Let's explore a powerful tool that forms the cornerstone of Gen AI models: Foundation Models.

Foundation Models, as their name suggests, offer a strong foundation for Gen AI models. They are not only large, but also capable of adjusting to a variety of tasks and producing high-quality outputs. Unlike traditional AI models, Foundation Models have unique features that represent a significant change in the evolution of AI.

Multitasking Abilities of Foundation Models

One of the key differentiating factors of Foundation Models is their multitasking abilities. They are not limited to a single task; instead, they can tackle a wide range of tasks like summarization, question answering, and classification, to name a few. Furthermore, they can handle various data types, including text, images, and code.

Foundation Models can function effectively with little or no training, making them user-friendly and accessible. With their training on extensive and diverse data, these models can learn general patterns and representations that can be applied across various domains and tasks. Even though accessing Foundation Models required specialized machine-learning skills and computational resources in the past, recent advancements in Generative AI have made them more accessible.

Advancements in Machine Learning Platforms

One of these advancements is Google Cloud's Vertex AI, a fully managed Machine Learning platform. This platform allows you to build, deploy, and manage ML models and provides tools for traditional data science, machine learning, MLOps, and AI-driven applications. Vertex AI has recently introduced two powerful tools: Model Garden and Generative AI Studio.

Model Garden, in particular, is an integrated platform where you can explore and interact with both Google's industry-leading models and popular open source models, with Google Cloud's enterprise MLOps tooling support built in. This garden houses both traditional machine learning models and foundation models for Gen AI applications, enabling you to choose the most suitable model depending on your use case, expertise in ML, and budget.

However, it's important to note that Google Cloud isn't the only platform offering these services. Other companies like Amazon, IBM, NVIDIA, OpenAI, Microsoft, DataRobot, and Databricks all provide tools and APIs for both traditional ML models and Gen AI Foundation Models.

Types of Foundation Models

Now, let's have a deeper look into the types of Foundation Models: text models, dialogue models, code completion/generation models, image generation models, and embeddings models. Text models perform natural language tasks with zero or few-shot prompting, helping with tasks like summarization, entity and information extraction, and idea generation. They work effectively right out of the box, but you can provide structured examples to guide their responses for a more tailored experience.

Dialogue models, similar to text models, are also text-based, but they've been fine-tuned to hold natural conversations. They allow you to engage in multi-turn conversations, keeping the context throughout the interaction. An AI chatbot powered by these dialogue models can assist customers, remembering the previous turn of the conversation and providing context-aware responses.

Code completion and generation models act as your supercharged coding assistant. They can help you write code faster and with fewer errors by suggesting the next lines of code or even completing whole functions based on your brief description.

Image generation models allow you to generate and edit images according to your specifications, accommodating various media-related tasks such as classification, object detection, and content moderation mechanisms to ensure responsible AI safety practices.

Embeddings models provide a unique ID card for every word or phrase in the form of a vector. These vectors capture the essence of the word or phrase, its meaning, context, and relationships with other words. They can make sense of unstructured data and power recommendation engines, target advertisements more effectively, and much more.

Conclusion

In conclusion, Foundation Models represent a significant leap forward in AI technology. They provide a robust, adaptable base that can be utilized for a wide range of tasks. Whether it's enhancing customer service with AI chatbots, assisting developers with auto-generated code, or powering recommendation engines, Foundation Models are shaping the future of AI. With these models and the power of Generative AI, we're not just predicting the future; we're building it!

Keep exploring!
Prof. Reza Team