In this special guest feature, Ryan Welsh, Co-founder and CEO of Kyndi, discusses how organizations are leveraging the latest natural language processing techniques to enable sophisticated natural language understanding. Ryan started Kyndi in 2014 with a vision of creating a world where AI would empower humans to do their most meaningful work. Under his leadership, Kyndi has created the natural language enablement category, offering a powerful Natural Language Enablement Platform and natural language-enabled solutions. Ryan received his B.A. in Anthropology from The Catholic University of America, his M.S. in Applied Math/Economics from Rutgers University, and an M.B.A. from the University of Notre Dame. According to Deloitte, as much as 80% of all information is hidden in unstructured, text-based data living in various systems inside and outside of the companies.

In this edition of Voices of the Industry, Jim Carson, Data Science Manager at Service Express, shares how natural language processing techniques can automate tasks and increase accuracy in your organization. The future of the data center will rely heavily on artificial intelligence (AI) and machine learning (ML) to improve business processes. As mentioned in our previous article, Streamlining Data Center Tasks With Machine Learning, many CIOs and technology leaders are already adopting an AI strategy in their IT departments. One ML technique that stands out as a focus of recent adoption in the data center due to its unique capabilities to analyze unstructured text data is natural language processing (NLP). According to IBM's Global AI Adoption Index, around half of organizations are using applications powered by NLP and over a quarter expect to implement them over the next year.

Text analysis, also known as text mining, is the process of compiling, analyzing, and extracting valuable insights or information from large volumes of unstructured texts, using machine learning and NLP (natural language processing) techniques. The sheer volume of data available on the internet today is incomprehensible. And manually analyzing this data is not really an efficient option. To help you better understand the situation, let's look at some numbers. In 2014, there were over 2.4 billion internet users either consuming or generating content. The number grew to 3.4 billion internet users by 2016.

In Alex Garland's 2014 sci-fi thriller, when Caleb the plot's anti-hero first meets Ava, an AI-driven humanoid, the first thing he does to test her intelligence is to engage her in a conversation. "So we need to break the ice. Do you know what I mean by that?", he asks. He tests her further, "what do I mean?". "Overcome initial social awkwardness", she quips.

The health & safety of our attendees & speakers is our primary concern. While this currently proves to be a tricky time for public gatherings, Dataiku is still committed to providing great tech content & facilitating discussions in the data science space. As such, weve decided to pivot towards online webinars via our partner platform, BrightTalk. IMPORTANT - RSVP HERE: https://www.brighttalk.com/webcast/17108/445121?utm_source Dataiku&utm_medium brighttalk&utm_campaign 445121 Tentative Schedule: (EST) 2:00pm: Intro 2:05pm: What Can You Do With Unstructured Text Data? w/ PwC 2:45pm: Q&A Talk Abstract: In this talk we will explore the opportunities that arise from unstructured text data. Then we will take a deep dive into a few concepts that are used in applying Machine Learning to text data & discuss how can they be leveraged using deep learning & other methods Speaker Bio: Abdallah Musmar is a Manager at PricewaterhouseCoopers.

So I just have to buy a Tableau license and I'm now a data scientist? Okay, let's just take that sales pitch with a grain of salt. I may be clueless, but I know there is more to data science than making pretty visualizations. I can do that in Excel. You got to admit it is slick marketing though. Charting data is the fun stage, and they leave out the painful and time-consuming parts of working with data: cleaning, wrangling, transforming, and loading it. Yes, and that is why I suspect there is value in learning to code. Maybe you can learn Alteryx. There's another software called Alteryx that allows you to clean, wrangle, transform, and load data.

Ontology learning is a critical task in industry, dealing with identifying and extracting concepts captured in text data such that these concepts can be used in different tasks, e.g. information retrieval. Ontology learning is non-trivial due to several reasons with limited amount of prior research work that automatically learns a domain specific ontology from data. In our work, we propose a two-stage classification system to automatically learn an ontology from unstructured text data. We first collect candidate concepts, which are classified into concepts and irrelevant collocates by our first classifier. The concepts from the first classifier are further classified by the second classifier into different concept types. The proposed system is deployed as a prototype at a company and its performance is validated by using complaint and repair verbatim data collected in automotive industry from different data sources.

So I just have to buy a Tableau license and I'm now a data scientist? Okay, let's just take that sales pitch with a grain of salt. I may be clueless, but I know there is more to data science than making pretty visualizations. I can do that in Excel. You got to admit it is slick marketing though. Charting data is the fun stage, and they leave out the painful and time-consuming parts of working with data: cleaning, wrangling, transforming, and loading it. God help you if you need your own custom domain logic when using closed tools. Yes, and that is why I suspect there is value in learning to code. Maybe you can learn Alteryx.

So I just have to buy a Tableau license and I'm now a data scientist? Okay, let's just take that sales pitch with a grain of salt. I may be clueless, but I know there is more to data science than making pretty visualizations. I can do that in Excel. You got to admit it is slick marketing though. Charting data is the fun stage, and they leave out the painful and time-consuming parts of working with data: cleaning, wrangling, transforming, and loading it. God help you if you need your own custom domain logic when using closed tools. Yes, and that is why I suspect there is value in learning to code. Maybe you can learn Alteryx.

So I just have to buy a Tableau license and I'm now a data scientist? Okay, let's just take that sales pitch with a grain of salt. I may be clueless, but I know there is more to data science than making pretty visualizations. I can even do that in Excel. You got to admit it is slick marketing though. Charting data is the fun stage, and they leave out the painful and time-consuming parts of working with data: cleaning, wrangling, transforming, and loading it. God help you if you need to write a specialized algorithm with your own domain logic when using closed tools. Yes, and that is why I suspect there is value in learning to code. Maybe you can learn Alteryx.