AI-generated election disinformation will be everywhere
If recent elections are anything to go by, AI-generated election disinformation and deepfakes are going to be a huge problem as a record number of people march to the polls in 2024. We’re already seeing politicians weaponizing these tools. In Argentina, two presidential candidates created AI-generated images and videos of their opponents to attack them. In Slovakia, deepfakes of a liberal pro-European party leader threatening to raise the price of beer and making jokes about child pornography spread like wildfire during the country’s elections. And in the US, Donald Trump has cheered on a group that uses AI to generate memes with racist and sexist tropes.
While it’s hard to say how much these examples have influenced the outcomes of elections, their proliferation is a worrying trend. It will become harder than ever to recognize what is real online. In an already inflamed and polarized political climate, this could have severe consequences.
Just a few years ago creating a deepfake would have required advanced technical skills, but generative AI has made it stupidly easy and accessible, and the outputs are looking increasingly realistic. Even reputable sources might be fooled by AI-generated content. For example, users-submitted AI-generated images purporting to depict the Israel-Gaza crisis have flooded stock image marketplaces like Adobe’s.
The coming year will be pivotal for those fighting against the proliferation of such content. Techniques to track and mitigate it content are still in early days of development. Watermarks, such as Google DeepMind’s SynthID, are still mostly voluntary and not completely foolproof. And social media platforms are notoriously slow in taking down misinformation. Get ready for a massive real-time experiment in busting AI-generated fake news.
Robots that multitask
Inspired by some of the core techniques behind generative AI’s current boom, roboticists are starting to build more general-purpose robots that can do a wider range of tasks.
The last few years in AI have seen a shift away from using multiple small models, each trained to do different tasks—identifying images, drawing them, captioning them—toward single, monolithic models trained to do all these things and more. By showing OpenAI’s GPT-3 a few additional examples (known as fine-tuning), researchers can train it to solve coding problems, write movie scripts, pass high school biology exams, and so on. Multimodal models, like GPT-4 and Google DeepMind’s Gemini, can solve visual tasks as well as linguistic ones.
The same approach can work for robots, so it wouldn’t be necessary to train one to flip pancakes and another to open doors: a one-size-fits-all model could give robots the ability to multitask. Several examples of work in this area emerged in 2023.