Hybrid AI
Hybrid AI brings together the best aspects of neural networks and symbolic AI. Combining huge data sets (visual and audio, textual, emails, chat logs, etc.) allows neural networks to extract patterns. Then, rule-based AI systems can manipulate the retrieved information by using algorithms to manipulate symbols.
Hybrid AI is also defined as structured, downstream, thorough and unified use of symbolic and non-symbolic AI to capture, map and structure and make all available data or knowledge of an organization readable so that the knowledge can be retrieved in natural form.
Researchers are working to develop hybrid AI systems that can figure out simple abstract relations between objects and the reason behind them as effortlessly as a human brain.
Below is the two different areas of AI:
Symbolic AI
Non-Symbolic
Symbolic AI is an area of artificial intelligence research that focuses on attempting to express human knowledge clearly in declarative form i.e., facts and rules. Key advantage of Symbolic AI is that the reasoning process can be easily understood – a Symbolic AI program can easily explain why a certain conclusion is reached and what the reasoning steps had been. A key disadvantage of Symbolic AI is that for learning process – the rules and knowledge has to be hand coded which is a hard problem.
Non-Symbolic AI area includes models in machine learning, deep learning and neural networks where lot of training data is used to try to get conclusions and decisions. A key disadvantage of Non-symbolic AI is that it is difficult to understand how the system came to a conclusion
Benefits of Hybrid AI in Business
Knowledge Graph development: As a starting point of developing any chatbot/voice assistant, we develop a Knowledge Graph for our clients. We see the Knowledge Graph as the data structure of the future. It will be the basis of all further AI-based use cases.
Process implementation: Organizations cannot avoid the digitization and preparation of organizational data. Therefore, the development of a Knowledge Graph becomes inevitable sooner or later. Onlim implements the organizational processes and workflows that will be required for regular knowledge documentation and updating in the future.
Contribution of decades of know-how: Collectively, the Onlim team has several decades of experience in Knowledge Graph development. Customers can benefit and learn from this massive wealth of experience while achieving their goal of implementing a chatbot/voice assistant.
Maximum convenience: Onlim takes care of the details in the background, while companies can concentrate on preparing and adding information. Via the Onlim Conversational AI platform, all information can be easily updated or adapted at any time.
Holistic process: Onlim accompanies customers through all phases of the process. From preparing knowledge in the form of a Knowledge Graph, to equipping chatbots or voice assistants with the ability to understand the data, provide customers with suitable answers, as well as letting customers carry out desired transactions such as purchases.
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