From LLM to LAM: how automation is evolving

In recent years, the evolution of artificial intelligence has undergone an unprecedented acceleration. From the first systems based on voice recognition to the most advanced Large Language Models (LLM) designed to understand and generate natural language, we now stand at the dawn of a new revolution: the rise of Large Action Models (LAM).

Beyond representing a significant technological leap, this transition should also be viewed as a true paradigm shift in how we interact with technology, especially in the context of smart homes and the Internet of Things (IoT).

To fully understand the potential of LAMs, let’s take a step back and start with the definitions.

Large Language Models are AI models designed to comprehend and generate natural language text. In practice, they can write content, answer questions, translate languages, summarize documents and much more. Despite their complexity, they remain confined to the linguistic domain: they can speak but do not act.

They are excellent tools for interaction and ideal for providing responses or assistance, but they lack a direct connection to the physical environment.

Large Action Models can be considered the natural evolution of LLMs. These systems combine the linguistic intelligence of traditional models with the ability to perform actions and orchestrate external tools or automated tasks.

In essence, LAMs mark the shift from understanding and processing to action, opening up new and fascinating possibilities for human-machine interaction.

Today’s smart homes largely rely on static automation. Users define rules through apps based on schedules, environmental conditions, routines, or sensor input.

With the advent of LAMs, home automation can evolve into a more dynamic and adaptive system. These models would be capable of learning user behaviors and preferences, distinguishing between explicit commands and implicit needs and adjusting to real-time environmental, behavioral, or even emotional changes.

Imagine, for instance, a smart home that adjusts the temperature not only in response to a voice command but also by analyzing the number of people in the house, their level of physical activity and the time of day. The result would be an optimal, context-aware level of comfort, greater energy efficiency and a more natural, seamless interaction between human beings and technology.

Despite their revolutionary potential, Large Action Models (LAM) still face several technical, design and cultural challenges that hinder widespread adoption.

One of the most pressing concerns is the risk of over-automation. A system with too much autonomy and without a proper balance between automation and human control may misinterpret the user’s true intentions. For example, it might switch off the lights in a room because it detects no activity, ignoring that someone is quietly reading without moving. In such cases, the system’s intervention is unwelcome and may even become irritating, undermining the user’s trust in the technology.

Another major obstacle is the management of multiple or conflicting preferences, which is a common scenario in shared living spaces. It's easy to imagine a home where one person prefers a warmer temperature, another enjoys dim lighting and someone else wants background music to relax. Processing and mediating these unspoken preferences requires a level of contextual intelligence and automated negotiation that current systems still struggle to achieve.

Added to this are concerns about privacy, consent and transparency in automated decision-making, which raise ongoing ethical and regulatory debates.

In short, LAMs are at the edge of innovation, where technological promise must be reconciled with the realities of user experience, inclusive design and social interaction. The challenge is not just to make them work, but to make them work well for everyone.

Although Large Action Models are not yet a fully mature technology, they represent a fast-evolving field where multiple branches of AI converge. Their development depends on the integration of advanced language models capable of interpreting human speech with increasing precision, multimodal perception technologies that combine visual, auditory and contextual input and intelligent sensors that collect real-time information about the surrounding environment. To all this, we must add the role of autonomous agents designed to make decisions and act without direct human input.

At the same time, several enabling technologies are emerging that lay the groundwork for the adoption of increasingly sophisticated and context-aware automation systems. Among these are interoperable standards such as Matter, which was created to ensure seamless communication between devices from different manufacturers. Localization technologies like Ultra-Wideband (UWB) make it possible to track the position of people and objects indoors with high accuracy, making automation more responsive and personalized. Finally, the growing presence of AI on-edge - that is, artificial intelligence processed locally on devices without relying on the cloud - ensures faster response times, stronger data privacy and operational independence even without an internet connection.

In conclusion, the shift from Large Language Models to Large Action Models marks a natural evolution of artificial intelligence toward more intuitive, proactive and context-sensitive interaction.

Shortly, smart homes will not only understand voice commands but also be able to interpret, decide and act in real time, adapting to our needs with a level of personalization and efficiency never seen before.