# Brainware Computing: Concepts, Scopes and Challenges
## 2 Need for Brainware Computing
### Benefits of Brainware Computing  
Existing AI enabled services have many limitations for better services in the future such as invariability, inefficiency and lack of flexibility. Therefore, Brainware Computing paradigm may solve current limitations furthermore by having the following benefits:  
* Multi domain adaption: Brainware Computing allows the robots to have a different brain at a different time based on the service demands. As it is not possible to develop a brain in an AI agent that can perform as an expert in multiple domains, changing the brain based on the services is one of the best solutions regarding this issue. The existing related robot does not allow huge computation and storage capacity, so keeping multiple brains and switching between the trained model is not feasible in concept. For example, if a model is trained for language translation using natural language processing principles, then that model is not able to perform using video or image data. Some recent techniques can be adaptive at a multi-domain workspace by sacrificing performances. Domain-specific trained models perform better due to having the easy way of generalization and pattern recognition in organized data instances. As Brainware Computing enables a service-oriented brain based on the service demands and introduces the Brain as a Service (BaaS) infrastructure, the robots can have extensive performances in any corresponding domain at a particular time by shifting the brain. * Efficient utilization: Having a huge computational and storage capacity of cloud and edge computing with high-speed 5G connectivity, proposed Brainware Computing supports virtualization and distribution among the edges and then robots based on the demands efficiently and effectively. Existing RaaS [16] utilizes cloud computing and overlooks the possibilities of edge computing and high-speed connectivity. Brainware Computing allows users to have a more specific, expert, and high-performing brain for the task that is trained using huge datasets and computation facilities. All the global models are stored in the intelligent brain store of the core cloud, then subsets of the whole number of brains, are distributed among the edges based on the local interests. Edges enable, install and remove the brain to/from the robot based on the services. Thus, Brainware Computing pushes up the margin of possibilities of brain re-usability and platform translation for the developers and performs efficient utilization of the available resources. * Performance enhancement: An AI agent or a trained model of general-purpose suffers from poor performance due to the heterogeneous data characteristics and patterns of training data that came from multiple domains. Having a domain-specific and specialized trained model supports the AI agent for performing better. Instead of focusing on a single giant model that is trained in cross-domain nature, Brainware Computing focuses on having domain-specific multiple brains for each service. Hence, robots do not suffer from poor performances due to the data distribution and structure problem of multi-domain datasets during training. * Fast new service deployment: High-speed 5G connectivity allows 1+ Gbps bandwidth in best cases and 100+ Mbps in average cases. Besides Brainware Computing grabs the edge computing as the backbone for the distribution of the data and brain. Unlike RaaS [16], Brainware Computing deploys the brain into the robot from the nearest edges and without demanding explicit command by the administrator or programmer. These autonomous arrangements allow Brainware Computing fast response and deployment of the corresponding brain into the robot. * Cost effectiveness: The intelligent brain store of cloud contains all of the global service-specific brain. The subsets of the brain are shared with the corresponding edges based on the local interests. A single brain can perform at different regions, in different robots and at different times.