Algorithms have long been able to produce basic news stories from press releases or sets of financial data; that’s not much of a threat to most humans in the news business. Now, however, artificial intelligence (AI) has taken a step further. It’s learnt to perform a tougher task — to produce convincing-looking fake news.
Stringing together a few formulaic passages from a set of numbers is a mechanical job. Inventing a fake news story on a random subject requires imagination; not every human is up to it. The San Francisco-based non-profit OpenAI, founded by Tesla chief executive officer Elon Musk and Y Combinator president Sam Altman, has produced a so-called language model that can do it. The quality of the output is somewhat uneven, but the best examples resemble human writing to a frightening degree.
On the surface, GPT-2, as the model is called, works somewhat like a popular game one can play with the less advanced version of AI on any smartphone, accepting its word suggestions one after another to create sometimes surprising little stories. GPT-2, trained on a dataset of eight million human-curated web pages, writes text by predicting the next word based on all the previous ones in it. One needs to give GPT-2 a line or two to get it started on any subject at all, the training dataset, consisting of outbound links from the social network Reddit, is rich enough for that. “The model is chameleon-like — it adapts to the style and content of the conditioning text,” OpenAI researchers wrote in a blog post.
The sample in the post is a surprisingly coherent story about a herd of unicorns discovered by a scientist in the Andes. Given two sentences about the find and the unicorns’ ability to speak perfect English, the machine produced what could almost be a story from any mainstream news site.
It gave the scientist a name, Dr Jorge Perez from the University of La Paz (there’s no school with that exact name), produced quotes from him and expanded on the unicorns’ appearance (“silver-white”) and language abilities (they speak a dialect of their own plus “fairly regular English”). Of course a human editor might have had trouble with the contradiction in this sentence: “Some believe that perhaps the creatures were created when a human and a unicorn met each other in a time before human civilisation.”
The model produced this result on the 10th attempt: The more it exercises on a given subject, the more confident and coherent its output. Examples of what GPT-2 “writes” unprompted, which OpenAI released on GitHub together with a weaker version of the model, range from slightly surreal to downright bizarre. They include a chronology of a tax scandal involving the late Senator John McCain.
Alaska Senator Lisa Murkowski became the first ‘serious’ name in the national political media drama to call for McCain to cooperate with Senate colleagues by either disclosing his tax returns or cooperating with what she called the ‘full force’ of the IRS, DOJ, FBI, etc.
Or take this bit of a technology review: The legendary Precision Bass brings a massive bass response and fun, smoky tone to the world! These versatile midbass speakers deliver incredible low frequency extension: 32’ high-frequency response — about two-thirds of a speaker.
Or what looks like a reported story from Bangladesh: DHAKA: Thousands of people marched through Dhaka on Thursday, many decked in the colours of the semi-arid northern region marked by the drought-stricken region’s tallest mountains.
To the OpenAI researchers, this crackpot creativity isn’t the most exciting feature of GPT-2; in a technical paper, they discuss its ability to perform a number of tasks for which specialised models are usually produced: translation, question answering, understanding text. It’s generally not as good as humans, but the system’s versatility is clear evidence that unsupervised learning techniques can bring AI far beyond highly specialised algorithms that can only excel at a specific task like playing a game or comparing particular kinds of images.
In the real world, however, GPT-2’s “literary gift” could have more ominous implications. Focusing its “creative power” for the narrow purposes of, say, political propaganda and disinformation could make the hand-production of such material unnecessary. No cottage fake news industry like the one that emerged in the Macedonian town of Veles during the 2016 United States presidential election would be needed for thousands of social network accounts and websites to spew any kind of partisan nonsense or invented news. They’ll be shared, too — research shows a majority of people are unable to distinguish between fake and real news.
To OpenAI’s credit, it’s fully aware of the harm that can be done with models like GPT-2; in addition to disinformation, it points to their potential for automated cyberbullying. So, though the model can also be used for innocuous purposes, such as creating better dialogue bots, the non-profit has, at least for now, decided against releasing the training dataset or the full code for the model.
Knowledge, though, can’t be contained in this way, and language models will keep improving. It’s conceivable that their “literary” product will eventually flood the media platforms that aren’t controlled by professional editors, above all the social networks. Intelligent human writing becomes especially important in the face of that coming flood — at least while there’s an audience for it.
Leonid Bershidsky is Bloomberg Opinion’s Europe columnist.