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Since a few years, chatbots are here, and they will not go away any time soon. Facebook popularised the chatbot with Facebook Messenger Bots, but the first chatbot was already developed in the 1960s. MIT professor Joseph Weizenbaum developed a chatbot called ELIZA. The chatbot was developed... Read more
The world of the C-suite isn’t quite ready to embrace artificial intelligence just yet. That’s according to an oft-cited Accenture study from late 2016, which found that less than half of the 1,700 business leaders interviewed in a global sample would feel comfortable trusting the advice... Read more
Imagine you’re Rene Descartes, data scientist at Facebook in charge of saying true things. Problem is, your only input is what people say, and everybody lies. It’s not a moral failure, it’s just that you’ve built Facebook into the world’s largest integrated content distribution machine, and convincing... Read more
Note: This post was written together with the awesome Julian Eisenschlos and was originally published on the TensorFlow blog. Hello there! Throughout this post we will show you how to classify text using Estimators in TensorFlow. Here’s the outline of what we’ll cover: Loading data using Datasets. Building baselines... Read more
The rise of Big Data created the need for data applications to be able to consume data residing in disparate databases, of wildly differing schema. The traditional approach to performing analytics on this sort of data has been to warehouse it; to move all the data... Read more
Beyond big data analysis lies an innovation known as cognitive analysis, which is capable of providing insights with minimum human support. Information accumulating from disparate sources, differing in formats, is known as big data. This data is essential for organizations as it is capable of providing... Read more
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
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
This is the 3rd installment of a new series called Deep Learning Research Review. Every couple weeks or so, I’ll be summarizing and explaining research papers in specific subfields of deep learning. This week focuses on applying deep learning to Natural Language Processing. The last post was Reinforcement Learning... Read more
Last month, I wrote a blog post warning about how, if you follow popular trends in NLP, you can easily accidentally make a classifier that is pretty racist. To demonstrate this, I included the very simple code, as a “cautionary tutorial.” The post got a fair amount... Read more