Ticary AI Magazine

A survey of the latest chatbot API’s

Image Credit: www.martechadvisor.com

So you want to build your own chatbot, and you want to do it quickly? Well, you’re in luck; several businesses have opened up their chatbot API’s to the public, so you can use them with little to no programming experience.


Author: Rutu Mulkar-Mehta
Date: 16 November 2017
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Why are Chatbots cool, and where are they headed?

The next new wave of Artificial Intelligence is here in the form on Chatbots — which enables end users to communicate directly with machines that are programmed to converse with humans. Several message interfaces such as Facebook Messenger are perfect avenues for deploying chatbots on already existing chat frameworks.


Author: Rutu Mulkar-Mehta
Date: 16 January 2017
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Natural Language Processing vs. Machine Learning vs. Deep Learning

What is Natural Language Processing?

Natural Language Processing (or NLP) is an area that is a confluence of Artificial Intelligence and Linguistics. It involves intelligent analysis of written language. If you have a lot of data written in plain text and you want to automatically get some insights from it, you need to use NLP techniques. These insights could be - sentiment analysis, information extraction, information retrieval, search etc. to name a few.


Author: Ticary Team
Date: 08 August 2016
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Online Word2Vec for Gensim

Word2Vec [1] is a technique for creating vectors of word representations to capture the syntax and semantics of words. The vectors used to represent the words have several interesting features.

Here are a few:


Author: Rutu Mulkar-Mehta
Date: 22 August 2015
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8 life skills that I learned from Grad school

I have spent a large chunk of my life in grad school - getting my masters and Ph.D. Looking back, I feel like grad school helped me learn things that were not even on the syllabus:

Here are some life skills I learned in grad school - in no particular order


Author: Rutu Mulkar-Mehta
Date: 29 June 2015
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Understanding your Data - Basic Statistics

Have you ever had to deal with a lot of data, and don’t know where to start? If yes, then this post is for you. In this post I will try to guide you through some basic approaches and operations you can perform to analyze your data, make some basic sense of it, and decide on your approach for deeper analysis of it. I will use python and a small subset of data from the Kaggle Bikesharing Challenge to illustrate my examples. The code for this work can be found at this location. Please take a minute to download python and the sample data before we proceed.


Author: Rutu Mulkar-Mehta
Date: 10 May 2015
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