Over the last year, a new type of artificial intelligence technology called foundation models (FMs) has rapidly emerged. FMs are revolutionizing the field of AI and enabling incredible new generative capabilities.
FMs are large, multipurpose machine learning models that can be adapted for a wide range of tasks. They are typically pretrained on huge datasets in a self-supervised manner to capture intricate patterns within the data. This allows them to develop a deep understanding of the concepts and relationships contained in the datasets.
Unlike traditional AI models that are narrowly focused on specific problems, FMs have much broader applications. Their flexible nature means you can fine-tune an FM that has already learned representations from large datasets to then perform specialized downstream tasks. This Transfer Learning approach saves significant time and resources.
Some examples of tasks FMs can accomplish include natural language processing, computer vision, speech recognition, translation between languages, content generation, conversational assistants, and much more. The possibilities are rapidly expanding.
In this lesson, we will explore what foundation models are, how they work, the different types of FMs, and use cases. We’ll also take a deeper look at Large Language Models (LLMs), which are a specific type of FM optimized for understanding and generating natural language.
The advent of FMs and LLMs represents a turning point for AI. These powerful models are the engines behind innovations like chatbots, personalized recommendations, automatic document summarization, and new creative applications. As FMs continue to advance, they promise to reshape industries across the board.
Generative AI Powered by Foundation Models
Generative AI aims to develop machine learning models that can generate novel, high-quality data and artifacts like text, images, video, and more. A promising approach to building these generative models is through leveraging foundation models.
Generative Models are trained to learn the complex distributions and properties of data in order to produce completely new, original output samples. Some popular generative architectures include Generative Adversarial Networks (GANs), Variational Autoencoders (VAEs), and autoregressive models.
What makes foundation models well-suited for generative tasks is their pretraining methodology. Foundation models are pre-trained models leverage massive datasets, unlabeled datasets across diverse modalities like text, vision, and audio data. By pretraining on this wide variety and vast amounts of unlabeled data, foundation models build expansive world knowledge and representations that form a strong generative starting point.
A key advantage of foundation models lies in their exceptional scale and model size. State-of-the-art foundation models may have billions or trillions of parameters, massively exceeding traditional model sizes. This high model capacity fuels their versatility - applicable across a broad range of applications.
After foundational pretraining, foundation models can fine-tune on downstream generative tasks through techniques like prompt engineering and conditioning. This adaptability empowers tackling a variety of generative tasks like conversational AI, image super-resolution, text-to-image synthesis, and much more in a highly performant manner.
In summary, leveraging massive pretrained foundation models enables generative AI systems to create novel, high-fidelity artifacts that distill intricate real-world patterns into creative new works spanning modalities and industries. The future looks bright for foundations models to push boundaries in generative capacities and positive societal impact through responsible development.
Human-Centered Artificial Intelligence
As artificial intelligence systems grow more advanced and integrated into society, an important paradigm is shaping leading research and development methodologies - human-centered AI (HAI).
The principles of human-centered AI place focus directly on developing AI to respect and empower people. Key tenets of HAI include:
- Building interpretability into models so people can understand why they behave in certain ways
- Enabling human oversight and control rather than fully autonomous systems
- Considering social impact with the goal of augmenting humans rather than replacing them
- Incorporating human feedback loops to realize preferences and values
- Promoting accessibility so all groups can benefit from progress in AI
By grounding the technology in serving human needs first, HAI aims to realize AI's profound potential to positively transform lives. Research initiatives at institutions like Anthropic and beyond are coalescing around this future vision. The years ahead are sure to demonstrate AI as a collaborative amplifier of human intelligence.
The responsible implementation of artificial intelligence will define new computing frontiers in the 21st century. With people at the center guiding its evolution each step ahead, the brightest future emerges.
Understanding FM Functionality
What Are Foundation Models?
Foundation models (FMs) represent a new paradigm in artificial intelligence compared to traditional machine learning approaches. Unlike most AI models which are narrow in scope and focus on specific tasks, FMs are large, multipurpose models that can adapt to a wide range of downstream applications.
Specifically, foundation models have three key characteristics:
- Size - FMs contain billions or trillions of parameters, allowing them to learn very intricate patterns and concepts from huge datasets. Their scale enables knowledge transfer across tasks.
- Breadth - Instead of specializing in one domain, FMs develop broad understanding and capabilities from their diverse training. This general knowledge can then apply to new situations.
- Adaptability - You can customize FMs for specialized tasks through transfer learning. Fine-tuning the pretrained parameters on smaller downstream datasets produces high performance at a fraction of the compute costs.
In essence, foundation models learn generalized world knowledge during the initial self-supervised pretraining phase. Then this strong base model becomes a springboard to efficiently optimize for more specific jobs.
Rather than building customized narrow AI models from scratch for each new application, FMs provide a reusable head start. This makes them an increasingly popular starting point to deploy production-grade AI. Leading examples of foundation models include Google's BERT, OpenAI's GPT models, Meta's OPT, Anthropic's Constitutional AI, and more.
Self-Supervised Learning
A key component that sets foundation models apart is their training methodology. Instead of requiring manually labeled data like in supervised learning, foundation models leverage self-supervised learning techniques.
With self-supervised learning, the input data itself provides the supervision signal without human intervention. The model looks for and learns patterns inherent in the structure of the unlabeled data.
For example, if training a text foundation model, it may mask or hide 15% of input words and then try to predict the missing words based on context clues. Or a computer vision model may be tasked with determining the original spatial relationship between portions of an image that have been scrambled.
Through these self-directed tasks, the model learns powerful representations from the data without explicitly being told the "right answers." This allows using vast datasets that would be infeasible for humans to hand label.
Key advantages of self-supervised learning include:
- Requires less human effort to prepare training data
- Can leverage billions of unlabeled examples
- Discovers intricate patterns within the data itself
- Builds universal representations applicable to many tasks
This automated training paradigm combined with enormous datasets gives rise to the impressive capabilities of modern foundation models. The self-supervised phase establishes a strong base model, which can then fine-tune on smaller downstream tasks.
Training, Fine-Tuning, and Prompt Engineering
There are several key training phases that allow foundation models to reach their full capability:
Pretraining
The unsupervised pretraining phase, often using self-supervised learning as discussed earlier, establishes the broad general knowledge of a foundation model. Models may also employ reinforcement learning from human feedback (RLHF) during pretraining to help align the model with beneficial behaviors.
Massive computational resources are leveraged to pretrain on huge datasets, from hundreds of gigabytes to petabytes of data, over the course of weeks to months. This intensive foundational training enables models to understand concepts, relationships, and patterns.
Fine-Tuning
While pretraining trains the general intelligence, the fine-tuning phase specializes the model for specific tasks. Fine-tuning adapts just the final layers of the neural network on smaller downstream datasets.
This transfer learning approach is more efficient than only pretraining or training an entire model from scratch. Fine-tuning for a particular task like text summarization produces a highly performant, tailored model.
Prompt Engineering
Prompt engineering formulates the model input to provide helpful context and steer the desired output. For example, a prompt could specify to summarize an input text appropriately for a 2nd grade student. Prompt engineering combines an understanding of the pretraining data and model capabilities to craft prompts that yield optimized results.
The combination of broad pretraining followed by fine-tuning and directed prompts allows users to tap into the versatile power of foundation model intelligence.
This chapter covered the training strategies enabling the flexibility, knowledge, and customization of foundation models. Next we will explore the major categories and architectural patterns of FMs.
Types of Foundation Models
While foundation models share common traits of scale, breadth, and adaptability, they can be categorized into various architectures designed for different data types and use cases.
Text-to-Text Models
Text-to-text models process and generate natural language. They are trained on vast text corpora, allowing them to deeply understand linguistic concepts.
Applications of text-to-text foundation models include:
- Content generation - Write blog posts, emails, code, and more
- Question answering - Provide information to user queries
- Summarization - Condense documents into concise overviews
- Sentiment analysis - Gauge emotional tone/intent in text
- Translation - Convert between languages
Popular examples include Google's BERT, OpenAI's GPT models, Anthropic's Claude, Meta's Llama and more.
Text-to-Image Models
Text-to-image models synthesize images from textual descriptions. They are commonly based on diffusion models which add noise then slowly denoise images during training.
Leading text-to-image foundation models such as DALL-E 2, Imagen, and Stable Diffusion can produce remarkably high-fidelity images reflecting messages conveyed in text prompts.
Other Types
There are also foundation models for video, audio, 3D shapes, and multimodal applications combining different data types like text, vision, and speech.
Text-to-Audio Models
Text-to-audio models convert textual information into human-like audio narrations. They intuit prosodic patterns to make generated speech sound more natural.
Text-to-Video Models
Text-to-video models generate video content from textual narrative scripts. They are able to align text describing sequential events to corresponding video frames.
Multimodal Models
Multimodal foundation models such as GPT-4, jointly process inputs across modalities like text, vision, speech, and more. This develops unified representations across data types to enable aligned cross-modal generation.
As computational power grows, the capabilities of foundation models across mediums continue expanding. Pretraining on huge datasets unlocks the ability to perform an increasingly diverse set of tasks.
This chapter provided an overview of major foundation model varieties. Next we will focus specifically on large language models which are commonly fine-tuned for natural language tasks.
Focus on Large Language Models
What Are LLMs?
Large language models (LLMs) are a specific type of foundation model specialized in understanding and generating natural language. As the name suggests, they focus entirely on language data and tasks.
LLMs are comprised of billions or trillions of parameters, allowing them to deeply comprehend linguistic concepts based on their pretraining on massive text corpora. For example, Anthropic’s Constitutional AI Claude has 12 billion parameters.
Key abilities of large language models include:
- Understanding context and nuance in written language
- Engaging in conversational dialogues
- Producing written content that reads naturally
- Translating between languages
- Answering questions about text passages
- Summarizing the key points of documents
LLMs represent the cutting edge of natural language processing capabilities. Their flexible architectures allow optimizing for a diverse range of language-focused tasks through fine-tuning on downstream datasets.
As computational power continues growing, enabling training ever-larger models on more data, LLMs are likely to become even more adept at communicating, understanding, and generating expressive language.
In the next sections we will explore how LLMs achieve these remarkable language skills from an architectural and use case perspective.
LLM Functionality
Large language models achieve their advanced natural language understanding and generation capabilities due to two key architectural factors:
Transformer Architecture
Most modern LLMs employ a transformer-based neural network. Transformers process data simultaneously through parallelization, unlike previous sequential models. This allows much faster training times.
Transformers have an encoder mechanism to contextualize input and a decoder to produce output text. The encoder and decoder contain multi-headed self attention layers to interrelate concepts.
The transformer patterns underpinning LLMs contribute greatly to their ability to model real-world linguistic relationships and nuances.
Neural Network Layers
LLMs contain multiple internal neural network layers that progressively build up representations of language:
Embedding Layer - Converts input text into numeric vectors representing word meanings in context
Feedforward Layer - Processes the embeddings to identify patterns between words
Attention Layers - Relate current words/phrases to other relevant context
As data passes through these layers, the model develops a hierarchical understanding of language leading to informed output generations.
Together, the transformer architecture and internal neural network processing layers enable large language models to achieve state-of-the-art natural language tasks across many domains.
In the next section we will highlight some real-world use cases benefiting from deployment of fine-tuned LLMs.
Use Cases
Thanks to advances in model architecture and scale, fine-tuned large language models can perform a variety of tasks and unlock a myriad of valuable real-world applications:
Content Creation
LLMs can generate any form of written content such as blog posts, tweets, emails, essays, code, and more. Journalists, marketers, and other creators are increasing leveraging LLMs to augment their productivity.
Virtual Assistants and Conversational AI
Chatbots and digital assistants like Siri or Alexa rely on underlying LLMs to engage in helpful dialogue. LLMs' ability to understand context and intent empowers smooth conversations.
Coding Assistants
LLMs fine-tuned on code can autocomplete lines of code, provide examples, identify bugs, or refine code. This aids programmers in writing better quality software faster.
And Much More...
Other use cases tapped by fine-tuned LLMS include:
- Legal contract analysis
- Academic paper writing
- Financial report generation
- Medical literature analysis
- Game dialogue generation
- Image Captioning
As LLMs continue rapidly advancing, their flexibility unlocks new applications across domains. Combined with a targeted prompt and dataset, LLM capabilities stretch as far as our creativity.
In summary, foundation models represent a new era of artificial intelligence characterized by unprecedented scale and versatility. There are vast use cases and potential of Foundation Models.
Key points we covered:
- Foundation models learn general world knowledge through self-supervised pretraining, allowing adaption to specialized tasks
- Major FM types include text-to-text for language, text-to-image for image generation, and other modalities
- Large language models like GPT-3 possess exceptional linguistic understanding and production from fine-tuning
- Real-world use cases with societal impact span content creation, coding, conversational agents, and more
Yet with their rapidly accelerating capabilities comes great responsibility. Considerations around potential downsides like bias, misinformation, and job disruption must be addressed responsibly.
Looking forward, the future is exceedingly bright for generative foundation models to augment human creativity and productivity if guided prudently. Emerging techniques like reinforcement learning from human feedback hold promise for even stronger and beneficial alignment with human preferences and values.
Harnessing these powerful models for positive impact will depend on the collective priorities and wisdom of researchers, developers, policymakers, and society as progress marches forward. If cultivated judiciously, foundation models stand ready to take a leading role ushering in an age of accelerated innovation across industries.
