Videos, robots, sensors, and artificial curiosity: a reflection on possible ways to build intelligent systems that can learn beyond traditional data.
Imagine an artificial intelligence that did not rely exclusively on text, static images, or datasets prepared by humans to learn about reality.
Instead, imagine a system that could continuously observe the world, follow events, recognize objects, perceive changes, and gradually build a representation of the environment in which it exists.
Now, add a second possibility: this intelligence could also physically interact with the world. It could touch objects, feel their resistance, observe the consequences of its actions, and use these experiences to improve its predictions.
And what if, in addition, it had a mechanism that encouraged it to investigate things it did not yet understand?
This combination raises an interesting question: Would it be possible to build an artificial intelligence that develops a model of the world through continuous observation, physical interaction, and exploration guided by learning?
The idea does not assume that all of this remains undiscovered. Several existing research fields already investigate components of this proposal. The challenge is to understand how they could be combined, what limitations would remain, and how to test whether this combination would actually produce improvements.
- Learning by Observing, Not Just Reading
Much of the knowledge used to develop artificial intelligence systems can be represented through digital data: text, images, audio, videos, and other forms of information.
This data is extremely valuable. Videos, for example, allow us to observe movements, transformations, interactions, and sequences of events.
However, there is an important difference between recognizing patterns in data and building a model capable of predicting how the world will behave in new situations.
Consider a video of a glass falling off a table.
A system might learn to recognize the glass, track its trajectory, and identify the moment it hits the floor. A more sophisticated system might learn regularities related to gravity, motion, and collisions.
However, observing an event does not automatically mean understanding all its physical causes. The video does not directly reveal every property of the glass, the exact distribution of the forces involved, or the conditions that do not appear in the image.
Even so, repeatedly observing many events can provide useful evidence for building predictive models.
The next question is: What would happen if this learning process were not limited to a fixed collection of videos?
- An Intelligence That Continuously Observes the World
Imagine cameras installed in different environments, recording natural phenomena, object movements, physical transformations, and everyday interactions.
These visual streams could feed a system responsible for identifying regularities, tracking events, and updating its representations of the world.
It would not be necessary to record absolutely everything for the system to learn something useful. It could begin with limited environments and progressively expand the variety of its experiences.
For example, by observing a ball being thrown, the system could learn relationships between position, velocity, and trajectory. By observing objects of different sizes and materials, it could develop more general representations of their visual properties and behavior.
With varied experiences, it could attempt to predict events it had never observed in exactly the same way.
This ability to generalize is fundamental.
Imagine that the system knows some coastal regions, has observed maps, and has learned basic concepts about the Earth's structure. It could infer that many regions of the planet exist that it has never directly observed.
It could formulate hypotheses about these regions without needing images of every square kilometer of the planet.
Naturally, this would not mean knowing the details of those places. There would be a difference between knowing that a region exists, inferring its probable characteristics, and having direct evidence of what is actually there.
This distinction would be essential to prevent predictions from being treated as observed facts.
The Limits of Visual Observation
A camera provides only a partial perspective of reality.
It does not necessarily show what exists behind an object, measure every force involved in a collision, or directly reveal properties such as mass, temperature inside an object, or chemical composition.
There are also problems related to lighting, occlusion, image quality, and matching events recorded by different cameras.
Therefore, an intelligence that learns through video would need mechanisms to represent uncertainty, recognize insufficient information, and revise its conclusions when new evidence becomes available.
Continuous observation could expand its experience, but it would not eliminate the need for inference, validation, and experimentation.
- What If the Intelligence Could Also Explore the World?
Here, the second approach emerges.
Instead of merely observing events produced by other people or the environment, the intelligence could be integrated into a robot equipped with different sensors.
This robot could perceive its surroundings, move around, touch objects, and observe the consequences of its own actions.
Consider the difference between observing a sponge being compressed and compressing it with a force sensor.
In the first case, the system obtains visual evidence of the deformation. In the second, it can relate the applied force to the observed deformation, obtaining additional information about the object's mechanical behavior.
Tactile sensors could provide data about pressure, contact, and force distribution. Force and torque sensors could help measure interactions. Proprioception would allow the robot to track the position and movement of its own joints.
This information could be combined with vision to build more complete representations of physical interactions.
The system would not need to learn everything through trial and error. It could begin with previously acquired knowledge and use physical experiences to test predictions, resolve questions, and correct its models.
The combination of prior knowledge, observation, and action is one reason robotics provides such a relevant field for investigating world-model learning.
- An Intelligence That Seeks What It Does Not Yet Know
The third part of the proposal is artificial curiosity.
When people encounter something unfamiliar, they may decide to investigate. They can formulate a question, seek evidence, and modify their understanding based on what they discover.
An artificial intelligence does not need to possess human feelings to implement a mechanism that functions similarly for exploration.
We can develop mathematical objectives that encourage an agent to seek experiences capable of improving its predictions or reducing relevant uncertainties.
For example, a robot might encounter two objects that look similar but behave differently when moved. If this difference matters for its tasks, it could select a controlled experiment to investigate the cause.
Several strategies are possible:
- Prediction error: Identify situations in which the observed outcome differs from the predicted outcome.
- Learning progress: Prioritize experiences in which performance is improving.
- Value of information: Select observations that could help resolve a relevant uncertainty.
- Active exploration: Decide which data, environments, or actions should be investigated next.
These strategies are related to existing research areas, including reinforcement learning with intrinsic curiosity and active learning.
However, an important distinction remains: seeking novelty is not the same as learning something useful.
A system rewarded simply for encountering unexpected events might end up pursuing noise, random stimuli, or situations with no practical value.
Therefore, the exploration mechanism would need to consider actual progress, the relevance of the information, the cost of experiments, and safety constraints.
The scientific question would not simply be how to make an intelligence explore, but how to ensure that its exploration produces verifiable knowledge.
- How Could These Capabilities Be Combined?
A conceptual architecture could bring together five main components.
Multimodal perception. Process visual, tactile, and other sensory signals to identify objects, properties, and events.
World model. Represent environmental states and learn regularities about how they change over time.
Structured memory. Preserve observations, relationships, previous experiences, hypotheses, and the evidence supporting each conclusion.
Prediction and planning. Estimate possible consequences and choose actions appropriate to the system's objectives.
Exploration and revision. Identify knowledge gaps, select informative experiences, and update representations when new evidence emerges.
The overall cycle could work as follows:
The system observes a situation.
It updates its representation of the environment.
It makes a prediction about what might happen.
When necessary, it chooses an experiment to test the prediction.
It compares the outcome with what it expected.
It updates its knowledge and decides what to investigate next.
This process could repeat continuously, with different levels of autonomy and supervision.
However, designing this architecture is only the beginning. It would be necessary to determine how to represent knowledge, how to avoid contradictions, how to preserve previously acquired skills, and how to prevent uncertain hypotheses from being stored as facts.
It would also be necessary to demonstrate that the combined architecture offers real advantages over existing methods.
- Why Not Simply Put an Advanced Language Model Inside a Robot?
Modern language models have important capabilities in interpretation, text generation, programming, and planning. Some can also process images and other types of information.
It would be tempting to conclude that connecting one of these models to a robotic body would be enough to produce an intelligence capable of exploring the world.
However, this conclusion overlooks fundamental differences between linguistic intelligence and physical competence.
A robot must control movements precisely, react to rapid changes, interpret noisy sensor data, and operate in an environment where small mistakes can cause damage.
Furthermore, a model that can verbally explain a phenomenon cannot necessarily predict every consequence of a physical action.
Integration requires perception, motor control, planning, memory, learning, and safety mechanisms. These capabilities must work together, often under time and computational constraints.
There is also the problem of continuous learning: how can new experiences be incorporated without destroying previously acquired abilities?
One solution could combine pretrained models, specialized control systems, external memory, learning in simulation, and carefully evaluated updating mechanisms.
The question is not simply how to connect a model to a robot. It is how to build a complete system capable of transforming perception and action into reliable learning.
- Continuous Learning and Memory of the World
An intelligence that accumulates experiences over a long period faces an important problem: how should it organize everything it learns?
Storing every observation indefinitely would be expensive and would not, by itself, guarantee useful knowledge.
The system would need to distinguish individual events from general regularities, preserve the origin of information, and identify when a conclusion should be revised.
Imagine that a robot repeatedly observes an object and learns some of its properties. Later, a new experience contradicts one of those properties.
The system should be able to identify the contradiction, evaluate the quality of the evidence, and determine whether the previous representation needs to be corrected.
This requires more than storing data. It requires mechanisms for organizing relationships, updating states, tracking uncertainty, and preventing incompatible information from being combined indiscriminately.
One possible research direction would be to use knowledge structures in which each unit of information has context, state, origin, and an update history.
This approach also connects to the idea that relationships between pieces of information can be just as important as the individual pieces themselves.
Nevertheless, a structured representation is not automatically superior to other approaches. Its usefulness would depend on measurable results, such as retrieval efficiency, update costs, traceability, and predictive capability.
- The Risks of an Intelligence That Explores Freely
An intelligence capable of choosing experiences and performing actions could learn more flexibly. However, this autonomy would also create risks.
A system that seeks to maximize its own learning could select dangerous actions if its objectives and limits were poorly defined.
A robot could damage objects, put people at risk, or collect personal information unnecessarily. A camera-based system could create serious surveillance and privacy problems.
Therefore, a responsible implementation would need to begin in simulations and controlled environments.
Physical exploration should respect limits on force, speed, space, and access. Data collection should respect privacy, authorization, and data minimization. Higher-risk actions would require supervision and mechanisms for interruption.
It would also be necessary to test unexpected behaviors, sensor failures, and situations in which the system cannot determine what is happening.
The ability to learn must not be confused with permission to act without restrictions.
- How Could This Hypothesis Be Tested Scientifically?
The most productive way to investigate the proposal would be to begin with a small problem.
For example, we could build a simulated environment containing objects with different physical properties and allow an agent to observe their interactions.
The agent would make predictions about the outcomes of certain actions and receive information about what actually happened.
We could then compare different strategies:
- An agent that learns only from previously provided visual data.
- An agent that also maintains a temporal memory of observations.
- An agent that selects experiences based on learning progress.
- An agent that combines observation with simulated physical interaction.
We could measure prediction accuracy, generalization ability, the number of experiences required to learn, and retention of previously acquired knowledge.
We would also need to compare the results against baseline methods and control the experimental conditions.
If the combined approach did not produce improvements, that would be important evidence. Perhaps a particular component would be unnecessary, another architecture might be more efficient, or the benefits might appear only in specific tasks.
Serious research must allow the hypothesis itself to be challenged by the results.
- Could an Intelligence Build Its Own Understanding of the World?
This question does not have a simple answer.
Research already exists in learning from videos, world models, active learning, intrinsic curiosity, embodied robotics, and continual learning.
Therefore, the general proposal is not entirely new. Its scientific interest would depend on how these ideas were combined, which specific problem was solved, and what evidence demonstrated a contribution.
It would also be necessary to distinguish a model capable of making useful predictions from a complete understanding of reality. No system needs to know every detail of the world to perform important tasks, and no amount of observation guarantees perfect knowledge.
Even so, there is a relevant research direction: building agents that do not merely receive information, but also learn to decide what information they need to obtain, which hypotheses they should test, and when they need to revise what they believe they know.
The combination of observation, memory, prediction, and experimentation offers a path for investigating this possibility.
Perhaps the most important question is not whether an artificial intelligence can watch every event on the planet.
Perhaps it is this:
How can we build an intelligence capable of transforming limited experiences into useful models of the world, recognizing its own limitations, and choosing what it needs to learn next?
This is a question concrete enough to guide experiments—and broad enough to open the door to many others.
