Lost in the AI lexicon: Decoding the AI Model Explosion

The public release of the ChatGPT website interface in November 2022 with GPT-3.5 marked a pivotal moment, rapidly bringing the capabilities of advanced AI into mainstream awareness and usage globally. This platform showcased the power of Large Language Models (LLMs), often referred to as chat models, which represent a major category within the rapidly evolving field of artificial intelligence. These models, exemplified by the GPT series and BARD from Google, are built upon architectures like the Transformer, introduced in 2017. Their primary strength lies in understanding and generating human-like text, enabling tasks ranging from answering questions and writing code to translation and creative writing. They learn patterns, grammar, and knowledge from vast amounts of text data, allowing them to engage in coherent and contextually relevant conversations.

However, the landscape of AI extends far beyond text-based chat. Building upon the same foundational Transformer principles, multimodal models have emerged. Unlike LLMs that primarily process text, multimodal models are designed to understand and integrate information from multiple types of data simultaneously. A key example is combining text and images. Models like OpenAI's CLIP learn associations between visual concepts and language, while Large Multimodal Models (LMMs) such as GPT-4V(ision), Google's Gemini or Claude 3 family can directly process and reason about inputs containing both text and images interleaved. This allows them to perform tasks like describing images, answering questions about visual content, or following instructions that refer to elements within a picture, representing a significant step towards more comprehensive AI understanding.

Distinct from models primarily focused on understanding or conversation are image generation models. These AI systems specialize in creating novel visual content. While early successes were seen with Generative Adversarial Networks (GANs), the post-Transformer era saw the rise of powerful new approaches. Some, like the initial DALL-E, directly employed Transformer architectures to generate images pixel by pixel or token by token based on text descriptions. More recently, Diffusion Models, such as those powering Stable Diffusion, DALL-E 2/3, and Midjourney, have become state-of-the-art. These models work by learning to reverse a process of adding noise to an image. Starting from random noise, they gradually refine it, guided by a text prompt (often processed by a Transformer-based text encoder like CLIP), to produce highly detailed and coherent images. Though the core mechanism is diffusion, they frequently incorporate Transformer components, particularly attention mechanisms, for effective text conditioning.

Building upon the advancements in image generation, text-to-video models represent another rapidly evolving frontier in generative AI. These models tackle the significantly more complex challenge of creating video sequences directly from textual descriptions. This requires not only generating visually plausible frames but also ensuring temporal consistency, realistic motion, and coherent narrative progression over time. Early approaches often extended text-to-image techniques, while newer models like OpenAI's Sora, Google's Veo and Lumiere, Runway's Gen-2, and Pika employ sophisticated architectures, often involving spatio-temporal diffusion or specialized Transformer variants, to generate entire video segments with greater fidelity and coherence. Like their image-generating counterparts, they rely heavily on powerful text encoders to interpret prompts and guide the generation process, pushing the boundaries of creating dynamic visual content from language.

Finally, the concept of reasoning models often refers less to a fundamentally different architecture and more to the enhanced capabilities being developed within advanced LLMs and LMMs. While the base Transformer architecture provided the means for powerful pattern recognition and text generation, achieving complex, multi-step reasoning required further advancements. This improvement comes not just from scaling up model size and training data, but also through sophisticated training techniques like instruction tuning and Reinforcement Learning from Human Feedback (RLHF). Furthermore, prompting strategies like Chain-of-Thought (GPT o3, o4-mini-high), developed after the initial LLM breakthroughs, guide models to break down problems and "think" step-by-step, significantly boosting their performance on tasks requiring logical deduction, mathematical problem-solving, and planning. Therefore, reasoning is an advanced capability built upon foundational models, pushing the boundaries of what AI can achieve beyond simple text or image processing towards more complex cognitive tasks.

In essence, while the ChatGPT website brought conversational LLMs dramatically into the public eye in late 2022, it represents just one facet of AI development spurred by architectures like the Transformer. Multimodal models bridge the gap between different data types, image and video generation models create novel visual realities, and the ongoing pursuit of better reasoning pushes these systems towards more sophisticated problem-solving. Each category, while often interconnected and leveraging shared technological principles, addresses distinct challenges and unlocks unique capabilities within the broader AI ecosystem.

Written with the assistance of Aethera Compose, an Aethera AI product I contributed to developing.