The AI industry has in the recent past been witnessing a boom in different types of AI models. It started with models that could only execute tasks related to particular datasets after being trained from scratch. Eventually, after being pre-trained on extensive datasets, NLP models like BERT and GPT-2 started to demonstrate emergent properties. 

A broad spectrum of data is used to create foundation models, thereby rendering it simple to create AI models with specialised functions on top of it. It can be regarded as stacking, where an AI model is stacked on top of the base, which is the foundation model. Foundation models are flexible since they can handle different kinds of data, and are capable of carrying out a variety of activities.  

Why foundation models? 

Big datasets comprising unlabelled as well as labelled information are used to train foundation models. Although unlabelled data is devoid of any tags, labelled data incorporates extra tags, such as a name, number, or other identifiers.  

An AI model must typically be trained on a sizable amount of labelled data which involves facial images in order to accomplish a specific objective, such as facial emotion recognition. Each emotion must then be associated with a tag. This can be a tedious and time-consuming operation. Moreover, the cost, power consumption, and carbon footprint all rise with the training and implementation of several AI models. 

The short version is that limited AI models can be constructed with ease since these foundation models are trained on several labelled and unlabelled datasets with little fine-tuning. When it comes to classic AI models, the final component needs to be adjusted in order to make the model more versatile. 

How do they work? 

Natural Language Processing models have made greater use of foundation models. After being educated on language, the foundation models are adjusted to provide answers to particular queries. Furthermore, deep neural networks are used in foundation models, which are trained using transfer learning and self-supervised learning. 

Transfer and self-supervised learning 

Transfer learning is the process by which a model that has been trained on a large volume of data becomes more efficient by applying its newfound knowledge to other similar tasks. 

The model is trained on unannotated data, or data without labels, in self-supervised learning. The BERT model has been trained to identify missing words in sentences, which allows it to comprehend the sentence's overall meaning effectively. Following this procedure, the foundation model is adjusted to carry out certain jobs now that it is aware of the general patterns and guidelines. Multimodal Foundation models can generate and understand content from different types of data such as visual, text and language. 

The evolving landscape  

Machine learning is currently moving from being 'data-centric' to being 'domain-centric.' When it comes to foundation models, use cases must be considered because they are becoming more and more task-centric.  

For example, AI is utilised on many platforms, including social media posts and email, to recognize emotions. Emotions in emails can be analysed by one model, and messages on social media by another. A single Foundation model can handle all of these responsibilities, eliminating the need for such extensive model construction and usage. 

Countering the data bottleneck issue 

In the initial stages, particular models were created to address issues related to the training data. To complete a single assignment, they had little data. However, by using foundation models, it is possible to customise it to a particular use case while still enabling multitasking, or the execution of multiple tasks at once.  

This marks the beginning of the transition from the limited AI framework to a broader AI framework, containing foundation models that can act as the basis for a variety of applications.  

Furthermore, these Foundation models can complete these jobs with minimal data, which removes data bottlenecks. This eliminates the need for custom models, which are expensive and difficult to train.  

When the Foundation model is trained, it often does not know about the outside world's current evolving changes. For example, the ChatGPT 3 model only has information until 2021. The 'Retrieval Augmented Generation (RAG)' technique is employed to address this issue. By serving as a link between your data and the Foundation model, RAG enhances the model's foundational understanding. This makes it possible for the foundation models to be relevant. 

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