NIPS 2016 Reviews

NIPS (the Conference and Workshop on Neural Information Processing Systems) is a machine learning and computational neuroscience conference held every December. It was first proposed in 1986, and for a long time, it was a small conference. Interest to NIPS significantly increased when deep learning started demonstrating great results in image recognition, speech recognition, and multiple other areas. Last year NIPS had 2500+ papers submitted and 5000+ people in attendance.

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2016 Review

I treat this blog as my lab journal. I will keep posting random thoughts along with better-written articles on particular topics. This post is an attempt to summarize my activity in some areas in 2016 (see overview of ML and AI in 2016 general in my previous post). Continue reading

Machine Learning and Artificial Intelligence in 2016

2016 was an interesting year. AI winter is over, but this time AI is almost a synonym for deep learning. Major technology companies (Google, Microsoft, Facebook, Amazon and Apple) announced new products and services built using machine learning. DeepMind AlphaGo beat the world champion in Go. Salesforce bought MetaMind to build a deep learning lab. Apple promised to open up its deep learning research.

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Deep Learning for Spell Checking

I use spell checking every day, it is built into word processors, browsers, smartphone keyboards. It helps to create better documents and make communication clear. More sophisticated spell checker can find grammatical and stylistic errors.

How to add spell checking to your application? A very simple implementation by Peter Norvig is just 22 lines of Python code.

In “Deep Spelling” article, Tal Weiss wrote that he tried to use this code and found that it is slow. The code is slow because it is brute forcing all possible combinations of edits on the original text.

An interesting approach is to use deep learning. Just create artificial dataset by adding spelling errors into correct English texts. And you better have lots of text! The author has been using one billion words dataset released by Google. Then train character-level sequence-to-sequence model with LSTM layers to convert a text with spelling errors to a correct text. Tal got very interesting results. Read the article for details.

Good quality spell checkers can be very useful for chatbots. Most of the chatbots rely on simple NLP techniques, and typical NLP pipeline includes syntax analysis and part of speech tagging, which can be easily broken if the input message is not grammatically correct or has spelling errors. Perhaps fixing spelling errors earlier in the NLP pipeline can improve the accuracy of natural language understanding.

It can be good to try train such spell checker model on another dataset, more conversational.

Have you tried to use any models like this in your apps?

Intelligence Platform Stack

Machine intelligence field grows with breakneck speed since 2012. That year Alex Krizhevsky, Ilya Sutskever and Geoffrey E. Hinton achieved the best result in image classification on LSVRC-2010 ImageNet dataset using convolutional neural networks. It’s amazing that end-to-end training of a deep neural network worked better than sophisticated computer vision systems with handcrafted feature engineering pipelines being refined by researchers for decades.

Since then deep learning field got the attention of machine learning researchers, software engineers, entrepreneurs, venture investors, even artists and musicians. Deep learning algorithms surpassed the human level of image classification and conversational speech recognition, won Go match versus 18-time world champion. Every day new applications of deep learning emerge, and tons of research papers are published. It’s hard to keep up. We live in a very interesting time, future is already here.

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Natural Language Pipeline for Chatbots

Chatbot developers usually use two technologies to make the bot understand the meaning of user messages: machine learning andhardcoded rules. See more details on chatbot architecture in my previous article.

Machine learning can help you to identify the intent of the message and extract named entities. It is quite powerful but requires lots of data to train the model. Rule of thumb is to have around 1000 examples for each class for classification problems.


If you don’t have enough labeled data then you can handcraft rules which will identify the intent of a message. Rules can be as simple as “if a sentence contains words ‘pay’ and ‘order’ then the user is asking to pay for an order”. And the simplest implementation in your favorite programming language could look like this:

def isRefundRequest(message):
    return 'pay' in message or 'order' in message

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How to Run Text Summarization with TensorFlow

Text summarization problem has many useful applications. If you run a website, you can create titles and short summaries for user generated content. If you want to read a lot of articles and don’t have time to do that, your virtual assistant can summarize main points from these articles for you.

It is not an easy problem to solve. There are multiple approaches, including various supervised and unsupervised algorithms. Some algorithms rank the importance of sentences within the text and then construct a summary out of important sentences, others are end-to-end generative models.

End-to-end machine learning algorithms are interesting to try. After all, end-to-end algorithms demonstrate good results in other areas, like image recognition, speech recognition, language translation, and even question-answering.

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