To detect fake news, this AI first learned to write it

One of thebiggest problems in media today is so-called “fake news,” which is so highly pernicious in part because it superficially resembles the real thing. AI tools promise to help identify it, but in order for it to do so, researchers have found that the best way is for that AI to learn to create fake news itself — a double-edged sword, though perhaps not as dangerous as it sounds.

Groveris a new system created by the University of Washingtonand Allen Institute for AI (AI2) computer scientists that is extremely adept at writing convincing fake news on myriad topics and as many styles — and as a direct consequence is also no slouch at spotting it. The paper describing the model is available here.

The idea of a fake news generator isn’t new — in fact, OpenAI made a splash recently by announcing that its own text-generating AI was too dangerous to release publicly. But Grover’s creators believe we’ll only get better at fighting generated fake news by putting the tools to create it out there to be studied.

“These models are not capable, we think right now, of inflicting serious harm. Maybe in a few years they will be, but not yet,” the lead on the project, Rowan Zellers, told me. “I don’t think it’s too dangerous to release — really, weneedto release it, specifically to researchers who are studying this problem, so we can build better defenses. We need all these communities, security, machine learning, natural language processing, to talk to each other — we can’t just hide the model, or delete it and pretend it never happened.”

Therefore and to that end, you can try Grover yourself right here. (Though you might want to read the rest of this article first so you know what’s going on.)

Voracious reader

The AI was created by having it ingest an enormous corpus of real news articles, a dataset called RealNews that is being introduced alongside Grover. The 120-gigabyte library contains articles from the end of 2016 through March of this year, from the top 5,000 publications tracked by Google News.

By studying the style and content of millions of real news articles, Grover builds a complex model of how certain phrases or styles are used, what topics and features follow one another in an article, how they’re associated with different outlets, ideas, and so on.

This is done using an “adversarial” system, wherein one aspect of the model generates content and another rates how convincing it is — if it doesn’t meet a threshold, the generator tries again, and eventually it learns what is convincing and what isn’t. Adversarial setups are a powerful force in AI research right now, often being used to create photorealistic imagery from scratch.

It isn’t just spitting out random articles, either. Grover is highly parameterized, meaning its output is highly dependent on input. So if you tell it to create a fake article about a study linking vaccines and autism spectrum disorders, you are also free to specify that the article should seem as if it appeared on CNN, Fox News, or even TechCrunch.

I generated a few articles, which I’ve pasted at the bottom of this one, but here’s the first bit of an example:

Serial entrepreneur Dennis Mangler raises 6M to create blockchain-based drone delivery

May 29, 2019 – Devin Coldewarg

Drone delivery — not so new, and that raises a host of questions: How reliable is the technology? Will service and interference issues flare up?

Drone technology is changing a lot, but its most obvious use — package delivery — has never been perfected on a large scale, much less by a third party. But perhaps that is about to change.

Serial entrepreneur Dennis Mangler has amassed an impressive — by the cybernetic standards of this short-lived and crazy industry — constellation of companies ranging from a top-tier Korean VC to a wholly owned subsidiary of Amazon, ranging from a functional drone repair shop to a developer of commercial drone fleets.

But while his last company (Amazon’s Prime Air) folded, he has decided to try his hand at delivery by drone again with Tripperell, a San Francisco-based venture that makes sense of the cryptocurrency token space to create a bridge from blockchain to delivery.

The system they’re building is sound — as described in a new Medium post, it will first use Yaman Yasmine’s current simple crowdsourced drone repair platform, SAA, to create a drone organization that taps into a mix of overseas networks and domestic industry.

From there the founders will for