Microsoft’s recently ongoing project called Bonsai Brain is dedicated to modeling and building a low-code-based AI component that can be applied to various autonomous tasks and applications. Bonsai brains have been trained and exercised to handle unforeseen scenarios and keep going. Its key selling point is the significant reduction in downtime due to improved production efficiency. Larger neural networks must be developed for automation tasks, but Bonsai’s brain functions without trained or emulated neural networks. Users can create their own custom AI models using the Bonsai Brain interface and implement them appropriately without additional resources.
In order to simulate and train the bonsai brain for all unpredictable conditions and to ensure that smarter autonomous systems are developed, the bonsai brain platform essentially uses deep reinforcement learning concepts. Three guiding principles drive how the Bonsai Brain Platform works, with the Integrate component serving as a central tenet. This part is responsible for merging bonsai brain training simulations with actual facts and providing appropriate feedback to the training process. With input from the Integrate component, the second component, known as Train, is responsible for training and modeling the brain. The final export component of the platform is a fully trained and simulative bonsai brain exposed as a Linux container installed on-premises or in the Azure environment. Two test conditions must be met in order to train the bonsai brain in the platform. To ensure that the bonsai brain is reliable and works as intended, the first criterion verifies that the precision for each simulated action must be exact. The second requirement ensures that the probability of undoing an error made by the brain must be high or fast.
Five essential elements form the basis of the entire bonsai brain. The agent in the bonsai platform that is trained and simulated to achieve the necessary goals is called the brain. The second element of the bonsai platform is the simulator that simulates the brain to be able to learn from different situations. Observations will be the input to the simulator and the output of the simulator will be the different sets of actions that the bonsai brain will perform in the bonsai platform. One of the parts of the Bonsai platform that houses all the brains and simulators developed on the platform is the workspace. One element of the Bonsai platform is iteration, which trains the brain to perform a specific action for each simulation set. The brain of the platform therefore refers to each action as an iteration. The last part of Bonsai Platform, called Episode, is used to set a cutoff point for platform iterations. The model’s ability to mimic and train the brain according to industry standards and expertise ensures that the brain simulation retains its robustness. This is one of its distinguishing features. It can be modeled to quickly adapt to any immediate production changes required.
With the help of Bonsai’s brain, Microsoft hopes to remove unnecessary code and implement effective and reliable AI models. The Bonsai Brain uses deep reinforcement learning methods to create efficient AI models that quickly simulate and create reliable AI models. According to the researchers, once Bonsai’s brain is complete, it will be an integral part of many automation systems and will be integrated into many AI models.
- Documentation: https://docs.microsoft.com/en-us/bonsai/
Khushboo Gupta is a consulting intern at MarktechPost. She is currently pursuing her B.Tech from Indian Institute of Technology (IIT), Goa. She is passionate about machine learning, natural language processing and web development. She enjoys learning more about the technical field by participating in multiple challenges.