Article: Open-Source Framework Simplifies Machine Learning Processes

Open Health, Open Source, Procedures (Medical)

Open-Source Framework Simplifies Machine Learning Processes

Researchers at MIT’s Data to AI Lab (DAI Lab) have developed a new framework that can streamline machine learning processes to help organizations uncover actionable insights from big data. The system, called Cardea, is open-source and uses generalizable techniques so that hospitals can share machine learning solutions with each other, leading to increased transparency and collaboration.

To develop Cardea, researchers leveraged automated machine learning, or AutoML. The goal of AutoML is to democratize predictive tools, making it easier for people to build, use, and understand machine learning models.

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Article: How we dodged risks and raised millions for our open-source machine language startup

Free, Language, Open Data, Open Software, Open Source

How we dodged risks and raised millions for our open-source machine language startup

Open-source software gave birth to a slew of useful software in recent years. Many of the great technologies that we use today were born out of open-source development: Android, Firefox, VLC media player, MongoDB, Linux, Docker and Python, just to name a few, with many of these also developing into very successful for-profit companies.

While there are some dedicated open-source investors such as the Apache Software Foundation incubator and OSS Capital, the majority of open-source companies will raise from traditional venture capital firms.

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Article: Google Open-Sources AutoML Algorithm Model Search

Code, Open Software, Open Source

Google Open-Sources AutoML Algorithm Model Search

A team from Google Research has open-sourced Model Search, an automated machine learning (AutoML) platform for designing deep-learning models. Experimental results show that the system produces models that outperform the best human-designed models, with fewer training iterations and model parameters.

Researchers Hanna Mazzawi and Xavi Gonzalvo described the system in a recent blog post. Model Search is implemented in the TensorFlow deep-learning framework and composes a deep neural network (DNN) from a set of component blocks such as Transformers or long short-term memory (LSTM) layers.

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