An Introduction to Foundation Models
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Generative AI is a popular terminology in the business industry, and its underlying technology, foundation models (FMs), are gaining traction due to its wide application and adoption. The technology is valuable for businesses worldwide because it enables a firm to gain a competitive advantage and accelerate business operations. However, a lot of technical jargon is attached to the field because terms are often mixed up, confused, and misused. Therefore, this blog post serves as your one-stop solution to clearing your basics on FMs and determining their applications for your business.
What are Foundation Models?
Foundation Models (FMs) are general-purpose AI models trained on massive amounts of unlabeled data using self-supervised learning. Generative AI produces novel, contextual, and human-like outputs because FMs are the underlying structure that understands and processes information.
Foundation Models are named so for two reasons. First, they are foundations for myriad industry use cases. For example, FMs help extract insights from unstructured data and help improve business efficiency by automating repetitive tasks to free up valuable time for strategic work. Second, the FMs are useful in fine-tuning models with custom training data for domain-specific and task-specific applications. FMs are expensive to train, so businesses popularly customize a pre-trained FM by fine-tuning it. For instance, LaMDA can be fine-tuned with a business's customer support tickets to enhance its chatbot's capabilities for responding to customers' complaints.
Foundation models already go through the following stages before being fine-tuned for further applications:
- Pre-training: FMs go through training on massive amounts of datasets.
- Generalizability: FMs can perform well across a wide range of tasks.
- Enormous: FMs architecture and extensive dataset provide it with a broad understanding and capabilities.
- Adaptability: FMs are modifiable, and techniques such as fine-tuning make them suitable for a wide range of tasks and applications.
- Self-supervision: FMs don't receive explicit guidance for their learning but learn by making sense of the unlabeled data.
Using learned patterns and relationships, foundation models predict the next item or items in a sequence, such as the next word or image. They can generate many unique outputs from a single input because they generate a probability distribution over all possible outputs that can follow the input, and then pick the output randomly from that distribution.
Businesses fine-tune foundation models for robust applications, widely known as generative AI. Generative AI is transforming businesses and reshaping industries. For example, rapid product development is possible as generative AI enables quick prototyping and testing of new product designs. Data analysis is much more efficient now, as generative AI tools can uncover patterns and insights from vast amounts of unstructured data to drive strategic decision-making within an organization.
Foundation Models vs Large Language Models
Foundation models (FMs) and large language models (LLMs) are two terms often used interchangeably, creating confusion that they are the same. An LLM is a kind of FM capable of understanding and generating only text. FMs, on the other hand, can handle images, text, voice, videos, etc.
While the two terms share some similarities, they are not the same, as can be noted by evaluating their differences. Both have contributed significantly to progress in natural language processing (NLP) and language processing. However, FMs are more general-purpose and less data-intensive than LLMs, which are more specialized and data-intensive.
Hence, the following is a tabular depiction of the similarities and differences between the two.
| Foundation Models | Large Language Models | |
|---|---|---|
| Similarities | Both kinds of models can understand the semantic relationship between words. The models utilize this ability to translate phrases from one language to another and output contextually sensitive and relevant responses to prompts. Word2vec represents words as vectors in a semantic space to interpret meaningful connections between them. | LLMs accelerate understanding semantic relationships between words as they learn the co-occurrence of words and phrases through statistical learning and understanding the context of sentences based on the overall message. GPT-3 can decipher context and meaning in sentences to output intelligible and contextually sensitive responses. |
Foundation Models perform sentiment analysis by deciphering texts' positive, negative, or neutral tone. Jurassic-1 Jumbo is useful for sentiment analysis as the model can classify text based on labels or categories. | LLMs further show advanced sentiment analysis by understanding varied tones such as sarcasm, hypocrisy, joy, etc. BARD can analyze sentiments in a text and understand customers' emotions towards products. | |
FMs enable chatbots to process user inputs and retrieve relevant information. PaLM enables building chatbots using its API. | LLMs facilitate chatbots to output natural and human-like responses, enhancing the customer's conversational experience. GPT-3 chatbot provides contextually sensitive responses and generates coherent answers. | |
| Differences | Foundation Models are applicable for a varied range of tasks. LaMDA helps create content, enhance learning experiences through personalized content, and respond to customer queries. | LLMs are used strictly for language text. Google's T5 is helpful for language tasks such as machine translation, keyword generation, summarization, and conversational AI. |
Foundation Models are not strictly trained only on language data. Hence, the responses are at a generic level. DALL-E 2is trained on vast amounts of text and image pairs. The model understands user inputs through text prompts and outputs desired images. | LLMs are strictly trained on language data, which makes them capable of understanding language intricacies. This makes them output grammatically correct, contextually sensitive, and meaningful outputs. Megatron-Turing NLG, developed by NVIDIA, exhibits proficiency in dialogue generation and other language-related tasks while maintaining grammatical correctness. | |
FMs are prone to output inaccurate results, but they are more innovative. PaLM is a powerful FM with various applications. However, when asked about historical or scientific information, the model's output has shown inaccuracies. | LLMs are primarily stable with their outputs and are more mature, making them a wide choice for business applications. Megatron-Turing NLG has wide applications for businesses due to its interactive responses. This makes it widely adopted by businesses for meetings, summarizations, and virtual assistance. |
Examples of Foundation Models
Different foundation models (FMs) exist for varied applications. For ease of understanding, find below the origins, functions, and areas of applications of these common FMs:
BERT: Google AI's BERT can scrutinize a word's context by considering words that come before and after it. This helps the model decipher the intent behind the search queries. BERT has found business applications in improving search results, enhancing chatbot capabilities by understanding user intent, providing context-sensitive responses, and translating content.
LLaMA: Meta AI's LLaMA is a foundational large language that takes a sequence of words as inputs and predicts the next word to generate text recursively. Like other models, LLaMA helps generate and translate text, answer questions, and generate code. Businesses can utilize LLaMA for customer service support, law firms can look for specific legal information, and e-commerce companies can use it to create product descriptions.
GPT-3: OpenAI's GPT-3 is a language model popular for its use as a creative writing assistant, summarizing text by evaluating lengthy pieces of text and providing informative answers to user's text prompts. Businesses are popularly using it to automate repetitive tasks, generate code, enhance software developer's productivity by assisting during code documentation, and provide feedback for educational content.
SeamlessM4T: Meta AI's SeamlessM4T is a multilingual multimodal AI model for seamless speech and text translations. The model can perform translations for up to 100 languages, depending on the task. It can perform speech recognition, speech-to-text translation, speech-to-speech translation, text-to-text translation, and text-to-speech translation.
Choosing the Right Foundation Model
Here is a proposed strategy for choosing a foundation model (FM) for your business:
1. Brainstorm value and advantages
When choosing a foundation model for your business, consider the value it will add to your business operations. This will help you identify your motive for choosing FM with tailored advantages for your business. Here are a few suggested motivations for selecting the right FM for your business:
Accelerate efficiency: Increase business efficiency by automating tasks and enabling employees to spend time and energy on strategic decision-making.
Enhance decision-making: Improve decision-making by using a foundation model to generate business insights.
Improve customer experience: Provide better services for customers by using a foundation model to personalize content.
Developing better products: Leverage foundation models capabilities for developing and launching new products.
2. Identify business needs and goals
You must identify what you need the foundation model for by assessing resources and budget. You must procure and prepare accordingly based on the current status and feasibility. Here is a brief list of essential factors to consider when it's time for you to choose the right FM for your business:
Technical Requirements: Evaluate the current status of your technical infrastructure and capabilities. FMs require vast amounts of computational resources and data infrastructure. You must assess whether you can provide or procure that kind of data storage and processing. If not, you must consider possible partnerships and tie-ups with businesses specializing in such services.
Personnel: If you want an appropriate a foundation model for your business, you will also need people who can select, maintain, and implement the FM. These could include data scientists, machine learning engineers, or NLP specialists. Identify people from within the business and recruit more if needed.
Cost: Training FM itself is expensive, making access to FMs expensive for a company. As with any other technology, the number of features and applications FMs provide determines their price and value. FMs with more capabilities are expensive, but they are also more generic. Therefore, using large FMs to validate your Minimum Viable Product (MVP) is best. Once your MVP is validated, it is viable to use smaller models, which are cheaper and tailored for your specific business application, with more ease and viability.
Latency: Businesses follow a structured approach for product launches and are often short of time. FMs vary in the time they take to train and deliver desired results. Therefore, you must choose your FM accordingly. Choose a model that prioritizes speed if you need a business application that requires quick responses.
3. Define Application Areas
You must know what you need the foundation model for. While foundation models have vast areas of applications, it's best to evaluate what you need it for so that you can choose the right one for your business based on the purpose you want it to serve. Here are some possible application areas for an FM:
Creating content: Foundation models are a powerful technology for creating business content through compelling marketing copy, writing product descriptions for e-commerce websites, or drafting business reports based on meeting summaries.
Provide customer service: Foundation models are proving to enhance chatbot capabilities by generating human-like responses, and some fine-tuning can make the model better at sentiment analysis and provide empathetic answers to customers.
Developing products: FMs can accelerate product development by analyzing customer reviews from websites, research results, and social media data to improve products and launch new ones.
Research and Development: Foundation models enhance data analysis by evaluating vast amounts of data, which can lay the groundwork for scientific research.
The suggested strategy provides a blueprint for choosing the right foundation model for your business. If you are clear about why you need a foundation model and what you need it for, you can make an informed decision. Choosing the right FM for your business will lay the groundwork for its value and help you gain a competitive advantage.
The Basis for Efficient Business Processes
Foundation models (FMs) are potent tools for generative AI, and their applications transform businesses and reshape industries. By further fine-tuning foundation models, businesses can customize foundation model training data by automating repetitive tasks, developing products efficiently, enhancing customer support, and creating compelling business documents. While both FMs and LLMs power the business applications of generative AI, both contribute differently as FMs are more general-purpose, while LLMs are specialized only for text. FMs unquestionably contribute significantly to efficient business operations. However, businesses must proceed with their usage cautiously by first crafting a strategy for picking the right foundation model for their use cases.
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