Dr. Roth’s research focuses on the study of machine learning and inference methods to facilitate natural language understanding. With an emphasis on computational foundations of intelligent behavior, he is dedicated to developing theories and systems pertaining to intelligent behavior using a unified methodology. At the heart of this is the idea that learning has a central role in intelligence.
Deep learning, a component of AI and machine learning, allows for knowledge to be gained using artificial neural networks that are modeled on how a human’s biological nervous system operates. While this concept has been around for decades, recent technological advances have allowed software to compute data at rapid rates in order to gain a comprehensive overview more effectively.
By having a deeper understanding of the context of data, and how past scenarios predict what may occur in the future, this process is applicable in detecting levels of risk that a certain outcome may occur.
The future of legal services is about levering advanced technology to boost efficiency, capture cost savings and keep current and prospective clients happy. This has created a race to implement solutions that tap into the power of artificial intelligence and machine learning.
In a recent LegalTech News article, author Sean Doherty takes a deep dive into how law firms are leveraging such technology to create new business models. He highlights one key point showing how law firms should be thinking about the future of AI. This article highlights NexLP’s proprietary Story Engine, and why it’s helping define the future of how AI is being applied to legal services.
Using Artificial Intelligence to analyze data isn’t a new concept. Using AI technology to make sense of disparate, unstructured data — giving it context — and turning that analysis into meaningful insight to solve real-world problems and create opportunities for legal and compliance teams is the future of AI.
NexLP's Head of Machine Learning, Dr. Irina Matveeva, discusses how NLP Technology can be used for understanding context at unprecedented scale. Architectural history provides a unique case study and challenge for NLP because it is a highly nuanced subject. However, Irina and the other researchers achieved greater accuracy and scale than were possible using previous techniques.