Home Data News Data Science News Daily Roundup – 14th March 2018

Data Science News Daily Roundup – 14th March 2018

Futuristic globalization interface.
2 min read

Big Squid, Inc. releases Kraken platform, Baidu’s Machine Reading Comprehension Challenge, Apache Kylin new version supports SparkSQL, and more in today’s top stories around machine learning, deep learning, and data science news.

Top Data science News Stories of the Day

Other Data Science News at a Glance

1. “Released the Kraken”, announces Big Squid, Inc.. The Kraken platform, through self-service machine learning, aims to bring powerful analytical insights to customers, which will further extend the ability of organizations to gain insight into future trends affecting their business and to make decisions on those forecasts with greater certainty, maximizing their data and business intelligence investment. 

Read more on PRWeb.

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2. Baidu Research launches a Machine Reading Comprehension Challenge to advance the state of the art in Natural Language Processing (NLP). Contestants will have access to the world’s largest Chinese MRC dataset and a chance to win 100k RMB.

Read more on Baidu’s Challenge Page.

3. Progress Launches AI-Driven Chatbot, Progress NativeChat. It is an artificial intelligence-driven platform for creating and deploying chatbots. NativeChat is based on patent pending CognitiveFlow technology that can be trained with goals, examples and data from existing backend systems, similar to the process used for training new customer service agents.

Read more on Digital Journal.

4. Apache Kylin has been updated with a new version that supports SparkSQL in building intermediate flat Hive tables. Kylin is an open source distributed analytics engine designed to provide a SQL interface and multi-dimensional analysis (OLAP) on Apache.

Read more on I Programmer blog.

5. Google Open Sources its Exoplanet-Hunting AI, Kepler. Google is saving everyone the time of training a neural network on Kepler data by releasing its code freely. One can get the TensorFlow code on GitHub.

Read more on Extreme Tech.



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