Beyond Large Language Models: How Large Behavior Models Are Shaping the Future of AI

Artificial intelligence (AI) has come a long way, with large language models (LLMs) showing impressive capabilities in natural language processing. These models have changed the way we think about AI’s ability to understand and generate human language. Although they are excellent at recognizing patterns and synthesizing written knowledge, they have difficulty imitating the way humans learn and behave. As AI continues to evolve, we’re seeing a shift from models that simply process information to models that learn, adapt, and behave like humans.

Large Behavioral Models (LBMs) ​​are emerging as a new frontier in AI. These models go beyond language and focus on replicating the way people interact with the world. Unlike LLMs, which are primarily trained on static data sets, LBMs continuously learn through experience, allowing them to adapt and reason in dynamic, real-world situations. LBMs are shaping the future of AI by enabling machines to learn like humans do.

Why behavioral AI is important

LLMs have proven to be incredibly powerful, but their capabilities are inherently tied to their training data. They can only perform tasks that match the patterns they learned during training. Although they excel at static tasks, they struggle with them dynamic environments that require real-time decision-making or learning from experience.

Furthermore, LLMs are mainly focused on language processing. They cannot process non-linguistic information such as visual cues, physical sensations, or social interactions, all of which are essential for understanding and responding to the world. This gap becomes especially apparent in scenarios that require multimodal reasoning, such as interpreting complex visual or social contexts.

Humans, on the other hand, are lifelong learners. From childhood, we interact with our environment, experiment with new ideas and adapt to unforeseen circumstances. Human learning is unique in its adaptability and efficiency. Unlike machines, we don’t need to experience every possible scenario to make decisions. Instead, we extrapolate from past experience, combine sensory input, and predict outcomes.

Behavioral AI seeks to bridge these gaps by creating systems that not only process language data, but also learn and grow from interactions and can easily adapt to new environments, just as humans do. This approach shifts the paradigm from “what does the model know?” to “how does the model learn?”

What are major behavioral models?

Large Behavioral Models (LBMs) ​​are intended to go beyond simply replicating what people say. They focus on understanding why and how people behave the way they do. Unlike LLMs that rely on static data sets, LBMs learn in real time through continuous interaction with their environment. This active learning process helps them adjust their behavior, just as humans do: through trial, observation and adaptation. For example, a child learning to ride a bike doesn’t just read instructions or watch videos; they physically interact with the world, fall, adapt, and try again—a learning process that LBMs must emulate.

LBMs also go beyond text. They can process a wide range of data, including images, sounds and sensory input, allowing them to understand their environment more holistically. This ability to interpret and respond to complex, dynamic environments makes LBMs especially useful for applications that require adaptability and context awareness.

Key features of LBMs include:

1. Interactive learning: LBMs are trained to take action and receive feedback. This allows them to learn from the consequences rather than from static data sets.
2. Multimodal understanding: They process information from various sources, such as sights, sounds and physical interactions, to build a holistic understanding of the environment.
3. Adaptability: LBMs can update their knowledge and strategies in real time. This makes them very dynamic and suitable for unpredictable scenarios.

How LBMs learn like humans

LBMs facilitate human-like learning by integrating dynamic learning, multimodal contextual understanding, and the ability to generalize across domains.

1. Dynamic learning: People don’t just remember facts; we adapt to new situations. For example, a child learns to solve puzzles not only by memorizing the answers, but also by recognizing patterns and adapting their approach. LBMs aim to replicate this learning process by using feedback loops to refine knowledge as they interact with the world. Instead of learning from static data, they can adapt and improve their understanding as they experience new situations. For example, a robot powered by an LBM could learn to navigate a building by exploring, rather than relying on pre-loaded maps.
2. Multimodal contextual understanding: Unlike LLMs that are limited to processing text, humans seamlessly integrate images, sounds, touch, and emotions to understand the world in a deeply multidimensional way. LBMs aim to achieve similar multimodal contextual understanding, being able to not only understand spoken commands, but also recognize your gestures, tone of voice and facial expressions.
3. Generalization across domains: One of the hallmarks of human learning is the ability to apply knowledge across domains. For example, someone who learns to drive a car can quickly transfer that knowledge to driving a boat. One of the challenges with traditional AI is transferring knowledge between different domains. Although LLMs can generate text for different fields, such as law, medicine, or entertainment, they struggle to apply knowledge in different contexts. However, LBMs are designed to generalize knowledge across domains. For example, an LBM trained to help with household tasks can easily adapt to working in an industrial environment such as a warehouse, learning as they interact with the environment rather than needing to be retrained.

Real-World Applications of Large Behavioral Models

Although LBMs are still a relatively new field, their potential is already evident in practical applications. For example, a company called Lirio uses an LBM to analyze behavioral data and create personalized healthcare recommendations. By continuously learning from patient interactions, Lirio’s model adapts its approach to support better medication adherence and overall health outcomes. For example, it can identify patients who are likely to miss their medications and provide timely, motivating reminders to promote medication adherence.

In another innovative use case, Toyota has teamed up with MIT and Columbia Engineering to investigate this robotic learning with LBMs. Their “Diffusion Policy” approach allows robots to acquire new skills by observing human actions. This allows robots to perform complex tasks, such as handling different kitchen objects faster and more efficiently. Toyota plans to expand this capability to more than 1,000 different tasks by the end of 2024, demonstrating the versatility and adaptability of LBMs in dynamic, real-world environments.

Challenges and ethical considerations

Although LBMs are promising, they also pose a number of significant challenges and ethical concerns. A key point is to ensure that these models cannot mimic malicious behavior based on the data they are trained on. Because LBMs learn from interactions with the environment, there is a risk that they may unintentionally learn or replicate biases, stereotypes, or inappropriate actions.

Another major concern is privacy. The ability of LBMs to simulate human-like behavior, especially in personal or sensitive contexts, increases the possibility of manipulation or invasion of privacy. As these models become more integrated into everyday life, it will be crucial to ensure that they respect user autonomy and confidentiality.

These concerns highlight the urgent need for clear ethical guidelines and regulatory frameworks. Good oversight will help guide the development of LBMs in a responsible and transparent manner, ensuring that their deployment benefits society without compromising trust or fairness.

The bottom line

Large behavioral models (LBMs) ​​are taking AI in a new direction. Unlike traditional models, they don’t just process information; they learn, adapt and behave more like humans. This makes them useful in areas such as healthcare and robotics, where flexibility and context matter.

But there are challenges. LBMs can detect harmful behavior or violate privacy if not handled carefully. That is why clear rules and careful development are so important.

With the right approach, LBMs can transform the way machines interact with the world, making them smarter and more helpful than ever.