Large Action Models
Large Language Models (LLMs) are rapidly becoming a part of our daily lives and business operations. As these AI models become more sophisticated, understand more, and respond in increasingly creative ways, there is a catch – they stop short of executing real-world actions. Enter Large Action Models (LAMs). LAMs represent the next evolution, designed to understand natural language commands and take decisive actions based on them. This leap from recommendation to action opens a new realm of possibilities.
How LAMs Work
LAMs combine the power of LLMs with a platform that bridges thousands of apps. This lets them understand your natural language requests and then perform actions across those apps. Imagine a system that can book a hotel, order an Uber, and confirm your flight all through a single voice command.
Pilot Case Study: Implementing Large Action Models at NLP Logix
Problem
Here at NLP Logix, we solve our own problems. This means that the solutions we develop and offer to clients are also utilized internally to address our own operational challenges. One such challenge that has emerged within our team revolves around the management of multiple projects. Our team members can often find themselves switching contexts due to the sheer number of projects they handle. This canlead to difficulties such astracking communications. For example, the need to frequently navigate through teams’ messages to ensure nothing crucial is missed, which necessitates multiple daily clean-ups to catch any actionable items that might have been inadvertently overlooked.
In Process Solution
To address this issue, NLP Logix is working towards a solution using the Large Action Model to leverage Microsoft Office and Microsoft Teams. This innovative solution will streamline communication across different team channels. The LAM will operate by aggregating messages related to specific subjects at regular intervals, summarizing them with concise footnotes, and ultimately compiling this information into an email. This process not only organizes the information efficiently but also provides a direct action point for the recipient. Upon receiving the email, team members can simply respond to it, and the LAM will, in turn, generate and post responses within the teams’ channels to all relevant parties. This mechanism effectively reduces the need for individuals to manually sift through and respond to messages across various team platforms.
The implementation of this LAM brings significant benefits to NLP Logix, chiefly in terms of productivity and efficiency enhancements. By reducing the time and effort required for team members to manage communications and action items, focus on the core aspects of our projects will increase.
Human In The Loop
The capacity of LAMs to perform actions such as sending emails to clients based on a chain of actions, presents both incredible efficiencies and potential pitfalls. For instance, a system programmed to automatically forward emails containing specific trigger words can inadvertently escalate to sending out sensitive information broadly, leading to unintended consequences. NLP Logix’s solution is a sophisticated approach that involves incorporating a ‘human in the loop’ system. This method entails the LAM preparing to take an action and then notifying a human with a detailed description and rationale for the intended action, awaiting their approval before proceeding. This safeguard ensures that suggested actions are scrutinized and validated by human judgment to mitigate risks.
LAMs can handle repetitive tasks, freeing humans for more strategic roles.By automating the mundane, we can empower workers to focus on critical decisions and achieve greater efficiency. This creates a stronger human-AI partnership, where both sides contribute to a more successful outcome.
