Evil Johannes Gutenberg
By: Vlad Alex, Merzmensch
Tags: Data Journalism, Artificial Intelligence, Society, Digital Literacy, Machine Learning

Johannes Gutenberg was an evil dude. He killed the human factor of books. Before his time people in the sweat of their brow wrote texts, letter by letter, they dedicated their whole life to the unique human experience of writing — and reading.

Gutenberg invented the cold machine, printing book after book. Multiplying nonsense, if once written. He enabled fake news. Before his time the mischief-makers had to write their inflammatory writings by hand — it took time and saved society for a while. Now, with this evil machine, everybody could print and spread fake news within the shortest time.

After all, “Mein Kampf” was also a printed book, it wasn’t circulating in hand-written manuscripts between Nazi conspirers of the 1930ies, it was proudly presented in the central bookstores of Nazi Germany.

Evil books…

…Evil AI.

In the past, propaganda needed human hands to write it. […]With minimal effort, GPT-3 can be guided to write in a range of styles: In a recent study, the Middlebury Institute of International Studies researchers Kris McGuffie and Alex Newhouse found that it could be prompted to generate plausible pro-Nazi posts, reproduce the writing style of mass-shooter manifestos, and answer questions like a QAnon disciple. The developers of GPT-3 understand the potential for abuse and have limited the number of people with access, though hostile countries will likely develop copycat versions soon enough. (The Atlantic, 20th September 2020)

The vision of AI misuse was always a big topic in discussions about New Technologies and their impact on society. Now, with GPT-3 as a universal writer, the world has found a new bogeyman, which can be addressed in case of info-apocalypse.

This topos — a hostile technology in juxtaposing with humans as victims — is probably now like never before one of the most popular themes in our media (however colors, political movements, or countries this media represents).

Yet the main topic remains without being mentioned: humans.

We’ve seen that mislabeling and inaccurate preparation of datasets can deliver horrible results by the perfect performance of an AI. Indeed, an AI developer bears responsibility for Machine Learning models: Datasets shall base on the highest diversity as possible, the AI model has to perform without glitches.

But they do it. Even if it is not so obvious and easy.

For image deepfakes:

StyleGAN implemented Latent Space Projection, to identify an image which is suspected to be fake. (Which works not always perfect, but the approach is given).

For video deepfakes:

AI researchers use heartbeat detection to find deepfake in moving pictures.

For NLP-generated texts:

As far as I know, OpenAI is working on detection tools for contents generated with GPT-3. They also implement various semantic flagging functions to signalize when the text might bear some particular risks.

Independently of this, there is a Chrome Plug-In GPTrue of False, which can show the probability of whether the text is transformer-generated or rather human-written.

…just to mention some of the manifold efforts of the AI community to provide preventive protection of AI misuse.

Unfortunately, these endeavors don’t really reach the public or aren’t really appealing for the mainstream audience (compared to horror stories of “Black Mirror” brought to life).

This is not a rant about the insufficient digital competency of an average member of our civilization. It’s about our task to raise awareness about it, to contribute to education, to open AIs, to provide lessons, to write books and articles, to go on the digital streets.

And even if the tendency of techno-angst seems to be popular nowadays, even if people plead for the back step from the technological progress: