training on vast unstructured data. She highlights their advantages, such as improved performance and productivity, 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, coding, and climate change.
Takeaways
- 🌟 Large language models (LLMs) like chatGPT have revolutionized AI performance and enterprise value generation.
- 📈 LLMs are part of a class of models known as 'foundation models,' which represent a paradigm shift in AI application development.
- 🛠️ Foundation models are trained on vast unstructured data, enabling them to perform multiple tasks through transfer learning.
- 🔄 These models are based on a generative AI principle, where they predict and generate the next word in a sentence.
- 🎯 By introducing labeled data, foundation models can be fine-tuned to perform specific natural language processing (NLP) tasks.
- 📊 Foundation models offer significant performance advantages due to their extensive training on terabytes of data.
- 🚀 They allow for productivity gains as they require less labeled data for task-specific models compared to traditional AI models.
- 💸 The main disadvantages are high computational costs for training and inference, which can be a barrier for smaller enterprises.
- 🔒 Trustworthiness is a concern as these models are trained on unstructured internet data, which may contain biases and toxic information.
- 🔄 IBM is actively working on innovations to enhance the efficiency and trustworthiness of foundation models for business applications.
- 🌐 Foundation models are not limited to language; they are also being developed for vision, code, chemistry, and climate change research.
Q & A
What are Large Language Models (LLMs) and how have they impacted the world recently?
-Large Language Models (LLMs) are AI models capable of understanding and generating human-like text. They have significantly impacted the world by improving AI performance in various tasks such as writing poetry and planning vacations, showcasing their potential to drive enterprise value.
Who is Kate Soule and what is her role at IBM Research?
-Kate Soule is a senior manager of business strategy at IBM Research. She provides insights into the emerging field of AI and its applications in business settings.
What is the concept of 'foundation models' in AI?
-Foundation models are a class of AI models that serve as a foundational capability to drive a wide range of use cases and applications. They are trained on unstructured data in an unsupervised manner, allowing them to be transferred to multiple tasks and perform various functions.
How do foundation models differ from traditional AI models?
-Traditional AI models are trained on task-specific data to perform specific tasks, whereas foundation models are trained on vast amounts of unstructured data, enabling them to be applied to a multitude of tasks with just a small amount of labeled data or through prompting.
What is the process of training a foundation model?
-A foundation model is trained by feeding it terabytes of unstructured data, often in the form of sentences, and teaching it to predict the next word based on the words it has seen. This training process is largely unsupervised and involves a generative capability to produce new text.
How can foundation models be adapted to perform traditional NLP tasks?
-Foundation models can be fine-tuned by introducing a small amount of labeled data, which updates the model's parameters and allows it to perform specific natural language processing tasks such as classification or named-entity recognition.
What are the advantages of using foundation models in business?
-The advantages include high performance due to extensive data training, and productivity gains as they require less labeled data for task-specific models. Foundation models can drastically outperform models trained on limited data points and can be adapted to various tasks with minimal additional effort.
What are the disadvantages of foundation models?
-The main disadvantages are high computational costs for training and running inference, making them less accessible for smaller enterprises. Additionally, there are trustworthiness issues as the models are trained on vast amounts of unvetted data from the internet, which may contain biases, hate speech, or toxic information.
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 such as vision, code, chemistry, and climate change.
Can you provide an example of how a foundation model can be used in a low-labeled data scenario?
-In a low-labeled data scenario, a foundation model can be used through a process called prompting or prompt engineering. For instance, a model can be given a sentence and asked to classify the sentiment as positive or negative, with the next word it generates serving as the answer to the classification task.
What are some of the domains where IBM is innovating with foundation models?
-IBM is innovating with foundation models in language, vision, code, chemistry, and climate change domains. They are integrating these models into products like Watson Assistant, Watson Discovery, Maximo Visual Inspection, and working on projects like molformer for molecule discovery and Earth Science Foundation models for climate research.
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 enterprise value. Kate Soule, a senior manager of business strategy at IBM Research, provides an overview of this emerging AI field. The paragraph explains that LLMs are part of a class of models known as foundation models, which were预见 as a new paradigm in AI by a team from Stanford. Foundation models are trained on vast amounts of unstructured data, enabling them to perform multiple tasks through transfer learning. The key feature of these models is their generative capability, allowing them to predict and generate the next word in a sentence, thus belonging to the field of generative AI. The paragraph also discusses the process of tuning foundation models with labeled data to perform specific natural language tasks like classification and named-entity recognition, as well as the use of prompting or prompt engineering in low-labeled data scenarios.
🚀 Advantages and Disadvantages of Foundation Models
This paragraph delves into the advantages and disadvantages of foundation models. The primary advantage is their superior performance due to extensive training on terabytes of data, which allows them to outperform models trained on limited data. Another advantage is the productivity gains, as these models require less labeled data for task-specific models through prompting or tuning. However, the paragraph also acknowledges the high computational costs associated with training and running these models, which can be a barrier for smaller enterprises. Trustworthiness is another concern, as the models' training data, often sourced from the internet, may contain biases, hate speech, or toxic information. The paragraph then transitions to discuss IBM's efforts to enhance the efficiency and trustworthiness of these models for business applications. It also mentions the application of foundation models beyond language, including vision models like DALL-E 2 and code models like Copilot, and IBM's innovations in domains such as chemistry and climate change.
Mindmap
Keywords
💡Large Language Models (LLMs)
💡Foundation Models
💡Generative AI
💡Tuning
💡Prompting
💡Performance
💡Productivity Gains
💡Compute Cost
💡Trustworthiness
💡IBM Research
💡Watson Assistant
Highlights
Large language models (LLMs) like chatGPT have revolutionized the AI landscape.
LLMs are part of a new class of models known as foundation models.
Foundation models represent a paradigm shift in AI, moving from task-specific models to versatile, foundational capabilities.
These models are trained on vast amounts of unstructured data in an unsupervised manner.
The generative capability of foundation models allows them to predict and generate new content based on patterns learned from data.
Foundation models can be fine-tuned with a small amount of labeled data to perform traditional NLP tasks.
Tuning and prompting are methods used to adapt foundation models for specific tasks without extensive retraining.
Foundation models can operate effectively even in low-labeled data scenarios.
The performance of foundation models is superior due to their extensive training on terabytes of data.
These models offer significant productivity gains by reducing the need for large labeled datasets.
High compute costs are a disadvantage of foundation models, making them less accessible for smaller enterprises.
Trustworthiness issues arise from the models' training on unvetted, internet-scraped data, potentially containing biases and toxic information.
IBM Research is working on innovations to improve the efficiency and trustworthiness of foundation models.
Foundation models are not limited to language; they are also being developed for vision, code, and other domains.
IBM's Watson Assistant and Watson Discovery leverage language models, while Maximo Visual Inspection utilizes vision models.
Project Wisdom is an initiative by IBM and Red Hat focusing on Ansible code models.
IBM has released molformer, a foundation model for molecule discovery and targeted therapeutics in chemistry.
IBM is developing Earth Science Foundation models to enhance climate research using geospatial data.
The video provides insights into IBM's efforts in making foundation models more practical and reliable for business applications.