What is a Foundation Model?
Data scientists now approach machine learning (ML) differently with foundation models (FMs), which are enormous deep learning neural networks trained on massive datasets. To create machine learning (ML) models that drive new applications more rapidly and economically, data scientists start with a foundation model rather than creating artificial intelligence (AI) from scratch. Researchers created the phrase “foundation model” to refer to machine learning models that have been trained on a large range of generalised and unlabelled data and are able to carry out a wide range of general tasks, including language comprehension, text and picture generation, and natural language conversation.
What makes foundation models special?
The versatility of foundation model is one of its special qualities. Based on input prompts, these models are capable of accurately completing a wide variety of diverse tasks. Among these tasks are picture categorisation, question answering, and natural language processing (NLP). FMs differ from regular ML models, which usually carry out certain tasks like sentiment analysis of text, picture classification, and trend forecasting, because to their size and general-purpose nature.
Foundation models can serve as base models for more complex downstream applications. The scale and complexity of these models have grown over the course of more than ten years of work.
One of the earliest bidirectional foundation models, BERT, for instance, was published in 2018. A 16 GB training dataset and 340 million parameters were used in its training. Just five years later, in 2023, OpenAI used a 45 GB training dataset and 170 trillion parameters to train GPT-4. Since 2012, the amount of computing power needed for foundation modelling has quadrupled every 3.4 months, according to OpenAI.
Modern FMs, like the text-to-image model Stable Diffusion from Stability AI and the large language models (LLMs) Claude 2 and Llama 2, can do a variety of tasks right out of the box across multiple domains, including creating images, writing blog posts, solving math problems, having conversations, and responding to queries based on a document.
How do foundation models work?
One type of generative artificial intelligence is foundation model. They use one or more inputs (prompts) in the form of instructions in human language to produce output. Complex neural networks, such as transformers, variational encoders, and generative adversarial networks (GANs), serve as the foundation for the models.
Despite the differences in how each kind of network operates, the underlying ideas are the same. Generally speaking, an FM predicts the subsequent item in a sequence by using learnt patterns and correlations. In picture generation, for instance, the model examines the image and produces a clearer, more distinct version of the image. In a similar manner, the model uses the context and preceding words in a text string to predict the following word. following, it uses probability distribution algorithms to choose the following word.
Foundation models generate labels from input data through self-supervised learning. This indicates that no one has used labelled training data sets to instruct or train the model. This characteristic sets LLMs apart from earlier supervised or unsupervised learning ML frameworks.
What problems do foundation models present?
On topics they haven’t had explicit training on, foundation model are able to respond to cues in a logical manner. However, they have some shortcomings. Foundation models confront these challenges:
Infrastructure needs
Training can take months, and creating a foundation model from the ground up is costly and resource-intensive.
Front-end programming
Developers must include foundation model into a software stack that includes tools for pipeline engineering, rapid engineering, and fine-tuning in order to use them in real-world applications.
Not understanding
While foundation models are capable of producing factually and grammatically accurate responses, they struggle to understand the meaning of a request. Furthermore, they lack psychological and social awareness.
Untrustworthy responses
Responses to enquiries on particular topics may be untrustworthy and occasionally offensive, poisonous, or inaccurate.
Bias
Given that models may detect offensive language and offensive undertones in training datasets, bias is a real risk. Developers should filter training data and integrate standards into models to avoid this.
Characteristic of a foundation model
The following are some of the primary characteristics of foundation models:
Scale
Three components are necessary for foundation models to be effective and allow for scale:
- Hardware upgrades. The speed and memory of GPUs, which power the chips in foundation models, have greatly risen.
- Architecture of a transformer model. Many language models, including BERT and GPT-4, are powered by the machine learning model architecture known as transformers.
- Availability of data. These models can learn and train on a large amount of data. To train, foundation models require a lot of unstructured data.
Conventional instruction
Traditional machine learning training techniques, such reinforcement learning via human feedback or a mix of supervised and unsupervised learning, are used in foundation models.
Learning transfer
Models employ transfer learning on surrogate tasks and then refine to a particular task by applying knowledge from one task to another. The GPT-n family of language models use a form of transfer learning called pretraining.
Emergence
Instead of being expressly created, model behaviour is induced. Results from the model are not directly associated with any one of its mechanisms.
Uniformity
A single general learning algorithm could power a variety of applications with homogenization. Numerous domains employ the same fundamental technique. Nearly all of the most advanced natural language processing (NLP) models are derived from one of a small number of foundation models, according to a Stanford Institute HAI report.
Opportunities and challenges of foundation models
Because foundation models possess many skills, such as language, hearing, and vision, they are multimodal.
The following industries could benefit greatly from the many options and use cases that foundation models could offer because to their broad adaptability:
Medical care
For generative applications like drug discovery, foundation models in this sector show potential. Using a standard architecture known as a variational autoencoder, the IBM foundation model Controlled Generation of Molecules, or CogMol, has produced a set of novel COVID-19 antivirals. Another foundation model that Moderna is now using to create messenger RNA medications is IBM’s MoLFormer-XL.
Law
Foundation models could assist with the generative tasks used by law. But as of right now, they are incapable of using logic to produce documents that are truthful. They would be useful in this area if they could be created to demonstrate provenance and ensure factuality.
Education
Understanding the objectives and learning preferences of students necessitates sophisticated human interaction in the complicated field of education. In education, there are numerous distinct data streams that, when combined, are insufficient to train foundation models. Nonetheless, foundation models might be widely used for generative tasks like problem-solving.
Foundation models have a lot of potential, but they can have drawbacks, such as the following:
Bias
Because foundation models are derived from a limited number of fundamental technologies, any AI application may be impacted by inherent biases resulting from social or moral flaws in those few models.
System
One of the main obstacles to increasing the size of models and the volume of data is computer systems. It may be necessary to use an excessive amount of memory to train foundation models. The training is computationally demanding and costly.
Data availability
For foundation models to work, a lot of training data must be available. They lack the fuel to operate if that data is blocked or restricted.
Security
Cybercriminals can target foundation models because they are a single point of failure.
Environment
Training and operating big foundation models, such as GPT-4, has a significant environmental impact.
Emergence
It might be challenging to link the results of foundation models to a certain stage in the development process.
