Today, when the world is running behind AI, we are creating the world’s most advanced and cutting-edge technology using NeuroSymbolic AI to get a human-like understanding of our world. Brain-Inspired Spoken Language Understanding (BISLU) with proprietary Thought Representation Ecosystem creates an intelligent low-power technology. In a nutshell, say what you want in a single line or as many sentences as you want, Brain-Inspired Spoken Language Understanding (BISLU) will know what you are saying.
World’s first Thought Representation Ecosystem, composed of unique tools for representing complicated human-like connected thought structures on computers with ease. The proprietary language called ETML (Extended Thought Mark-up Language) is used to encode structured data representing human thoughts.
Simbo is based on Brain-Inspired Spoken Language Understanding (BISLU) architecture which understands a human-like human. Brain-Inspired Spoken Language Understanding (BISLU) uses Universal NLU which is an alternative to the intent-based AI classification model.
Universal NLU enables Simbo to transform speech into thought representation making it one of its kind and unique.SimboAlpha is a Smart Voice-Based Assistant for Doctors. This works like a Digital Secretary for Doctors. It supports native and all Indian English accents and it is trained on International and Indian clinical terms. SimboAlpha is highly accurate even in a noisy environment. It is powered by Neural Networks trained on 10 million+ audio recordings with the state-of-the-art Speech-To-Text-Engine and Clinical NLU.
In healthcare, AI can play a big role. Symbolic AI is an alternative but is mostly seen as a rule-based engine limiting the scale at which it can be used. Merging statistical AI like ANNs with Symbolic AI is a good promise and active research is being pursued. We believe that this decade is of NeuroSymbolic AI. Our active research is around breaking down the mammoth AI models into multiple stages whose boundaries are valid symbolic representations.
Our research is helping us in not just working towards explainable AI goals with NeuroSymbolic AI but also training models with minimal data. This helps us go on higher-order datasets like paragraphs and stories level without having to build large datasets.
Our NeuroSymbolic AI architecture is based on an AI architecture called GIPCA (General Intelligence Predictive and Corrective Architecture). BISLU (Brain-Inspired Spoken Language Understanding) is built up using the GIPCA architecture. Conventional NLU today is an AI model trained as an Intent-classification model. Mostly these intents are a handful and a restriction on understanding a hum human-like human by a computer.
Universal NLU is an approach to understand humans like a human which takes spoken utterances stream on one side and generates Human Thought Representations at the output. If the utterance is in domain knowledge of Universal NLU it will generate high-resolution thoughts and if out of domain then it will generate low-resolution thoughts. Universal NLU is always aware and keeps extracting information for further processing.