Dublin, Nov. 25, 2022 (GLOBE NEWSWIRE) -- The "Growth Opportunities in Artificial Intelligence, Analytics, Cloud, Data Management" report has been added to ResearchAndMarkets.com's offering. This edition of IT, Computing and Communications (ITCC) Technology Opportunity Engine (TOE) provides a snapshot of the emerging ICT led innovations in Artificial Intelligence, Analytics, Cloud and Data Management. This issue focuses on the application of information and communication technologies in alleviating the challenges faced across industry sectors in areas such as retail, healthcare, BFSI, and manufacturing. ITCC TOE's mission is to investigate emerging wireless communication and computing technology areas including 3G, 4G, Wi-Fi, Bluetooth, Big Data, cloud computing, augmented reality, virtual reality, artificial intelligence, virtualization and the Internet of Things and their new applications; unearth new products and service offerings; highlight trends in the wireless networking, data management and computing spaces; provide updates on technology funding; evaluate intellectual property; follow technology transfer and solution deployment/integration; track development of standards and software; and report on legislative and policy issues and many more. The Information & Communication Technology cluster provides global industry analysis, technology competitive analysis, and insights into game-changing technologies in the wireless communication and computing space.

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.

How to call the wider group of companies which bring to market innovations that could replace existing technologies with more environmental-friendly ones, even if their primary goal doesn't strictly align with the definition of Green Tech? Green Tech has been around for the past twenty years but has only gained traction recently due to the rising concerns about global warming. The green tech and sustainability market was valued at $11.2 billion in 2020, and it is expected to reach $36.6 billion by 2025. Strictly speaking, green technology or "Green Tech" is a "technology whose use is intended to mitigate or reverse the effects of human activity on the environment" explains the Oxford English Dictionary. For the Greentech alliance, Green Tech companies are founded with the purpose of protecting the environment, have a science-based, measurable impact and do not engage in greenwashing. This definition mostly encompasses companies involved in recycling, producing clean water, or using alternative energy sources like solar or wind power.

Taylor Peer is currently working as the Director of Data Science at Cortical.io, a Viennese AI company developing business solutions for information extraction and processing using Natural Language Understanding. On the show we are talking about Semantic Folding, the core technology developed by Cortical.io

"Promising Use of Natural Language Understanding (NLU) to Identify Off-Label Drug Usage in Social Media" – this is the title of the submission that has just been accepted by the International Society of Pharmacovigilance (ISoP) for their annual conference in November. Working with a major pharmaceutical company, this project, based on Message Intelligence, evaluated Reddit posts, using NLU to identify relevant posts and classify on- vs off-label usage of drugs regardless of the wording.

All the sessions from Transform 2021 are available on-demand now. In the past, reviewing large stacks of documents was a mind-numbing chore for junior attorneys -- a process that could literally consume months of multiple employees' lives. But innovations in artificial intelligence have enabled Cortical.io Using large quantities of documents as inputs and a semantic folding theory-based natural language understanding system to parse content, Contract Intelligence can transform structured agreements and unstructured documents into comprehensible data. The software is able to search, extract, classify, and compare data from contracts, policies, financial reports, and other documents, including the ability to understand the meanings of concepts and whole sentences -- more than just keywords, which might previously have been extracted and searchable using basic optical character recognition.

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

Enterprises, in particular, are overwhelmed with communications from inside and outside of their organizations. Corporate email continues to be at the heart of this flood of data, with content that needs to be acted on in a timely manner. Consider these facts: – In 2019, more than 128.8 billion business emails were sent and received per day. This impairs productivity in matters where speed is critical, resulting in operational delays and rising costs. The product accomplishes this by understanding the semantic content--the meaning and intent of the messages – at massive scale in real time.

Because of the exponential growth of text data, enterprises need to work shifting from numeric towards text information. Making sense of text information is becoming a key asset for businesses. Take an insurance company for instance: its whole business is dependent on text data since all its products are defined verbosely. All customer interactions happen in natural language. At the moment, the only way to deal with this mass of textual information is to use a human understanding of language.