In this article, we have taken an observational approach to defining an important aspect of computer generated information based on our understanding of â€œwhat is knowledge representation in artificial intelligence?â€ In general terms, KR is used to define the graphical relationship between data models and their applications within the software development cycles, such as apps, connectors, and automation tools. There is a whole new industry in the machine learning domain that specifically works to generate new applications breaking the conventions of what is knowledge representation management (also called as KRM) in AI and delivering new objectives to machine learning developers.
Why so much attention to knowledge representation in Machine Learning and AI fields
In the recent times, many new subdomains within AI have developed to expand the horizon of computer software development for machine learning algorithms. As we head into the next phase of innovations with exciting machine learning software development tools, experts are already pivoting their bet on concepts such as Voice AI, Natural language processing, computer vision, and so on. The primary component driving the excitement is related to an important technique that AI experts have found to be the answer for solving many complex machine level problems. Yes! We are talking about Knowledge Representation, the precursor to data fabric models used by scientists to develop knowledge graphs at a machine intelligence level.
Letâ€™s discuss the role of knowledge representation in Artificial Intelligence.
Bridging the gaps in Human Intelligence
AI engineers have found out new ways to express ML outcomes for improving the understanding of the external brain, also called as Artificial Neural Networks or Artificial Human Brain. The ANN projects are considered to be the stepping stones to building the perfect human bot that can perform not just every work but also think and supervise on others on how to do and when to do it.
All these are representations of objects, events, performance data management, and knowledge management at a machine level. Knowledge representation is an advanced statistical technique used extensively for developing a data fabric that governs thinking, action, and supervising for any machine that is developed to supersede humans using ANNs.Before we succeed in building or developing a Data Fabric for KRM, we must be able to define the scope of Knowledge Graphs in AI.
KRM would be close to human intelligence in terms of how machines are able to understand, reason, and respond to software commands using pre-existing data fabric and intelligence models. How humans do a work canâ€™t be matched by machines â€“ but the whole intent of developing KRs in AI is to prove this notion wrong and tilt the table on its head for future applications linked to cameras, drones, smart devices, and connected appliances that seemingly force us to decode how the future of the Internet of Things would look like in the future. The answer is – -everything would be programmed with Knowledge Representation capabilities.
Whatâ€™s the scope of KR in the AI Industry?
We have covered so much on virtual assistants and chatbots. Itâ€™s time we excel in our understanding of what a real virtual assistant would look like.
Knowledge Representation would solve is that related to how ANNs are developed for multiple classification and landscapes. KRM in AI allows the machine to think differently for the same problem, and therefore arrive with many potent sets of responses, each of which could be considered as a reasonable solution to an existing problem. With refined Machine Learning, these KR delivered answers could be further synthesized and fed to a secondary system of intelligent machines.
Knowledge representation empowers a machine to see, hear and respond to complex instructions based on its â€œthinking pre-programmed AI brain.â€ The whole system of KR-empowered AI machines demonstrates the unique ability to intelligently behave as agents of humans, deciphering the rights from wrongs in minimal cues or prompts. The way we are developing supercomputers and embedded AI software, we can safely assume today that intelligent machines of tomorrow would be nothing less than the offset of human agents that can learn and unlearn things from the database that are designed by Knowledge Representation experiments.