Different Architectures of Artificial Intelligence and How They Differ

Artificial Intelligence is not a single technology, but rather a whole set of different approaches. Over the past few years, numerous architectures have emerged, each addressing its own tasks and processing data in its own way.

Below are the main types of models that are currently most often discussed in the context of modern AI systems.

LLM — Large Language Models

LLM (Large Language Models) are models that work with text, breaking it down into smaller parts and successively predicting the next element.

Their strength lies in their ability to find patterns in vast amounts of text and generate coherent responses, texts, and arguments.

The most well-known examples are GPT-4, Claude 3, Grok, and Llama 3. These models are trained on hundreds of terabytes of text from books, websites, and code repositories. An interesting fact: large LLMs often exhibit "emergent abilities"—abilities that suddenly appear only after a certain model size (for instance, the ability to solve math problems or make jokes). However, they are very resource-intensive: training a top model can cost tens of millions of dollars and consume as much energy as a small city.

LCM — Large Concept Models

LCM (Large Concept Models) is a newer approach where the model works not with individual words but with whole semantic units.

Instead of processing text step by step, these models operate on "concepts," which allows them to better grasp the overall meaning and structure of information.

LCMs attempt to overcome the main drawback of classical LLMs—the "loss of meaning" in long chains of reasoning. Instead of tokens, they work with high-level ideas, making them promising for complex planning, scientific research, and long-term dialogue. These models can better understand causal relationships and hallucinate less in abstract topics.

VLM — Vision-Language Models

VLM (Vision-Language Models) combine image processing and text.

They can analyze visual information and describe it in words, as well as answer questions related to images.

Bright examples include GPT-4V, Claude-3-Opus, LLaVA, Qwen-VL, and Gemini. These models are already used in real life: from assisting visually impaired people (describing the surrounding world) to automatic analysis of medical images and factory production control. Interestingly, modern VLMs can not only describe pictures but also understand memes, read text in images, and even reason about the sequence of frames in videos.

SLM — Small Language Models

SLM (Small Language Models) are compact versions of language models, optimized for use on devices with limited resources.

They are faster and lighter, but typically fall short of larger models in quality and depth of reasoning.

Examples: Microsoft Phi-3 (3.8B parameters), Google Gemma 2B/7B, Apple OpenELM. These models can be run on a smartphone or laptop without the internet, which is critical for privacy and regions with poor connectivity. Although they are smaller, advanced techniques like distillation and quantization allow them to sometimes come surprisingly close in quality to much larger models.

MoE — Mixture of Experts

MoE (Mixture of Experts) is an architecture in which the model consists of many specialized subsystems.

For each request, only a part of these "experts" is activated, making the system more efficient and scalable.

A classic example is Mixtral 8x7B and Grok-1 (314B). Instead of using all 314 billion parameters simultaneously, MoE activates only 20-30%. This results in enormous energy savings and allows for the creation of truly gigantic models. Currently, MoE is considered one of the main paths toward further scalability of AI.

MLM — Masked Language Models

MLM (Masked Language Models) are trained on tasks involving the recovery of missing parts of text.

They analyze context from both sides simultaneously, which helps in better understanding the meaning of phrases.

The most famous representative is BERT from Google and its descendants (RoBERTa, DistilBERT). The MLM approach has revolutionized Google search, recommendation systems, and sentiment analysis. These models are especially strong in comprehension tasks (classification, named entity recognition) rather than generation, so they are often used as the "brain" within more complex systems.

LAM — Large Action Models

LAM (Large Action Models) are models that go beyond text generation and can perform actions in a digital environment.

They are capable of interpreting requests and turning them into a sequence of operations.

LAM is the next step after chatbots: models that can control browsers, fill out forms, work with applications, or even manage robots. Examples include projects like Adept, Anthropics Computer Use, and various Agent frameworks. They pave the way for true digital assistants that not only advise but actually do the work for humans.

SAM — Segment Anything Models

SAM (Segment Anything Models) are designed to work with images at the level of individual objects.

They allow for the identification and separation of any elements in an image with high accuracy.

The Segment Anything model (Meta, 2023) has become a true breakthrough: it can segment almost any object in a photo or video from a single point or textual description. It is used in photo editing software, medicine (tumor identification), autonomous driving, AR/VR, and generative tools (for precise editing in Midjourney and Runway). It is one of the most versatile computer vision models available today.

Different AI architectures exist not by chance — each solves its own class of tasks. In some cases, speed is more important, while others emphasize understanding images, and yet others focus on the ability to perform actions or work with large volumes of text.

Here is the latest video

Modern Artificial Intelligence is not progressing toward a single universal model, but rather toward a whole series of specialized solutions.

Modern Artificial Intelligence is not evolving toward a single universal model, but rather towards a whole series of specialized solutions and their combinations (for example, MoE + VLM + LAM in one agent). In the coming years, we are likely to see "orchestras" of different architectures, where each part does what it does best.