1. Steering: Leading model companies are working on improved steering techniques to enhance control over large language models (LLMs) and ensure model outputs align with user demands. This progress will address concerns about hallucination and bias in LLMs, fostering greater confidence in their performance and broader adoption in various industries.
2. Memory: Enhanced memory capabilities are crucial for LLMs to provide more personalized and contextual outputs. By expanding context windows and improving retrieval mechanisms, LLMs can better understand individual use cases, leading to refined outputs tailored to specific needs. This will have significant applications in enterprise use cases, such as navigating large knowledge repositories and improving customer support.
3. “Arms and legs”: LLMs have the potential to become more than just knowledge repositories by gaining the ability to execute actions. Companies are working on enabling LLMs to use external tools and perform tasks based on extracted information. This shift towards an action-oriented paradigm will unlock a range of use cases, making apps easier to use and reducing the time required for complex tasks.
4. Multimodality: While current LLMs primarily focus on text-based interactions, the evolution of multimodality will allow LLMs to process and generate content across multiple audio and visual formats. This will enable more comprehensive and engaging user experiences, extending beyond language and incorporating images, audio, and other modalities.
Supplemental Information ℹ️
This article highlights four key unlocks on the horizon for generative AI. The discussed breakthroughs include improved steering techniques for better control over LLMs, enhanced memory capabilities for personalized outputs, empowering LLMs with “arms and legs” to interact with external tools, and the evolution of multimodality for more comprehensive user experiences. These advancements pave the way for exciting possibilities in AI applications, benefiting various industries and users.
This article explores upcoming advancements in generative AI. It covers four important areas: better control over large language models, personalized outputs through enhanced memory, empowering models to use external tools, and the ability to process multiple formats of content. These advancements will make AI more versatile and useful for different industries and users.