MIT's Feature Labs Helps Companies Develop Faster Machine Learning Algorithms | Intelligent Machines - Evolving Science

Photo: Nikos Dimitris Fakotakis

Nikos Dimitris Fakotakis, PhD  Researcher summarizes, "Feature Labs, a startup that began at MIT in 2015, was initiated with the primary purpose of helping data scientists build machine learning (ML) algorithms that run much faster."

Faster machine learning algorithms created by MIT’s Feature Labs.
Photo: Public Domain

This is important for the future of artificial intelligence because of the impact it will have on many scientific areas. Dedicated collaborators, with strong roots in research, have systematically tried to create algorithms that warrant major changes. These changes could be with respect to how organizations build and integrate artificial intelligence models into their new services and products.

With machine learning, there is high storage of training data that needs to be decoded at an initial stage because of the complexity of content.

For this purpose, the scientists at Feature Labs have developed tools to have a better acceleration of the algorithms. The main goal of the model is the transformation of big data into valuable knowledge, which can be applied to real-world scenarios with the help of sophisticated training algorithms.

In various fields of study, we have significant amounts of data for the right training of systems. With machine learning, we build models, which lead us to our common goal - the extraction of valuable information.

A vital characteristic of ML's success is feature engineering, the process of transforming raw data into features that better represent the problem, compared to predictive models. This is, obviously, depending on how the data can be presented.

Feature Labs contributes to the quality of the main model by developing an automatic feature selection and preparation. The aim is to provide great features, describing the structure, that is inherent in the data.
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Source: Evolving Science