This has been a wild year for AI. If you’ve spent much time online, you’ve probably bumped into images generated by AI systems like DALL-E 2 or Stable Diffusion, or jokes, essays, or other text written by ChatGPT, the latest incarnation of OpenAI’s large language model GPT-3.
Sometimes it’s obvious when a picture or a piece of text has been created by an AI. But increasingly, the output these models generate can easily fool us into thinking it was made by a human. And large language models in particular are confident bullshitters: they create text that sounds correct but in fact may be full of falsehoods.
While that doesn’t matter if it’s just a bit of fun, it can have serious consequences if AI models are used to offer unfiltered health advice or provide other forms of important information. AI systems could also make it stupidly easy to produce reams of misinformation, abuse, and spam, distorting the information we consume and even our sense of reality. It could be particularly worrying around elections, for example.
The proliferation of these easily accessible large language models raises an important question: How will we know whether what we read online is written by a human or a machine? I’ve just published a story looking into the tools we currently have to spot AI-generated text. Spoiler alert: Today’s detection tool kit is woefully inadequate against ChatGPT.
But there is a more serious long-term implication. We may be witnessing, in real time, the birth of a snowball of bullshit.
Large language models are trained on data sets that are built by scraping the internet for text, including all the toxic, silly, false, malicious things humans have written online. The finished AI models regurgitate these falsehoods as fact, and their output is spread everywhere online. Tech companies scrape the internet again, scooping up AI-written text that they use to train bigger, more convincing models, which humans can use to generate even more nonsense before it is scraped again and again, ad nauseam.
This problem—AI feeding on itself and producing increasingly polluted output—extends to images. “The internet is now forever contaminated with images made by AI,” Mike Cook, an AI researcher at King’s College London, told my colleague Will Douglas Heaven in his new piece on the future of generative AI models.
“The images that we made in 2022 will be a part of any model that is made from now on.”
In the future, it’s going to get trickier and trickier to find good-quality, guaranteed AI-free training data, says Daphne Ippolito, a senior research scientist at Google Brain, the company’s research unit for deep learning. It’s not going to be good enough to just blindly hoover text up from the internet anymore, if we want to keep future AI models from having biases and falsehoods embedded to the nth degree.
“It’s really important to consider whether we need to be training on the entirety of the internet or whether there’s ways we can just filter the things that are high quality and are going to give us the kind of language model we want,” says Ippolito.
Building tools for detecting AI-generated text will become crucial when people inevitably try to submit AI-written scientific papers or academic articles, or use AI to create fake news or misinformation.
Technical tools can help, but humans also need to get savvier.
Ippolito says there are a few telltale signs of AI-generated text. Humans are messy writers. Our text is full of typos and slang, and looking out for these sorts of mistakes and subtle nuances is a good way to identify text written by a human. In contrast, large language models work by predicting the next word in a sentence, and they are more likely to use common words like “the,” “it,” or “is” instead of wonky, rare words. And while they almost never misspell words, they do get things wrong. Ippolito says people should look out for subtle inconsistencies or factual errors in texts that are presented as fact, for example.
The good news:her research shows that with practice, humans can train ourselves to better spot AI-generated text. Maybe there is hope for us all yet.
Melissa Heikkilä is a senior reporter at MIT Technology Review, where she covers artificial intelligence and how it is changing our society. Previously she wrote about AI policy and politics at POLITICO. She has also worked at The Economist and used to be a news anchor. Forbes named her as one of its 30 under 30 in European media in 2020.
Twitter: Melissahei
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