"The robotic process automation (RPA) momentum started way before AI piqued the interest of enterprises," The Forrester analysts explain. "Until now, firms have been treating these set of technologies distinctly; i.e., RPA for automation, AI for intelligence. But to create breakthrough opportunities, we believe that an RPAplus-AI technology innovation chain will turbocharge your innovation efforts. Firms are already combining AI building block technologies such as ML and text analytics with RPA features to drive greater value for digital workers in four use cases: analytics that solves nagging platform issues; chatbots that boss around RPA bots; internet-of-things (IoT) events that trigger digital workers; and text analytics that lifts RPA's value."
Big Data & Analytics Innovation will bring you right up to speed to assist you with your every need covering an array of topics, themes and problem points. This summit will help you & your business understand & utilise data-driven strategies and discover what disciplines will change because of the advent of data. With a vast amount of data now available, modern businesses are faced with the challenge of storage, management, analysis, privacy, visualisation, security and disruptive tools & technologies. Join 150 of the industry's top minds at the world's largest executive led Big Data & Analytics Summit and share challenges and best practices with pioneers in the data science field.
Text analytics is a genre of analytic capabilities intended to function across the typed/written word. This area of analytics seeks to learn from huge quantities of text data to expose human intent, sentiment, and behaviors. Examples include doctor notes, tweets, product/content reviews, survey text response, and much more. There are varying types of text analytics such as text parsing, Levenshtein distance, entity extraction, tagging/classification, and chunking - just to name a few. Many areas of machine learning use text analytics as data preparation steps in order to develop models.
Artificial intelligence (AI), natural language processing (NLP), machine learning (ML), deep learning and neural networks represent powerful software-based techniques used to solve many problems. However, recently it seems like everywhere you look there is a new tech company that's using AI. It's almost as if AI is just hype or some sort of fad. I believe AI is the future. The overuse of the term made me wonder why so many companies reference the technology even though their products or services don't even use AI.
Intelligent Automation represents a developed adaptation of automation in which machines copy human activities and have cognitive abilities, including natural language processing, speech recognition, computer vision innovation, and ML. Despite the fact that Artificial Intelligence and automation are not new ideas in the innovation space, the developments in digital technologies, accessibility of sensors and increased computing power and storage have brought about expanding its reach far and wide in the worldwide business world. Automation gives off the impression of being a transfer of tasks that people do to the machines. But, as a matter of fact, automation is on a very basic level changing the way the activities/tasks are completed in business conditions. Automation comes in various forms including fixed automation, programmable automation, Robotic procedure automation, robotics, etc. Intelligent Automation innovation contrasts from typical IT automation, given the additional layer of Machine Learning and Artificial Intelligence abilities integrated with it.