– A cutting-edge abstract language model poised to revolutionize research and knowledge discovery functions across multiple industries.
– Harnesses the potential of AI to contribute up to $15.7 trillion to the global economy by 2030.
India, 14 March 2023: GyanAI has announced the launch of the world’s first language model and natural language understanding engine that is fully explainable. GyanAI’s technology is proprietary and aims to understand the meaning of language as closely as humans do. The output of GyanAI’s model is a combination of extractive and generative text, and a user can trace all results back to their source. Furthermore, GyanAI can provide reasoning for its output, making it a trusted source of information for organizations that rely on AI systems to make critical decisions.
According to McKinsey’s estimates, the number of AI capabilities used by organizations has doubled between 2018 and 2022, including natural-language generation. As the reliance on AI systems continues to grow, it is increasingly important for organizations to have access to trustworthy results and conclusions. Explainable AI is a crucial factor in achieving this goal. Companies with at least 20% of their bottom-line returns attributed to the use of AI are more likely to follow advanced AI best practices that enable explainability. Additionally, those that build digital trust with consumers through measures such as making AI explainable can see a 10% or higher increase in revenue, according to research conducted by McKinsey in 2022.
Escape the ‘Black Box’: Transforming Beyond the Transformer Model
Large Language Models (LLMs) are neural networks trained on massive amounts of natural language content. However, LLMs are often considered black boxes, purely a product of their training data. They are primarily designed to predict the next word in a sequence, with documented limitations such as lack of explainability, reasoning and factual errors, inability to fully comprehend compositional context, susceptibility to biased training data, and tractability, among others.
On the other hand, GyanAI represents a new kind of knowledge engine based on a content-independent language model that can deeply understand textual discourse without relying solely on word patterns. Unlike LLMs, GyanAI acquires knowledge in near real-time from processed documents and, if desired, one or more Gyan Knowledge Stores. As the Gyan language model is independent of content, it cannot be manipulated with biased training data or used to generate misinformation.
Venkat Srinivasan, Founder of GyanAI and Serial AI Entrepreneur, said: “Our core objective is to provide an interpretable, robust capability for machines to understand natural language as close as possible to the way humans do. We have re-defined what a ‘language model’ can be. We are principally motivated by a desire to efficiently acquire and apply, automatically where possible, insights from the enormous amount of information we have access to in various fields. GyanAI can be used in conjunction with LLMs where beneficial. While GyanAI is ready for purpose-specific enterprise deployment and is in production use, we anticipate announcing API for application developers in the near future.”
From Search to Knowledge
According to Gartner’s report, the number of global knowledge workers has surpassed one billion and continues to grow at a rapid pace. Typically, these workers rely on search engines based on both online content and internal documents. However, today’s search engines are limited in their ability to provide full knowledge acquisition, often only providing access to information. Additionally, they can generate a significant number of false positives and negatives.
In contrast, GyanAI supports the entire knowledge acquisition process, guiding users from their initial search to a deeper understanding of the information at hand. Rather than simply providing access to information, GyanAI enables users to truly acquire knowledge and avoid the pitfalls of false positives and negatives that plague traditional search engines.
Nitin Nohria, former Dean of Harvard Business School commented: “Large language models have opened up our imaginations on how AI will soon become ubiquitous in its use. Although answers that emerge from black box LLMs may be satisfactory in several use cases, there will be many others where a full understanding of how we arrived at the answers will really ‘matter’. Most professional and knowledge work will require explainable AI. And that’s what makes GyanAI such a promising technology. GyanAI is showing us a whole new way to realize the immense possibilities of AI to transform knowledge work and unleash a new era of unprecedented innovation”.
M.S. Vijay Kumar, Senior Advisor to VP, Open Learning, and former Associate Dean of Open Learning at the Massachusetts Institute of Technology added:
“GyanAI’s capabilities, that I have seen, present important opportunities for purposeful, lifelong learning. For example, GyanAI can be used to rapidly assemble relevant content collections into stackable micro-learning units and customized learning pathways directed towards different competencies – thus dynamically connecting learners to labor market opportunities and societal needs. I consider GyanAI’s ability to keep knowledge fresh and updated through continuous knowledge discovery to be a critical element of the infrastructure to support continuous learning and research”.