Foundation models refer to AI systems that are developed using a large amount of typically unlabeled data. Some of these models are available as open source, while others are proprietary. When released, the proprietary models are generally accessible through application programming interfaces. Foundation models can be trained using a single type of medium, such as unstructured text, or multiple types, like text combined with images or text alongside code. These models 'learn' the connections between various data points. The extensive range of training data allows these models to be adaptable and capable of handling numerous use cases, including those for which they have not been explicitly trained.
Traditional AI to Gen AI
In just a few months, AI has transitioned from performing tasks such as classification, clustering, dimensionality reduction, and prediction to becoming a creator of original content. Generative AI denotes the capability of AI models to produce new content, which encompasses text, images, video, code, audio, and synthetic data. In this brief overview, we will clarify the main distinctions between traditional AI and generative AI, explore the foundational models that drive generative AI, provide a glimpse into its relatively brief history, and outline some of its diverse applications, risks, and considerations.
The rise of generative AI, supported by foundation models, does not imply that traditional AI is obsolete. Rather, generative AI enhances the existing toolkit and can assist in addressing new challenges.
Why are Foundational Models Important?
Foundation models, including LLMs as one of their types, are fundamental to generative AI. When these pre-trained general-purpose models undergo fine-tuning — meaning they are trained on additional data pertinent to a specific subject — they can be tailored for particular applications. For instance, FinBERT is a BERT model that has been pre-trained on financial documents, such as financial statements, earnings call transcripts, and analyst reports, enhancing the precision of its finance-related analyses and outputs.
While fine-tuning is crucial, it is not the sole method for optimizing outputs. Less resource-intensive options for IT infrastructures include prompt engineering, utilizing longer context windows to offer more intricate input instructions, and employing plug-ins. Plug-ins can equip models with external resources for information retrieval and broaden their capabilities, enabling them to, for instance, search through customer databases. Prompt engineering, longer context windows, and plug-ins are not mutually exclusive; many organizations are likely to invest in all three strategies.
Foundation models drive generative AI and represent a new class of deep learning. Similar to deep learning algorithms, foundation models are AI neural networks, yet they surpass older models in complexity and size, both regarding the number of embedded parameters and the amount of data required for training.
In contrast to foundation models, traditional AI deep learning models that utilize algorithms, such as recurrent neural networks and convolutional neural networks, have a limited scope and are designed for particular applications. For instance, a manufacturing firm may implement AI with deep learning computer vision systems for managing warehouse inventory. The applications for foundation models are more extensive since they are trained on vast and varied datasets. This does not inherently enhance the quality of the output. Nevertheless, foundation models can be better tailored to specific use cases if humans refine the models or provide more detailed instructions.
Use Cases
Companies have only recently started the large-scale productization of these models. The generative AI market is projected to grow at a compound annual growth rate of 58% from 2023 to 2028, potentially reaching $36.4 billion by the end of 2028. The foundation model segment is expected to generate revenue of $11.4 billion by 2028, remaining relatively consolidated due to the substantial resources and budget necessary for pre-training models that can compete with leading foundation models. This situation creates significant barriers to entry, meaning that large technology firms, which already have considerable computational resources, will dominate a significant portion of the foundation model market. Most startups are likely to build upon these models, for instance, by adding services or offering additional training to enhance their performance. The influence of tech giants will be somewhat mitigated by regional foundation models that are tailored to local languages and contexts, such as Abu Dhabi's Falcon, an open-source LLM.
Risks of Gen AI and Foundational Models
Generative AI, driven by foundation models, holds significant potential; however, the associated risks and limitations necessitate a human-in-the-loop strategy. Some prevalent risks linked to foundation models include:
Hallucinations: Foundation models can generate fictitious responses due to insufficient context in prompts, biases present in the training data, and the quality of that data, among other factors.
Output inaccuracies, such as outdated or limited information: The current architectures of foundation models make them prone to inaccuracies, but this issue can be alleviated by implementing deterministic controls, like vector databases, which anchor responses in real data.
Misuse: The harmful application of AI, which includes creating deepfake images or videos and executing generative AI-driven cyber-attacks.
Biased responses: Outputs may be discriminatory or unjust due to the presence of human biases (gender, political, racial, etc.) in the training data, infrequent updates to the training data, or insufficient diversity within it. Additional bias concerns may arise from the organizations behind the model; for instance, companies or governments might incorporate their cultural sensitivities into a model, potentially leading to biases.
Lack of transparency: The challenge for humans to elucidate how the model reached a particular conclusion or to replicate its responses.
Intellectual property issues: The risk that models could generate outputs similar to existing content, potentially resulting in copyright infringements. A precedent in the U.S. indicates that machine-generated content is not eligible for patent or copyright protection since it is not created by a human.
Data privacy: Worries that sensitive information may inadvertently be exposed in publicly accessible models.
Infrastructure requirements: Generative AI entails considerable computational and network resource needs, which escalate infrastructure costs and hinder sustainability initiatives.
Now that you have understood the core of what foundational models are, you may want to consider a career in using foundation models to power generative AI applications. With the rise in gen AI job vacancies, you can learn all complex concepts at Eduinx, a leading edtech institute based in Bangalore. Our mentors are here to guide you through every step of your journey and help you land an appropriate job with the right career support. Get in touch with us to know more about our generative AI course.
