Types of Artificial Intelligence
There is no universally agreed-upon definition or delineation in most of the subfields of Artificial Intelligence, and most sources classify the domains in different ways.
In the period spanning late 2022 to early 2023, the rise of consumer-facing generative AI tools marked a significant shift in how both the public and enterprises perceived AI's potential. Although discussions about generative AI's capabilities started with the introduction of GPT-2 in 2019, its full promise only became palpable to businesses recently.
What is Generative AI and where does it fit into the scope of AI?
Generative AI is a subfield of artificial intelligence (AI) that focuses on creating new content, data, or solutions autonomously by learning from existing data. It leverages machine learning techniques, particularly deep learning and neural networks, to generate outputs that resemble real-world examples. This technology has a wide range of applications, such as generating art, creating realistic game environments, data augmentation, drug discovery, and even enhancing privacy by generating synthetic datasets. It has significantly contributed to the advancement of AI by enabling more creative and diverse solutions across various domains. Popular generative AI models include Generative Adversarial Networks (GANs), Variational Autoencoders (VAEs), and Transformer-based language models (e.g., ChatGPT).
Transformer-based language models, like GPT-4, are a class of generative AI models that employ the transformer architecture to excel in natural language processing tasks. Transformers use self-attention mechanisms to capture complex dependencies within textual data, enabling them to generate contextually relevant outputs. These models are pre-trained on large text corpora, learning patterns, and structures that represent the grammar, syntax, and semantics of a language. Once trained, transformer-based models can be fine-tuned for various NLP tasks, including text generation, where they generate coherent and contextually relevant text based on a given input or prompt.
I created my best attempt at a taxonomy that represents the most widely accepted understanding of each AI field. I consider this “Rev 2” and will modify/expand on it in the future as I learn more.
