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Professor John Kelleher discusses recurrent neural networks and conversational AI
Voice translate assistants like Google Home, Siri, Alexa and other similar platforms are now commonplace.  However, for the most part, these device are limited to question and answer  type exchanges and not conversational.  The next big focus for machine translation is dialog systems that go beyond... Read more
Text Analysis in Excel: Real world use-cases
Last month, we launched an Excel add-in, a solution for using ParallelDots NLP APIs to do text analysis on unstructured data without writing a single line of code. The Excel add-in is very easy to use and provides a convenient, yet effective solution for your text analysis... Read more
This is a presentation given for Data Science DC on Tuesday November 14, 2017. PDF slides PPTX slides Further resources up front: A Brief Survey of Deep Reinforcement Learning (paper) Karpathy’s Pong from Pixels (blog post) Reinforcement Learning: An Introduction (textbook) David Silver’s course (videos and slides) Deep Reinforcement Learning Bootcamp (videos, slides, and labs) OpenAI gym / baselines (software)... Read more
Making a machine learning model usually takes a lot of crying, pain, feature engineering, suffering, training, debugging, validation, desperation, testing and a little bit of agony due to the infinite pain. After all that, we deploy the model and use it to make predictions for future data. We can run our... Read more
A Global Perspective: The Future of Artificial Intelligence
The world is being transformed through rapid breakthroughs in data and AI. Every day, we hear about more innovations — from small startups to global economies. Nearly every job currently occupied by human labour–from farmers, offshore customer service representatives and even taxi drivers–could be given to... Read more
Named Entity Recognition: Milestone Models, Papers and Technologies
Named Entity Recognition: Extracting named entities from text Named Entity Recognition (NER), or entity extraction is an NLP technique which locates and classifies the named entities present in the text. Named Entity Recognition classifies the named entities into pre-defined categories such as the names of persons, organizations,... Read more
GANs explained. Generative Adversarial Networks applied to Generating Images
Editor’s note: Guest post co-writer Keshav Dhandhania.  In this article, I’ll talk about Generative Adversarial Networks, or GANs for short. GANs are one of the very few machine learning techniques which has given good performance for generative tasks, or more broadly unsupervised learning. In particular, they... Read more
Bayesian Surprise
For reasons not entirely unconnected with NZ election polling, I’ve been thinking about surprise in Bayesian inference again: what happens when you get a result that’s a long way from what you expected in advance? Yes, your prior is badly calibrated and you should feel bad,... Read more
In a previous article, we discussed the origin story and history of the Python deep learning library TensorFlow. It’s experienced a monumental rise like nothing seen before, in just two years since its debut it currently holds the title of the most forked repo on GitHub.... Read more
UNHCR Refugee Data Visualized
Where’s the Data? The data I’m using is taken from the United Nations High Commissioner for Refugees (UNHCR) website – the UN Refugee Agency. You can read more on what they do and why the exist in the link above.  Currently you can only download the mid-year statistics for 2015.... Read more