Energy-Efficient Domain-Specific Artificial Intelligence Models and Agents: Pathways and Paradigms

2025-10-24

AI Summary

To address the critical bottlenecks of large language models—high energy consumption, hallucination propensity, and poor deployability in safety-critical applications—this project proposes a next-generation lightweight, domain-specific multimodal AI paradigm. Methodologically, it integrates multimodal learning, continual learning, neurosymbolic reasoning, and brain-inspired computing architectures to build intelligent agents capable of real-time perception, reasoning, planning, and decision-making, co-designed with high-energy-efficiency hardware. Key contributions include: (1) enabling online inference and autonomous evolution in dynamic environments by synergistically leveraging prior knowledge and streaming data; (2) breaking conventional energy-efficiency limits, targeting over 1000× improvement in energy efficiency; and (3) establishing a theoretical framework and technical pathway for low-power intelligent agents robust to real-world uncertainty.

Technology Category

• Machine Learning: Large Multimodal Models (LMMs)
• Intelligent Robots: Multimodal Perception & Sensor Fusion
• Cognitive Modeling & Cognitive Systems: Agent Architectures

Application Category

• Semantics and Knowledge: Data modeling to support human-machine intelligence, including LLMs agents, intelligent system behavior, explanations, and user-friendly interactions
• Search and Retrieval-Augmented AI: Retrieval-Augmented Generation (RAG) and multi-modal RAG
• Systems and Infrastructure for Web, Mobile and WoT: Applied ML and AI for Web-based mobile applications

Abstract

The field of artificial intelligence (AI) has taken a tight hold on broad aspects of society, industry, business, and governance in ways that dictate the prosperity and might of the world's economies. The AI market size is projected to grow from 189 billion USD in 2023 to 4.8 trillion USD by 2033. Currently, AI is dominated by large language models that exhibit linguistic and visual intelligence. However, training these models requires a massive amount of data scraped from the web as well as large amounts of energy (50--60 GWh to train GPT-4). Despite these costs, these models often hallucinate, a characteristic that prevents them from being deployed in critical application domains. In contrast, the human brain consumes only 20~W of power. What is needed is the next level of AI evolution in which lightweight domain-specific multimodal models with higher levels of intelligence can reason, plan, and make decisions in dynamic environments with real-time data and prior knowledge, while learning continuously and evolving in ways that enhance future decision-making capability. This will define the next wave of AI, progressing from today's large models, trained with vast amounts of data, to nimble energy-efficient domain-specific agents that can reason and think in a world full of uncertainty. To support such agents, hardware will need to be reimagined to allow energy efficiencies greater than 1000x over the state of the art. Such a vision of future AI systems is developed in this work.

Problem

• Developing energy-efficient domain-specific AI models
• Creating agents that reason and plan in dynamic environments
• Reducing energy consumption while enhancing decision-making capabilities

Innovation

• Developing lightweight domain-specific multimodal AI models
• Creating energy-efficient agents for reasoning in uncertainty
• Reimagining hardware for 1000x energy efficiency gains