![]() This was an excellent investigation looking at how the generative AI boom has created a seedy marketplace for deepfake porn. ![]() Inside the AI porn marketplace where everything and everyone is for sale Researchers managed to help women who had lost their ability to speak communicate again with the help of a brain implant, AI algorithms and digital avatars. ( MIT Technology Review) Two papers in Nature show major advancements in the effort to translate brain activity into speech. Our reporter Zeyi Yang looks at what this means for Chinese internet users. ( MIT Technology Review)īrain implants helped create a digital avatar of a stroke survivor’s face ![]() Bits and BytesĬhinese ChatGPT alternatives just got approved for the general publicīaidu, one of China’s leading artificial-intelligence companies, has announced it will open up access to its ChatGPT-like large language model, Ernie Bot, to the general public. It’s a good start, but watermarks alone won’t create more trust online. Watermarking-a technique where you hide a signal in a piece of text or an image to identify it as AI-generated-has become one of the most popular ideas proposed to curb such harms. The hope is that it could help people tell when AI-generated content is being passed off as real, or protect copyright.īaby steps: Google DeepMind is now the first Big Tech company to publicly launch such a tool, following a voluntary pledge with the White House to develop responsible AI. Users will be able to generate images and then choose whether to add a watermark or not. The tool, called SynthID, will initially be available only to users of Google’s AI image generator Imagen. Google DeepMind has launched a new watermarking tool that labels whether images have been generated with AI. Google DeepMind has launched a watermarking tool for AI-generated images Nobody knows how language models work: “I think that the fundamental problem is that we keep focusing on test results rather than how you pass the tests,” says Tomer Ullman, a cognitive scientist at Harvard University. Lessons from the animal kingdom: Lucy Cheke, a psychologist at the University of Cambridge, UK, suggests AI researchers could adapt techniques used to study animals, which have been developed to avoid jumping to conclusions based on human bias. But that’s not true: human psychology tests rely on many assumptions that may not hold for large language models,” says Laura Weidinger, a senior research scientist at Google DeepMind. “This is the sort of thing that children can easily solve,” says Taylor Webb, one of the researchers.ĪI language models are not humans: “With large language models producing text that seems so human-like, it is tempting to assume that human psychology tests will be useful for evaluating them. GPT-3 proposed elaborate but mechanically nonsensical solutions. The idea is that the story hints at ways to solve the problem. GPT-3: Researchers at the University of California, Los Angeles, gave GPT-3 a story about a magical genie transferring jewels between two bottles and then asked it how to transfer gumballs from one bowl to another, using objects such as a posterboard and a cardboard tube. It doesn’t mean the same thing that it means for a human.” “The issue throughout has been what it means when you test a machine like this. Our tendency to anthropomorphize makes this messy: “People have been giving human intelligence tests-IQ tests and so on-to machines since the very beginning of AI,” says Melanie Mitchell, an artificial-intelligence researcher at the Santa Fe Institute in New Mexico. With these tests, we’re trying to measure and glorify their “intelligence” based on their outputs, without fully understanding how they function under the hood. What stood out to me in Will’s story is that we know remarkably little about how AI language models work and why they generate the things they do. ![]() As my colleague Will Douglas Heaven writes in his most recent article, “some people are dazzled by what they see as glimmers of human-like intelligence others aren’t convinced one bit.”Ī growing number of experts have called for these tests to be ditched, saying they boost AI hype and create “the illusion that have greater capabilities than what truly exists.” Read the full story here. The models tend to do really well in these exams, probably because examples of such exams are abundant in the models’ training data.
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