In the development of artificial intelligence, the availability of training data of ImageNet leads to a dramatic shift in 2012, particularly with the first successful vision Artificial Intelligence models. First time in history, researchers have succeeded in creating models that recognize objects with the accuracy of a human being using machine learning (ML). Scientists also have discussed Artificial Intelligence and pointed the question that “are we inside Artificial Intelligence and how it is originated?”
While ImageNet is leading the current revolution of Artificial Intelligence, there are many areas where researchers do not have data available enough to create models of high quality. The reason is the required data is either too expensive, not accessible or doesn’t exist. For example, limiting the production of self-driving cars is the lack of data across possible driving conditions. The information about accidents and misses also needs to be recorded. It is impossible to create traffic accidents to get data for self-driving AI models. But what we can do is develop simulations. They can help us get whatever data we want.
Unity Simulation is available on Google Cloud, and there are many reasons to get excited about it. Using the tremendous scalability of Google Cloud and real-time 3D technology, the simulation combines unity’s experience for creating visually rich simulations. Vice President at Machine Learning and Artificial Intelligence, Unity Technologies, Danny Lange, says that Unity Simulation can enable users to run multiple Unity projects at an unbelievable scale.
Types of Simulations
We have been using computers for simulation ever since their invention. In the very beginning, researchers were using some of the computers for the simulation of complex physical phenomena—their main uses are for the Simulation Creation varying from ballistics to weather. Some of the largest supercomputers are in use even today to predict climate change simulations to find new chemical compounds.
Two main types of Simulations are recognized. The first one being High Precision Scientific (or engineering) Simulation, and another is Large Scale Probabilistic Simulation.
Simulations that deal with Science and Engineering should be exact and accurate. To understand the weather’s working, one needs to model fluid dynamics equations that deal with density and temperature. He should also have a model of the world with very high resolution. If one builds a new turbine fan, one needs to understand its physical properties with excellent detail. Thermal conductivity and elasticity play an essential role when fans rotate at the speed of more than a thousand revolutions per minute and operate at a temperature of more than a thousand-degree Fahrenheit. It’s critical to understand microscopic and quantum effects for studying a particular physical system.
We need to explore many options and have rougher models of reality when it comes to Probabilistic Simulations. Many of the simulations originate from Monte Carlo methods (popularized by Ulam). Here, we try to arrive at a solution or a range of approximate solutions by going through slight variations in a simulation scenario. Suppose we are attempting to guess whether a particular city grid can see a lot of traffic. We can simulate thousands of random, different techniques with various vehicles, speeds, and destinations in Simulation Creationism. The higher-level properties of the simulated world matter much more than the physical characteristics of the car engines.
Scientific Simulations usually require moderate amounts of exact data. And they are adamant about building and scale. There may be instances where they may require expensive and dedicated supercomputers. On the other hand, Large scale probabilistic simulation is inherently parallel. Researchers can scale up and build this type of simulation effectively on the cloud. Because of all this, these simulations can be a perfect data source for creating the training sets required for AI Models. They play an essential role in enabling end-to-end testing for AI systems. When we talk about simulation, some scientists believe for our existence that we may be in a computed simulated universe created by some terrestrial being.
Simulation use cases
The use of simulation goes way beyond self-driving cars. It is a new standard for the creation of Artificial Intelligence. We can solve many problems on the platform where we get many examples of how human beings do it. It is possible to simulate the real environment with a high degree of accuracy.
In the Robotics space lies some of the fascinating problems that simulations can solve. In principle, it’s easier for us to build the factory’s twin digitally and rain or program Robots in virtual space safely rather than creating them physically in a real environment. This type of simulation is useful, especially in situations where Robots need to communicate safely with humans workers or where there is a large parameter space, such as thousands of grippers for the items which maybe are in a retail warehouse. In such situations, it’s not easy to program the robots for many potentially encountered scenarios.
Other than robotics, there are many areas where we can use the Simulation Creationism approach. In retail, while designing a floor plan, it’s possible to simulate people’s movement, making the process easier. In the supply chain and logistics, one can create transportation networks that are very resilient and efficient. And, the researchers have barely begun to explore the application of simulation in the healthcare space. If researchers could simulate the chemical interactions inside a cell, we could predict a new drug’s potential outcome and effect. And even if the simulation is not entirely accurate, it can still help and guide the testing process on new substances, which may save years of time, development, and hundreds of million dollars in the process.
