simulation and AI - Robotic Simulation Services

Combining simulation and AI technologies like Machine Learning & Deep Learning unveils outstanding new possibilities and opportunities. Moreover, the use of AI on traditional approaches to simulation may even bring forth a paradigm shift in the industry regarding how we perceive and develop the simulation.

Although simulation and Artificial Intelligence (AI) are two different technology paradigms, these technologies are related to each other in their primary forms. In computer engineering, simulation imitates an environment or a machine, while AI effectively simulates human intelligence.

While they may be related, simulation and AI were being used very differently with different mathematical and engineering approaches. However, in recent years, the development of AI-based simulations has experienced rapid growth in various industries.

For instance, now infamous, Cyberpunk 2077 used AI to simulate facial expressions and lip-syncing in the gaming industry. On the other hand, Microsoft Flight Simulator 2020 used AI to generate realistic terrains and air traffic.

The power of AI to enable rapid simulation development with faster, more optimized, and less resource-hungry simulations even on a large scale would empower more applications of simulation technology in far wider industries and platforms.

However, to understand the benefits of using AI in simulations and its development, we need to understand the traditional simulation development approach and its use in this scenario at first.

Traditional Simulation vs. AI-based Simulations

The basic idea behind simulation development is to gather data related to the machine, environment or anything for different inputs and conditions. These data would then be collected, analyzed, and studied to understand how the machine/environment/anything simulated functions and behaves under different conditions and situations.

This understanding would then be used to build a basic mathematical model that can govern and imitate the actual object in different conditions, then used to construct a simulation model that can replicate or simulate the real thing.

However, when AI is used to build these simulation models, the AI has to be fed with data related to the object/environment's behavior and how these subjects (object/environment to be simulated) function under different conditions and settings. During this process, the AI model requires relevant data that can be considered a sample of the simulation subject and represents the subject properly.

Generally, Neural Networks (NNs) would be used as the AI model to be trained. After the training, this would simulate the subject and its behavior.

Both approaches, either traditional approaches or AI for simulation, have their advantages and disadvantages. One of the significant advantages of the conventional simulation method is that the mathematical model defined after studying the simulation subject can be reused and reconstructed easily.

This allows other development teams to verify or reuse the same mathematical principles or models to generate the simulation. A traditional approach would also enable the developers to expand the simulation based on their understanding of the subject without explicit testing or proof test.

One of the significant disadvantages of this traditional approach remains to be its complex and resource-hungry process to generate the simulation. This is because everything has to be done by the simulation developers, who would also have to be experts in respective domains such that they need to understand the subject very closely.

Meanwhile, in AI-based simulation development, data is one of the essential components. The subject's information needs to be in abundant amounts and deterministic such that the data can represent the subject very closely.

This type of data may not be available readily when the data needs to be either collected or generated. But after the collection of accurate and abundant data, an AI-based/aided approach is very advantageous since there is no need to understand the subject by developers themselves.

Another significant advantage of the AI-based simulation holds within the power of AI to discover patterns or behavior in subjects not even considered or found by the developers. Apart from this, training an AI model usually takes a lot of time, but it may not be as resource hungry, complex, and costly as the traditional approach.

One of the significant disadvantages of the AI approach is that the model builder cannot be recognized or understood by developers in any way, so it cannot be usually reconstructed unless similar data or input is fed again to train the model.

Apart from this, due to the data required to qualify the model, expanding the model will generally be impossible without sufficient data.

Combining Simulations and AI

Using AI in simulation generation or development would enable data-powered development with rapid changeability and minimal human involvement. Although the simulation traits would be considered too complex for humans to develop, AI may easily reconstruct such characteristics if sufficient data is provided.

Due to this, AI can be used to simulate something too hard, complex, or time-consuming for humans in a short time without too much effort. Thus, not only would the development of simulations be faster, more productive, and easy, but AI would also enable the rapid iteration and tweaking of simulations that would be far less feasible, especially on a large scale.

We can open new doors by combining the power of AI and simulation for product design and development. Generally, without AI simulations, developers have to design a product/model that must be intensively tested before production, and changes are needed after the story. Then, the same process would have to be repeated.

This process is very resource-intensive. But through AI, design changes and validation can be easily tested through simulation, enabling rapid iteration and development.

The development and adoption of AI for simulation are far more required in industries like Augmented Reality (AR) and VR (Virtual Reality), where the sheer complexity of building high scale models, environments, and graphics through the traditional methods would be infeasible compared to using AI to develop and deploy simulations with its data-driven approach of development. The opportunities in AR and VR could be far more explored and matured through the AI to generate and develop simulations.

Alongside this, simulation of subjects like fluids (air and water) is brutal to construct with only a traditional approach, the result of which would still not be good and very close to reality. But with the help of AI, such simulations would be closer to reality and more refined.

One of the significant advantages of AI-based simulation compared to the traditional approach is that the conventional system would be significantly resourced heavy since it usually calculates each simulation particle.

However, AI-based simulation would enable such complex simulations easily since AI can perform these calculations/predictions much faster and less resource hungry. Alongside this, generative simulations like the generation of models, terrains in games, and product designs would also be possible with AI.

For instance, take the game Microsoft Flight Simulator 2020 as an example. This game allows gamers to experience realistic flights worldwide without lagging in the quality of models, terrains, and environment.

By traditional approach, this would mean that the game developers would have to model and build all terrains used in 3D along with matching landscapes and backgrounds to give the simulation a realistic feeling.

This would have cost the game developers a massive amount of time, resources, and a considerable number of experts to deal with complex problems lying ahead in such an enormous project. Realistically, such a project would not be feasible or even practically be possible to complete.

But through the use of AI, the developers used massive amounts of data that are already available and combined them with vast amounts of computation through the power of the cloud to train an AI model that could build realistic 3D models of terrains, environments, along with grasses, trees, and water-based upon the real world.

The results produced were pretty spectacular and received substantial critical acclaim from game developers and gamers alike.

Conclusion

By combining simulations and AI, we can unfold new opportunities and endless possibilities in different industries.

Along with technologies like Machine Learning and Deep Learning, AI-enabled simulations will be propelled by the data-driven backend. Conquering the disadvantages of the traditional approach to simulation, AI-based simulations will be able to push the boundaries of what simulations can do.

Even the most complex simulations, which would be next to impossible when developed with traditional methods, will be attainable by combining simulations and AI.

Moreover, with AI enabling rapid development of more optimized and improved quality, the industry may experience a revolution empowering next-level simulations with realism and details never seen before.