While Large Language Models (LLMs) dominate AI conversations, alternative technologies are emerging with unique strengths. LLMs excel at natural language processing but struggle with reasoning, real-time updates, and contextual understanding. They also present challenges like bias, high computational costs, and the potential for generating inaccurate information.
Alternatives like Liquid Learning Networks (LLNs) offer continuous learning capabilities, adapting in real-time to new data. Small Language Models (SLMs) require less computing power and are less prone to 'hallucinations'. Logical reasoning systems, a more established AI approach, can process data based on logic, addressing a key weakness of LLMs. Open-source models such as Google's Gemini, Meta's LLaMa, and OpenAI's GPT-oss are also gaining traction, offering customisation and cost-effectiveness. Companies are exploring AI physics models to simulate and accelerate design processes across industries.
These emerging AI models and techniques could complement LLMs, addressing their limitations and potentially reshaping the AI landscape. Combining different AI models may lead to more efficient and accurate solutions for specific tasks.
