This article will explain the concept to identify fake news. We use Deep Learning to classify a set of articles into'fake' and'real' news classes. The data set contains three CSV files which are train, test, and submit files. Now reset the index because we remove the nan values. Data pre-processing because the data have different characters, special characters, spaces and words that are not important. For that we can remove them with stopwords.
We are interviewing Walid Saba on *Friday* for Machine Learning Street Talk show (with Yannic Kilcher). He has just written an article, but written many before claiming that deep learning and memorisation / statistical approaches are completely flawed for NLU. He calls these approaches "BERTology" which I think it a funny name! He points out the "the missing text phenomenon" as the biggest issue i.e. "the corner table wants a beer" -- "the _person_ at the corner table wants a beer" ... and provides many other similar examples. He makes a "proof" for this by equating ML to "compressability" and NLU to "expansion" which is intuitive, although I would argue ML could just as easily be used to decompress, think a basic generative model to learn to decompress something.
Google is putting AI and machine learning technologies into the hands of journalists. The company this morning announced a suite of new tools, Journalist Studio, that will allow reporters to do their work more easily. At launch, the suite includes a host of existing tools as well as two new products aimed at helping reporters search across large documents and visualizing data. The first tool is called Pinpoint and is designed to help reporters work with large file sets -- like those that contain hundreds of thousands of documents. Pinpoint will work as an alternative to using the "Ctrl F" function to manually seek out specific keywords in the documents.