As the use of AI surges across industries, it accelerates the evolution of technology. New concepts and ideas emerge and become converted into new models that can provide a competitive advantage. The latter is why innovation-oriented businesses monitor everything related to large action models (LAMs) and their application among enterprises.
Claimed to reshape the future of technology, LAMs are igniting the hype, looking like the next-level disruption—and yet, the information about them is scarce, leaving many gaps and blindspots. Are LAMs what they seem?
Since artificial intelligence is a very complicated matter, every novelty should be investigated carefully before action is taken. This article illuminates the blindspots and debunks the myths concerning large action models.
What is a large action model (LAM)?
Large action model is an artificial intelligence model that can understand and execute complex tasks by translating human intentions into action. Within LAMs, such levels of autonomy and comprehension turn generative AI into an active assistant that can perform various tasks, from booking rooms to making complex decisions based on past and present data analysis.
To achieve this level of intricate decision-making, LAMs are said to learn from massive datasets that contain user action information and use this data for strategic planning and proactive action in real-time.
Prompt engineering
Creating a prompt to instruct models to provide relevant results (inputs). The success of the prompt response depends on the data the model was provided with.
Zero-shot learning
This test measures the model's ability to use its pre-training data by making it respond to a generated scenario of a task it hasn't been specifically trained for.
Few-shot learning
Enabling the model to process new task requirements by giving it several task examples within a submitted input.
While the concept of large action models already sounds revolutionary and certainly has the potential to change the way industries operate, from healthcare to retail, it's important to establish whether they act exactly as they sound and evaluate the probability of applying them within the enterprise.
AI is a very complicated matter. When you see statements made by industry leaders, you have to keep in mind that visionaries can introduce concepts and ideas that still have a long way ahead of them before they are productized into services and become available to enterprises. They need to be interpreted and communicated to end users—and such a feat isn't achieved in a day. Therefore, you should always separate hype from reality regarding AI.
Large action models: myths and facts
The first red flag regarding large action models is that whenever they are mentioned in content pieces available online, it's always about what they can do for industries across the globe—not what they have already done or are currently doing. There are no trackable metrics, large action model market to monitor, or widely known use cases.
While it's possible to connect the lack of information to the fact that LAMs are a nascent trend and are yet to make waves, it doesn't change the fact that decision-makers need to tread carefully, cutting through promising descriptions and focusing on facts.
For instance, there are several misguided assumptions about LAMs that executives need to keep in mind:
- Enterprises are adopting large action models already
The concept of a large action model was first introduced by Rabbit AI company, promoting its product—a device with an installed custom OS supporting a trainable AI assistant that uses LAM to execute user's requests (making reservations, giving directions, ordering services) and adapt to their specific prompts.
The product is currently in a pre-order phase, meaning there is a limited number of existing use cases and reviews to track. Nevertheless, it debunks the claim of industry-wide application of large action models and their dominance over large language models. - Large action models perform critical tasks
Following the previous debunked myth, this statement requires scrutiny. In theory, LAMs can execute complicated and important tasks, but theory isn't enough to convince the stakeholders—and it shouldn't be enough. Even the notion of LAMs performing simple assignments is being researched, so it's important to separate expectations and assumptions from reality.
It's important to remember that AI isn't just an application or a piece of software. It's also hardware. OpenAI is supported by a supercomputer with over 285,000 CPU cores, which allows it to interpret data and provide swift responses. And in time, it will need an upgrade because large AI models constantly grow in size. All this volume is needed for smooth large language model performance. Imagine the scale necessary to support truly complex and multi-level decision-making attributed to large action models.
Such a scenario is possible for industry titans like Amazon or Meta because they can afford it and create specific departments to maintain the system. But it's not something we'll be seeing in the near future
- Large action models will change the industry
Given that the first use case of a large action model is still pending and a user-oriented device, it's too early to speak about any industry-level benefits. The technology is yet to prove itself and demonstrate its advantages—and only through testing and feedback will its actual value and competitive differentiation be revealed. Artificial intelligence is a powerful game-changer that has already made a global impact, so it's important to focus on its realistic capabilities instead of claims that haven't been supported yet.
Are large action models real?
With all the myths debunked, a logical question follows: Do large action models exist? The answer is more complicated than a simple "yes" or "no."
While large action models aren't what they're described as by online sources, the functions attributed to them have actually been implemented for a while with the help of large language model agents.
Depending on the source, a large action model is either said to be the opposite of a large language model or its advanced option. None of these statements are correct. Large language models can be used to handle a sequence of tasks with the help of agents—software units powered by an LLM that handle specific prompting for improved decision-making and task execution.
LLM agents use large language models as computing power to solve complicated processes and problems.
As a result, it enables the use of large language models for tasks other than text generation and even the creation of autonomous systems. The concept of LAM may be an iteration of what LLM agents do, which is why it makes sense to break them down by components and explore their operation.
LAM vs LLM agents
Large language model agents provide an illustrative example of large language model evolution from passive systems to proactive respondents. The addition of agents was inspired by the discovery of a prompt engineering effect on the model’s knowledge and tone. By encoding a certain persona within a prompt, users could affect the model's ability to plan and express its identity. Such a development provided an interesting opportunity, which LLM agents further explored.
In simple terms, LLM agents are a combination of tools and large language model chains, imbued with the autonomy to decide what they should do or which tool they should use for accomplishing a goal. LLM agents are characterized by the following:
- Understanding context
With the help of the large language model AI, LLM agents can decipher the goals and context of the prompt without users having to enter additional prompts. As a result, they become more independent in responding to prompts and making decisions. - Using reasoning
LLM agents leverage multiple prompt engineering techniques (tree-of-thought, chain-of-thought) to make conclusions and establish logical connections for solving a task. Their reasoning isn't limited to text processing—they can also work with images and other content types. - Utilizing tools
LLM agents can be equipped with search engines, APIs, or other tools to gain data and complete a chain of actions that precedes task completion. This feature considerably expands and augments the decision-making potential of a large language model.
With these specifics outlined, it's important to understand what makes LLM agents self-governed. It's well-known that a large action model needs to be fed a new prompt—which means there has to be an intervention or a system that corrects and reviews the output and generates the next prompts.
In the LLM agent's case, this need is solved through creating a training loop within which the agent and a supervising agent interact and improve each other's behavior and responses. Accordingly, it enables the LLM agent to evolve towards smarter replies and actions, considerably enhancing the potential of a large language model.
Depending on their objectives, LLM agents can be divided into two large types: conversational agents and task-oriented agents.
- Conversational agents
Conversational agents are what make AI-powered chatbots work, presenting an identity, a tone, and a great understanding of context. Conversational LLM agents have enabled organizations and enterprises to use intelligent chatbots and helpers for considerable personalization and improved customer service, which allowed them to take a bulk of tasks off their teams' shoulders
- Task-oriented agents
These LLM agents are goal-driven and committed to accomplishing set objectives in the most efficient and fulfilling way possible. Task-oriented agents are skilled at taking a strategic approach to tasks, breaking them down into sub-tasks, analyzing similar tasks, and using a wide range of methodologies to complete an objective.
Regardless of the type, a large language model agent consists of several important elements:
LLM
A large language model serving as a computational engine for the agents.
Prompts
Instructions to agents are further divided into general prompts (guidelines about the role and objectives that never change) and specific prompts (tasks handled by agents under their role that change each time).
Memory
Consists of short-term memory (allows the agent to recall its previous steps) and long-term memory (provides months-long history of conversations and interactions necessary for the agent to gain contextual knowledge.
Knowledge
The fine-tuned LLM provides the necessary information or is extracted from a database.
Planning
A chain of thought built by an agent through reflecting on the plan critically and breaking the task into smaller sub-tasks.
Tools
Services, sources, APIs, and other features assigned to the agent to complete its task.
So, are large action models just another name for LLM agents? There is little point in denying the AI's ability to evolve and gain new, unique capabilities. Therefore, it makes no sense to ascertain that LAMs will never appear or be part of enterprise workflow.
However, there has been no documented unique differentiation between promoted LAMs and LLM agents, which indicates that the current appearance of the terms signals a very real and imminent trend — the commercialization of AI and the introduction of more custom solutions. The next stage of AI adoption foresaw the appearance of new AI projects trying to introduce unique concepts and beneficial features to everyday users and enterprises.
While it's certainly a challenging and ambitious endeavor due to AI's complexity and reliance on resources, this development will undoubtedly affect investors and stakeholders who are looking for new innovative leverage. With that in mind, it's crucial for decision-makers to always get to the essence of every new AI-related disruption and term, separating facts from assumptions.
Everything large action models are described to do, LLM agents have been doing already. The quality of their performance depends on the quality of the prompt they are given—and with a professional AI team, you can fine-tune it seamlessly, enabling more flexible, responsive, and secure large language models. Meanwhile, the concept of LAMs is vague and lacks a frame of reference.
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Will the elements of an action model appear in the future?
Although large action models are still yet to make an impactful appearance in the world of AI, the rapid adoption of artificial intelligence prompts visionaries and creative minds to look for new ways to use the technology and go beyond what current solutions offer.
Initiatives focused on creating autonomous AI systems dedicated to solving complicated problems and making decisions will certainly exist. But innovative thinkers will also have to keep in mind hard-to-remove constraints: the issues of hardware and energy consumption aren't going anywhere, which is guaranteed to make the advent of large action models slower than people expect.
The best way to avoid becoming overwhelmed by technological hype is to check whether you have explored everything about the innovation available right here. Ask yourself, "How do large language models work?" If you feel like there are some blind spots in your idea, then there is an opportunity area to tap into. Often, the benefits you seek can be delivered by the technology you are already familiar with.
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