Quid: At Quid we took a closer look at academic papers about machine learning -- some 5,000 in total from U.S. universities -- to see how the science around it is changing.
The data for the analysis comes from SCOPUS, the largest abstract and citation database of peer-reviewed academic literature from around the globe. In just a few minutes, Quid software�sifted through the thousands of papers about machine learning and created a network where connections between nodes represent similarities in language between them.
The network map looks like this:
In the map, we noticed several interesting trends:
First, Text Analysis and Natural Language Processing make up the largest cluster in the network. Scientific papers here point to advances in machine learning through pattern recognition in language, leading to everything from faster crime analysis to more efficient assessments of clinical narratives.
While much of the network is related to health and medicine, the fourth-largest cluster is focused on environmental topics; here, machine learning helps predict how climate shifts affect, say, small mammals in Alaska or plant species in Peninsular Thailand.
The Artificial Neural Networks cluster, on the upper left, seems to have a foot in both worlds: a significant number of papers on neural networks deal with human health, while others deal with the health of lakes or forests.
The data for the analysis comes from SCOPUS, the largest abstract and citation database of peer-reviewed academic literature from around the globe. In just a few minutes, Quid software�sifted through the thousands of papers about machine learning and created a network where connections between nodes represent similarities in language between them.
The network map looks like this:
In the map, we noticed several interesting trends:
First, Text Analysis and Natural Language Processing make up the largest cluster in the network. Scientific papers here point to advances in machine learning through pattern recognition in language, leading to everything from faster crime analysis to more efficient assessments of clinical narratives.
While much of the network is related to health and medicine, the fourth-largest cluster is focused on environmental topics; here, machine learning helps predict how climate shifts affect, say, small mammals in Alaska or plant species in Peninsular Thailand.
The Artificial Neural Networks cluster, on the upper left, seems to have a foot in both worlds: a significant number of papers on neural networks deal with human health, while others deal with the health of lakes or forests.