For years, human labor sustained the economy, until artificial intelligence and robotics began to claim factories, energy, and a place of their own alongside people
https://eladelantado.com/en/artificial-intelligence-robots/
Publish Date: 2026-03-01 12:30:00
Source Domain: eladelantado.com
Artificial intelligence (AI) is reaching more aspects of our lives, we see it everywhere. However, it’s no longer just about programs on computers or experiments in labs, but also used in real robots, fabrics, warehouses, and public spaces. This is exciting, but it also represents great challenges.
To make artificial intelligence work in robots, companies need new systems and technological infrastructure, including powerful computers, fast networks, and systems that can handle huge amounts of data from sensors, cameras, and robots moving in the real world. So, let’s find out more about this
Why artificial intelligence needs a new infrastructure
Physical AI is different to the traditional one. While models like large language models (LLMs) can learn from internet text, robots need context-specific data such as:
- Motion and position data.
- Photos and videos.
- Sensor readings like LiDAR.
This data must match the real actions and outcomes of the robots. Collecting it only in the real world is slow and expensive, which is why simulations—virtual environments that mimic real-world conditions—are crucial. Basically, simulations allow teams to test robots, create synthetic data, and try different scenarios faster than using only real robots. To make these simulations possible, companies must manage:
- Optimizing speed for massive numbers of tests rather than just fast results.
- Thousands of GPUs working together.
- Preparing 3D assets ready for simulation.
Hardware reliability is also critical because if one GPU fails, it can disrupt an entire training cycle. So, choosing the right cloud system for simulation requires balancing price, performance, and reliability.
Managing big data and low latency
Once robots are developed, they produce huge amounts of data, like:
- Sensor readings from LiDAR and motion.
- Video from cameras.
- Simulation outputs.
Not all this data is easy to use. Unlike clean datasets used to train traditional artificial intelligence, this…
