Today's businesses are drowning in the volumes of data they create and collect daily. Many data-related jobs are too complex or time-consuming, which is why organizations are turning to artificial intelligence (AI) and its branches of machine learning (ML) and natural language processing (NLP). Most commonly, the ML models that steer those tasks rely on deep learning: neural networks trained with large data sets--sometimes billions of data points. While these models are proving more adept than people at conquering the data mountain, the result is high computational cost and a growing data center footprint. But what if, instead of using processing-heavy neural networks, the AI could be trained to "learn" the same way the human brain does?

Paul is joined by Francisco Webber for a conversation around high-efficiency artificial intelligence (AI) and natural language understanding (NLU). Francisco Webber is co-founder and CEO of Cortical.io, He has explored search engine technologies and documentation systems in various contexts but became frustrated with the limitations to current methods. Follow him on LinkedIn and be sure to follow Paul and Rocket Software.

The global data sphere, consisting of machine data and human data, is growing exponentially reaching the order of zettabytes. In comparison, the processing power of computers has been stagnating for many years. Artificial Intelligence – a newer variant of Machine Learning – bypasses the need to understand a system when modelling it; however, this convenience comes with extremely high energy consumption. The complexity of language makes statistical Natural Language Understanding (NLU) models particularly energy hungry. Since most of the zettabyte data sphere consists of human data, such as texts or social networks, we face four major obstacles: 1. Findability of Information – when truth is hard to find, fake news rule 2. Von Neumann Gap – when processors cannot process faster, then we need more of them (energy) 3. Stuck in the Average – when statistical models generate a bias toward the majority, innovation has a hard time 4. Privacy – if user profiles are created "passively" on the server side instead of "actively" on the client side, we lose control The current approach to overcoming these limitations is to use larger and larger data sets on more and more processing nodes for training.

This is a terrific conversation on cutting-edge Natural language processing (NLP) with an entrepreneur in Vienna. Read on! Sramana Mitra: Let's start by introducing our audience to yourself as well as to Cortical.io. Francisco Webber: I am the CEO and one of the founders of Cortical.io. It is a startup originating from Vienna, Austria. We are working in the domain of natural language understanding. We have developed our own technology for rendering text information. Based on that technology, we are offering a certain number of technologies and technical platforms to the market. We started early as a startup. It might sound like a long time but we started in 2011. Believe me, setting up a company that creates the technology beneath the product takes time. Sramana Mitra: Do a historical overview of the NLP trajectory. What is the prior arc and where are you innovating? What is the approach that you bring to the table that is unique and differentiated? Francisco