Developed Out of its Cogito Labs, the Company Continues Its Strategy of Innovation and Differentiation in Cognitive Computing
– Expert System (EXSY.MI), the leader in multilingual cognitive computing technology for the effective management of unstructured information, today announced the release of Cogito Studio, a product for developing customized semantic applications for text analytics, including information analysis, categorization and extraction.
With Cogito Studio, the company advances its market leadership by leveraging the research and innovation of its Cogito Labs for cognitive computing. The product combines the best of both artificial intelligence algorithms for simulating the human ability to read and understand language (semantics) and deep learning techniques (machine learning) to help companies optimize the creation of applications that are advanced, intelligent and intuitive.
“The new release of Cogito Studio is the result of the hard work and dedication of our labs, which are focused on developing products that are both powerful and easy to use,” said Marco Varone, President and CTO, Expert System. “We believe that we can make significant contributions to the field of artificial intelligence. In our vision of AI, typical deep learning algorithms for automatic learning and knowledge extraction can be made more effective when combined with algorithms based on a comprehension of text and on knowledge structured in a manner similar to that of humans.”
The innovations introduced with Cogito Studio reflect Expert System’s technological differentiation strategy and offer companies the opportunity to develop new cognitive computing applications that leverage both the benefits of semantic technology and the advantages of deep learning.
Cogito Studio will help companies optimize the launch of new projects by automatically learning new knowledge, such as that for a specific domain, by applying its flagship semantic technology that reads and understands words in context. In this way, Cogito exceeds the limitations of deep learning because the automatic learning process no longer requires constant supervision from humans, nor the need to manually acquire large volumes of data.
