OpenAI Trains Language Model, Mass Hysteria Ensues

On Thursday, OpenAI announced that they had trained a language model. They used a large training dataset and showed that the resulting model was useful for downstream tasks where training data is scarce. They announced the new model with a puffy press release, complete with this animation (below) featuring dancing text. They demonstrated that their model could produce realistic-looking text and warned that they would be keeping the dataset, code, and model weights private. The world promptly lost its mind.

For reference, language models assign probabilities to sequences of words. Typically, they express this probability via the chain rule as the product of probabilities of each word, conditioned on that word’s antecedents

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Alternatively, one could train a language model backwards, predicting each previous word given its successors. After training a language model, one typically either 1) uses it to generate text by iteratively decoding from left to right, or 2) fine-tunes it to some downstream supervised learning task.

Training large neural network language models and subsequently applying them to downstream tasks has become an all-consuming pursuit that describes a devouring share of the research in contemporary natural language processing.

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Fake News Challenge – Revised and Revisited

The organizers of the The Fake News Challenge have subjected it to a significant overhaul. In this light, many of my criticisms of the challenge no longer apply.

Some context:

Last month, I posted a critical piece addressing the fake news challenge. Organized by Dean Pomerleau and Delip Rao, the challenge aspires to leverage advances in machine learning to combat the epidemic viral spread of misinformation that plagues social media. The original version of the the challenge asked teams to take a claim, such as “Hillary Clinton eats babies”, and output a prediction of its veracity together with supporting documentation (links culled from the internet). Presumably, their hope was that an on-the-fly artificially-intelligent fact checker could be integrated into social media services to stop people from unwittingly sharing fake news.

My response criticized the challenge as both ill-specified (fake-ness not defined), circular (how do we know the supporting documents are legit?) and infeasible (are teams supposed to comb the entire web?)

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Is Fake News a Machine Learning Problem?

On Friday, Donald J. Trump was sworn in as the 45th president of the United States. The inauguration followed a bruising primary and general election, in which social media played an unprecedented role. In particular, the proliferation of fake news emerged as a dominant storyline. Throughout the campaign, explicitly false stories circulated through the internet’s echo chambers. Some fake stories originated as rumors, others were created for profit and monetized with click-based advertisements, and according to US Director of National Intelligence James Clapper, many fake news were orchestrated by the Russian government with the intention of influencing the results.  While it is not possible to observe the counterfactual, many believe that the election’s outcome hinged on the influence of these stories.

For context, consider one illustrative case as described by the New York Times. On November 9th, 35-year old marketer Erik Tucker tweeted a picture of several buses, claiming that they were transporting paid protesters to demonstrate against Trump. The post quickly went viral, receiving over 16,000 shares on Twitter and 350,000 shares on Facebook. Trump and his surrogates joined in, promoting the story through social media. Tucker’s claim turned out to be a fabrication. Nevertheless, it likely reached millions of people, more than many conventional news stories.

A number of critics cast blame on technology companies like Facebook, Twitter, and Google, suggesting that they have a responsibility to address the fake news epidemic because their algorithms influence who sees which stories. Some linked the fake news phenomenon to the idea that personalized search results and news feeds create a filter bubble, a dynamic in which readers only encounter stories that they are likely to click on, comment on, or like. As a consequence, readers might only encounter stories that confirm pre-existing beliefs.

Facebook, in particular, has been strongly criticized for their trending news widget, which operated (at the time) without human intervention, giving viral items a spotlight, however defamatory or false. In September, Facebook’s trending news box promoted a story titled ‘Michele Obama was born a man’. Some have wondered why Facebook, despite its massive investment in artificial intelligence (machine learning), hasn’t developed an automated solution to the problem.

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