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In today’s world, it seems like we can’t do anything without encountering artificial intelligence (AI), and the businesses that want to stay ahead must adapt to new consumer expectations. In fact, the AI market has grown significantly since 2023, reaching 184 billion U.S. dollars in 2024 and offering an array of new opportunities for businesses across different sectors.
While there are various fields within the AI market, the adoption and advancement of large language models (LLMs) is one of the underlying technologies behind the overall rise of AI-based tools. That said, all eyes are on a new technology that can improve business processes even more—large action models.
Large action models (LAMs) are built on the foundation of large language models, providing actionable solutions and enabling decision-making, task execution, goal completion, and greater autonomy.
Since AI capabilities are constantly evolving, businesses must be ready to adopt new technologies, such as large action models, to stay ahead of the competition. Keep reading to learn how LAMs work, how they can be applied to different industries, and the major LAM tools that are currently available on the market.
What is a Large Action Model?
A large action model is an AI model that’s designed to understand human language, translate insights into action, and respond in real time. This concept gained significant traction after being introduced by the Rabbit AI company, showcasing how AI could go beyond text-based interactions and take direct actions in the digital and physical world.
A common myth about this new technology is that it’s already been adopted by many companies. In reality, LAMs are still in the early stages of development, with very few applications available.
That said, while still a new technology, it gained widespread attention for its ability to understand and execute tasks in a way that only humans could before. So, if you haven’t implemented this technology yet, don’t worry—now is the perfect time to start exploring its potential.
How does a Large Action Model Work?
So, how are LAMs able to perform tasks?
Large action models use neural networks and deep learning architectures to break down complex processes into manageable steps. They integrate with external systems to interact with the real world, requiring extensive training on large amounts of data.
Key stages of LAM development include designing large action model architecture, selecting the right training algorithms, and refining the model through supervised or reinforcement learning.
The goal is to enable the model to act autonomously in real-world environments, integrating moving parts like action recognition, decision-making frameworks, and feedback mechanisms to optimize performance over time.
As a result, the capabilities of large action models go way beyond other technologies, enabling them to translate suggestions into real-life actions.
Large Action Models vs. Large Language Models
There’s a common misconception that large action models and large language models are similar technologies. Though LAMs are built on the foundation of large language models, LAMs have key differences that set them apart.
While LLMs are a great solution for many applications, they are limited to generating responses to queries. This is where LAM’s capabilities really shine, as they don’t just understand the exact meaning of words but also the intent behind human language. These “agents” can then perform specific tasks without human intervention.
|
Feature |
Large Action Models |
Large Language Models |
|
Primary Function |
Autonomous decision-making and action execution |
Language understanding and text generation |
|
Focus |
Performing tasks in real-world environments |
Generating coherent and contextually relevant text |
|
Application |
Robotics, autonomous vehicles, real-time operations |
Chatbots, translation, text summarization, content creation |
|
Technology |
Combines neural networks with machine learning systems |
Primarily deep learning based on natural language processing |
|
Autonomy Level |
High autonomy with decision-making in dynamic environments |
Limited autonomy, requires human input to initiate actions |
|
Feedback Mechanism |
Continuously adjusts based on real-time feedback and outcomes |
Adjusts based on the context and previous prompts in conversations |
Example:
Let’s say a customer is talking with a chatbot to reset their password. If the chatbot has LLM capabilities, it will understand the question and generate a text-based response, providing the customer with information on how to reset their password. A large action model, on the other hand, would understand the request and take action by resetting the password for them.
Use Cases of Large Action Models
While large action model apps are still in the early stages of development, it’s clear that they can add a layer of efficiency to traditional AI business solutions. But what can they do for your specific business?
Due to LAMs’ advanced capabilities, they can enhance processes in a range of industries. Keep reading to better understand how they can be applied in the real world, where we’ll explore large action model examples in customer service, healthcare, manufacturing, finance, and retail and logistics.
LAMs in Customer Service
Similar to LLMs, large action models can automate time-consuming tasks, respond to queries, and provide personalized recommendations. However, LAMs have powerful features that outperform LLMs, offering businesses a more advanced solution for customer service.
By understanding the meaning behind what customers are saying, chatbots and AI business assistants that leverage large action models can increase their efficiency in problem solving, tailor responses more efficiently, and identify opportunities for improvement.
However, the feature that has the most potential in transforming everyday customer interactions is LAM’s ability to execute complex tasks without human intervention—a characteristic that other AI technologies have yet to offer.
This includes tasks like processing refunds, updating accounts, and troubleshooting technical problems.
LAMs in Healthcare
An increasing number of healthcare institutions are leveraging artificial intelligence in software development to process large amounts of health information, generate insights, and support clinical decision-making.
While these AI capabilities are important, large action models can reduce the workload even more by turning these insights into actions. This includes time-consuming administrative tasks, such as scheduling appointments, updating medical records, placing orders, managing billing, and more.
LAMs can also interact directly with physical systems, such as medical equipment or robotic surgical assistants, ensuring precise execution of tasks. For instance, in surgeries, LAMs can provide real-time guidance by adjusting robotic tools, enhancing precision, and improving outcomes—something LLMs can’t achieve.
By automating these tasks, healthcare facilities can alleviate staff burdens and improve overall operational effectiveness. This is especially important in today’s world, as one study predicts that there will be a national healthcare worker shortage of more than 4 million workers by 2026.
LAMs in Manufacturing
In manufacturing, having a system that goes beyond text generation is essential for advanced processes. Unlike LLMs, large action models can adjust machine settings, control production lines, and enable fully autonomous operations.
One of its biggest advantages, however, is its ability to create closed-loop systems. How would this look in the real world?
Manufacturers can use LAMs to:
- Monitor production equipment.
- Detect issues in real time.
- Make adjustments or initiate repairs.
This differs from many existing AI technologies, which primarily monitor equipment and alert workers of potential issues but rely on human intervention to solve them. That said, LAM adoption can significantly enhance production processes and reduce downtime.
LAMs in Finance
There are multiple ways that large action models can be leveraged in finance, ranging from customer service to internal banking operations.
In banking, LAMs can carry out multiple tasks for consumers that other AI technologies cannot currently perform. This includes automating tasks like opening an account, processing loans, and customer verifications.
In addition to reducing the workload within financial institutions, LAMs can improve security by identifying vulnerabilities and helping solve potential issues. Over time, they may also be utilized to enhance revenue-generating processes, such as integrating into workflows that assist customers in purchasing a home.
Even more, LAMs have the potential to automate trading decisions by analyzing vast amounts of market data, predicting market movements, and making real-time buy or sell actions based on strategies. Since security is a huge factor in this sector, custom AI solutions are crucial for ensuring safe operations throughout these processes.
LAMs in Retail and Logistics
LAMs’ ability to make data-driven decisions offers various benefits to the retail and logistics sectors.
To start, large action models AI can offer hyper-personalized customer shopping experiences. While LLMs and LAMs can both provide recommendations and improved customer service, large action models can perform tasks that LLMs can’t, such as sending targeted promotions and managing customer interactions across multiple channels.
LAMs can also analyze data in real time, such as customer demand, competitor prices, and stock levels, to automatically adjust prices for products, resulting in optimized revenue and profitability.
In logistics, LAMs can initiate adjustments to logistic routes for faster delivery times, therefore reducing delays and enhancing customer satisfaction.
Market Examples of Large Action Models
Here are 3 of the major LAMs that are currently available on the market:
xLAM by Salesforce
xLAM is a family of large action models, designed for function calling, reasoning, planning, and streamlining the integration of AI into workflows. Intended to perform difficult activities, Salesforce is confident about the capabilities of xLAM, claiming that it outperforms major models, including OpenAI’s GPT-4 and Claude-3-Opus, even though it’s smaller and more cost-effective.
One of its advantages is that it’s not limited to a single agent. Rather, xLAM can be used to power the decision-making and actions of many collaborative agents. This solution
maintains consistency across different data sources.
One customer states, “The models Salesforce is delivering for its Agentforce platform are what give us confidence that we’ll have the capabilities we need to roll out strong and cost-effective autonomous AI capabilities over time. Salesforce is truly paving the way for the AI Agent revolution.”
Rabbit R1 by Jesse Lyu Cheng
Rabbit R1, which was released in January 2024, is an AI-powered handheld device that can be described as a universal controller for apps. It was designed to perform distinct tasks all through a single interface by learning user behavior and automating tasks.
Rabbit R1 claimed to have been trained on more than 800 applications, using perplexity AI to get information from the internet and deliver fast response times.
One of its most innovative features is a dedicated training mode, allowing users to teach the system how to perform custom tasks. Once trained, Rabbit R1 can repeat these tasks on its own, significantly improving efficiency for both individual users and businesses.
Autodroid by arXiv
Autodroid is a mobile task automation system that can handle tasks on any Android application without human intervention.
Leveraging the capabilities of LLMs to generate instructions, the system produces a sequence of UI actions that can be seamlessly executed on a smartphone. It works by exploring UI relations to obtain app-specific knowledge, allowing it to adapt to different interfaces.
This capability enables Autodroid to automate complex workflows, such as filling out forms, navigating between screens, and triggering app functionalities without manual input. Overall, its ability to integrate with any Android application makes it a versatile tool in the realm of mobile automation.
Challenges Associated with Large Action Models
While large action models present various opportunities in the business landscape, there are certain challenges and concerns that must be considered.
One of the key concerns is the potential for bias and unfair decision-making, which is directly related to the data used to train these models. Since this technology relies on large datasets, ensuring that the data is unbiased, secure, and aligned with human values is crucial.
For the same reason, industries that handle sensitive information, such as healthcare and legal fields, must be especially careful when implementing large action models. While automated decision-making processes can streamline many tasks, this technology must be carefully implemented to address these ethical concerns and build trusting relationships with clients.
In addition, training and deploying large action models require substantial computational resources. The vast amount of data needed for training, along with the infrastructure required, can lead to high costs and longer development times. As a result, the adoption of LAMs may be out of reach for smaller businesses with fewer resources.
For the above reasons, partnering with an experienced development company is key, as they ensure LAM solutions are implemented securely and effectively.
How to Navigate the Future of Large Action Models in Business?
Large action models AI can perform tasks without human intervention—a skill that other technologies have yet to offer. While it’s still a new technology that requires additional research and development, it shows great potential to transform the way businesses handle everyday operations.
With time, we expect that AI development companies will continue to invest in LAM capabilities, offering custom solutions that help businesses automate tasks and save time and money.
“Large action models are a very exiting research area, but in my opinion it’s still in its early stages as we’ve seen with the Rabbit R1. Performing well on new unseen applications or Interfaces that look different from the data available during training is a big challenge, but I’m eager to see how the technology evolves and gets better at understanding new UI’s with less and less supervision or example workflows.”
– Felipe Riquelme, Machine Learning Engineer at Scopic
Our Expertise in AI Development
At Scopic, we understand the importance of leveraging AI to increase app performance and enhance user satisfaction. For that reason, we offer a variety of AI development services that help businesses address industry-specific challenges and stand out from the competition.
To get an idea of how we leverage artificial intelligence in web development and software solutions, check out some of our favorite projects:
OrthoSelect
OrthoSelect uses advanced technology to help orthodontists achieve greater clinical efficiency. To develop a desktop application capable of generating precise 3D models of patients’ teeth, the founders of OrthoSelect partnered with Scopic, who implemented an AI-driven approach and utilized deep learning to segment teeth accurately. The result was DIBS AI, an integrated software and hardware solution that provides automated case setups through advanced bracket-positioning software. This system improves accuracy and enhances the customer experience while reducing overall treatment time.
Codeaid
Codeaid saw the potential in using AI to transform the talent acquisition sector. With a goal of streamlining the hiring process while ensuring high-quality candidate assessment, Codeaid partnered with Scopic to develop the AI Interviewer tool, an innovative AI-driven solution designed to transform the recruitment landscape. This tool has a range of features, including the use of AI to generate skill-based questions, gain deeper insights on CVs, evaluate results in real-time, and essentially, improve the entire recruitment process.
Abby Connect
Abby Connect faced the challenge of managing a high volume of customer interactions daily. With Scopic’s insight, they quickly understood the importance of implementing AI to enhance solutions and keep up with today’s market. Scopic’s team built a web portal and mobile app, leveraging some of the most advanced AI technologies to optimize customer service management. As a result, Abyy Connect now provides fast, accurate call scripts, instant call summaries, and caller sentiment analysis—all of which wouldn’t be possible without the use of artificial intelligence.
Chroma Coloring Book
Chroma Coloring Book is an adult coloring mobile app that was developed by Scopic. This application features a unique AI-powered generative art function, which transforms user-submitted text prompts into personalized and complex images, offering a tailored coloring experience that reflects the users’ imagination and emotional state. The introduction of generative art functionality resulted in enhanced user satisfaction, longer session times, and increased downloads.
Conclusion and Key Takeaways
Large action model apps have the potential to transform business operations, offering capabilities that surpass current solutions. As this new technology continues to evolve, we’re excited to see just how it will be adopted by different industries.
At Scopic, we believe in the power of AI, which is why we offer a range of solutions that include machine learning development, large language models, deep learning solutions, and more.
For a free quote on your next project, contact us today.
FAQs
Are large action models real?
Yes, large action models are real and emerging as a promising area of AI technology. They focus on automating decision-making and performing actions without human intervention. This differs from traditional AI models, which primarily handle data analysis or language processing.
What are some of the myths that surround large action models?
One myth is that LAMs are simply enhanced versions of large language models. However, LAMs go way beyond text generation, performing real-world actions that automate complex tasks.
Another myth is that they can replace all human decision-making, while in reality, they still require refinement to handle complex, unpredictable situations.
What are the types of actionable AI samples that involve large action models?
Examples include LAMs used in retail for dynamic pricing, in finance for automating trades, and in logistics for optimizing delivery routes. These models handle real-time decision-making, helping businesses streamline operations and improve efficiency.
How do large action models combine language understanding with autonomy?
LAMs use natural language processing to interpret user commands and autonomously translate them into actionable steps, like adjusting inventory or assisting in transactions. This allows for seamless integration between human language input and automated decision-making.
What are the main features and capabilities of large action models?
Key features of LAMs include autonomy, allowing them to act without human input, and adaptability, enabling them to learn and adjust based on new data. They also excel at real-time integration with multiple platforms, making them highly versatile in executing complex tasks.
About Large Action Models Guide
This guide was authored by Baily Ramsey, and reviewed by Enedia Oshafi, Director of Business Development at Scopic.
Scopic provides quality and informative content, powered by our deep-rooted expertise in software development. Our team of content writers and experts have great knowledge in the latest software technologies, allowing them to break down even the most complex topics in the field. They also know how to tackle topics from a wide range of industries, capture their essence, and deliver valuable content across all digital platforms.
