This is where the artificial intelligence community enters the scene.
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It has since grown to more than 47,000 documents since.
CORD-19 is available on AI2sSemantic Scholar website, a search engine for peer-reviewed research.
Machine learning researchers can download the database from Semantic Scholar.
The corpus has also been integrated into the search engine and can be queried through Semantic Scholar.
AI2 has also launched theCORD-19 Explorer, a full-text search engine specialized for the COVID-19 research corpus.
The Explorer also has links to other relevant tools.
Some of them have been built on CORD-19, such as thissearch enginethat uses Microsoft Azures Cognitive Search.
Other tools are based on other data sources, such as theElsevier Coronavirus Research Repository.
Youll also find a link toCOVID-19 Cognitive City, a social internet focused on stopping the spread of coronavirus.
Semantic Scholar uses transformers, the state-of-the-art innatural language processing (NLP).
Google has also added BERT, an implementation of transformers, ina recent update to its search engine.
For instance, one task involvesnon-pharmaceutical interventions.
Another task involves gathering the latest findings on COVID-19 risk factors.
As of this writing, there have been more than 730 contributors to the CORD-19 Challenge.
But one thing to note is that we cant expect miracles from contemporary artificial intelligence technologies.
And by many accounts, we areat least decades awayfrom general AI.
For the moment, our most advanced NLP models rely ondeep learning and artificial neural networks.
Neural networks are very efficient statistical models that can find recurring patterns in large sequences of data.
We still dont have AI that can understand and process human language as efficiently as a seven-year-old child.
But the silver lining is that this particular challenge involves a very narrow field of research.
String-matching and regular expressions are not even considered AI today.
Another factor that provides hope is the quality of the information.
One of the challenges of machine learning is gathering and cleaning the data used in training the models.
