Artificial intelligence (AI) has become an integral part of our daily lives. While generative AI, like ChatGPT, creates content from textual data, another type of AI, embodied AI, is making waves. But what is it precisely? And how does it differ from other forms of artificial intelligence?
Embodied AI: intelligence responsive to the real world
Embodied AI refers to systems capable of interacting with their physical environment in real time, using sensors and machine learning algorithms. Unlike generative AI, which generates insights from data, embodied AI reacts to real-world data, allowing it to perform concrete actions, such as moving, avoiding obstacles, or adjusting its behavior accordingly. external conditions.
Take the example of autonomous drones or driverless cars. These technologies use sensors and algorithms to analyze their environment and make instant decisions without human intervention. The “embodied” aspect of this AI refers to how these systems perceive and react to what they see, hear or measure.
How does embodied AI work?
One of the essential elements of embodied AI is its model of the worlda digital representation of the environment in which AI evolves. This model allows the AI to make decisions based on the information it collects. For example, a drone can create a virtual map of its environment using its sensors and thus navigate without hitting obstacles.
Sensors are at the heart of this technology: cameras, radars, heat detectors, GPS, etc. These sensors provide crucial data that is then processed by machine learning and computer vision algorithms, allowing the AI to interact with the real world. Embodied AI stands out for its ability to interpret information that often escapes the human eye, such as infrared rays or GPS signals.
In terms of learning, two main approaches are used: reinforcement learning et active inference. Reinforcement learning works on a simple principle: AI learns by experimenting, adjusting its behavior to maximize rewards. For example, an autonomous vehicle learns to optimize its route by avoiding traffic jams or reacting quickly to dangers. Active inference, on the other hand, is an ongoing process where AI constantly adjusts its model of the world based on new data it collects, much like the human brain updating its understanding of the environment.
Applications in daily life
Embodied AI is already present in many fields. One of the most obvious examples is that of autonomous vehicles. These cars, equipped with sophisticated sensors and algorithms, can detect obstacles, adjust their trajectory and even react to unforeseen situations, such as a pedestrian suddenly crossing. In addition to reducing the risk of accidents, this technology promises to transform the way we think about mobility.
Embodied AI is also used in domestic roboticsespecially with devices like robotic vacuum cleaners and lawn mowers. These robots are able to move in complex environments and navigate while avoiding obstacles, while adapting to different configurations of rooms or gardens.
THE autonomous drones are another striking example. These intelligent machines, used for surveillance, delivery or inspection missions, navigate airspace without human intervention, adapting to weather conditions and obstacles in real time.
Constantly evolving technology
The history of embodied AI dates back to the 1950s, with early experiments like cybernetic turtle by William Gray Walter. Although rudimentary, this machine was already capable of moving by reacting to its environment. Since then, embodied AI has evolved at a rapid pace, thanks in part to advances in sensors and algorithms.
Today, this technology continues to evolve. Unlike cognitive AI, which learns from large abstract databases, embodied AI learns directly from its interaction with the physical world. This makes this technology particularly powerful, as it allows autonomous systems to adapt in real time to often unpredictable environments.
Challenges and prospects
Despite its advantages, embodied AI faces several challenges. Sensor accuracy is a major issue, because the reliability of the information collected is essential for making safe decisions. Environmental conditions, such as rain or fog, can alter the quality of data captured by sensors.
THE ethical issues are also important. For example, how should a self-driving car react in an emergency, when the life of a pedestrian or passenger is at stake? Researchers are working to make these systems as reliable and transparent as possible to avoid bias and ensure the security of their decisions.
Finally, thewidespread adoption of embodied AI requires overcoming technical and regulatory obstacles. Autonomous vehicles, for example, must be rigorously tested and validated before being integrated onto the roads.
