LLMs are AI models, but not all AI models are LLMs.
Building upon traditional approaches, these eight specialized models advances AI's ability to understand, reason, and generate across different domains and modalities.
Here's architectures of these 8 state-of-the-art models:
1๏ธโฃ LLMs (Large Language Models)
These foundational models process text token-by-token, enabling everything from creative writing to complex reasoning.
2๏ธโฃ LCMs (Large Concept Models)
Meta's newer approach encodes entire sentences as โconceptsโ in SONAR embedding space, transcending word-level processing.
3๏ธโฃ VLMs (Vision-Language Models)
These multimodal combine visual and textual understanding to interpret images and generate text about them.
4๏ธโฃ SLMs (Small Language Models)
Compact yet powerful models optimized for edge devices with tight energy and latency constraints.
5๏ธโฃ MoE (Mixture of Experts)
These models activate only relevant expert networks per query, dramatically improving efficiency while maintaining performance.
6๏ธโฃ MLMs (Masked Language Models)
The OG bidirectional models that look at both left and right context to understand meaning in text.
7๏ธโฃ LAMs (Large Action Models)
Emerging models that bridge understanding with action, executing tasks through system-level operations.
8๏ธโฃ SAMs (Segment Anything Models)
Foundation models for universal visual segmentation with pixel-level precision.
Here's how these specialized architectures differ from traditional approaches:
Traditional AI:
- One model architecture applied to many tasks
- Often excels in one area but underperforms in others
- Requires significant compute and data for general capabilities
Specialized Architectures:
- Purpose-built for specific modalities and tasks
- Optimized for particular constraints (speed, size, precision)
- Open up new capabilities like concept-level understanding, visual segmentation, and action execution
Understanding these distinctions is essential for selecting the appropriate model architecture for specific applications, making more effective and contextually appropriate AI interactions.
These specialized models aren't alternative approaches; they're redefining technologies.
โ Process information in ways that match specific tasks and domains
โ Optimize for different constraints like size, speed, accuracy, and multimodality
โ Generate more reliable, contextual, and useful outputs for targeted applications
Matching the right architecture to the right task is essential. It saves time, boosts productivity, and creates a more natural flow in AI-human interactions.
Over to you: What specialized AI architecture do you think would benefit your work the most?
Building upon traditional approaches, these eight specialized models advances AI's ability to understand, reason, and generate across different domains and modalities.
Here's architectures of these 8 state-of-the-art models:
1๏ธโฃ LLMs (Large Language Models)
These foundational models process text token-by-token, enabling everything from creative writing to complex reasoning.
2๏ธโฃ LCMs (Large Concept Models)
Meta's newer approach encodes entire sentences as โconceptsโ in SONAR embedding space, transcending word-level processing.
3๏ธโฃ VLMs (Vision-Language Models)
These multimodal combine visual and textual understanding to interpret images and generate text about them.
4๏ธโฃ SLMs (Small Language Models)
Compact yet powerful models optimized for edge devices with tight energy and latency constraints.
5๏ธโฃ MoE (Mixture of Experts)
These models activate only relevant expert networks per query, dramatically improving efficiency while maintaining performance.
6๏ธโฃ MLMs (Masked Language Models)
The OG bidirectional models that look at both left and right context to understand meaning in text.
7๏ธโฃ LAMs (Large Action Models)
Emerging models that bridge understanding with action, executing tasks through system-level operations.
8๏ธโฃ SAMs (Segment Anything Models)
Foundation models for universal visual segmentation with pixel-level precision.
Here's how these specialized architectures differ from traditional approaches:
Traditional AI:
- One model architecture applied to many tasks
- Often excels in one area but underperforms in others
- Requires significant compute and data for general capabilities
Specialized Architectures:
- Purpose-built for specific modalities and tasks
- Optimized for particular constraints (speed, size, precision)
- Open up new capabilities like concept-level understanding, visual segmentation, and action execution
Understanding these distinctions is essential for selecting the appropriate model architecture for specific applications, making more effective and contextually appropriate AI interactions.
These specialized models aren't alternative approaches; they're redefining technologies.
โ Process information in ways that match specific tasks and domains
โ Optimize for different constraints like size, speed, accuracy, and multimodality
โ Generate more reliable, contextual, and useful outputs for targeted applications
Matching the right architecture to the right task is essential. It saves time, boosts productivity, and creates a more natural flow in AI-human interactions.
Over to you: What specialized AI architecture do you think would benefit your work the most?
