On 9 June, Anthropic released its Fable generative AI model. Three days later, the US government classified it as a dangerous munition, and used its export-control authority to prohibit any foreign nationals from accessing it. Unable to differentiate between Americans and foreigners, the company shut off access for everyone.
The government’s actions won’t help. The problem isn’t any one particular models; it’s the general trend of increasing AI capabilities. And any real solution requires the sort of collective action that just isn’t possible right now.
Fable is the constrained version of Mythos, the AI model Anthropic announced in April. It only released it to a few selected organizations, because it claimed it was so good at finding and exploiting vulnerabilities in computer code that it releasing it more generally would be dangerous.
It was an obviously self-serving announcement, and because few were able to verify Anthropic’s claims they was met with some skepticism. Those with access used Mythos to find, and patch, many vulnerabilities in their own software. But one UK group found the latest, already public, OpenAI model to be just as powerful.
Fable is just another incremental improvement in the years-long climb of AI capabilities. But just as important as the AI model is the “harness”. This is typically not AI. It’s ordinary computer code that interfaces with the user. It stitches together AI models, decides how and for what purposes they can be used, and gives them useful tools such as web search and the ability to run it’s own computer code.
When Mythos first entered limited release, there was widespread debate whether its power came from the model or the harness. With Mythos demonstrating that it was possible, the open-source community scrambled to build harnesses that could steer other AI models towards similar capabilities.
They largely succeeded. For example, a Prague company was able to replicate Anthropic’s few verifiable cybersecurity capabilities with a much smaller and cheaper model – and a more sophisticated harness. Last week, a group showed that multiple cheaper models harnessed in concert matches Fable’s performance.
The broader community had only a few days with Fable, but that time we learned some about its capabilities. It’s difference is less the new model’s raw analytical and problem solving capabilities, and more that the model doesn’t need that sophisticated harness.
Fable requires much less expertise and detailed prompting from the human user. You can give it a difficult goal and it will figure out novel and unexpected ways to satisfy it, finding loopholes in whatever constraints you or the system have imposed on it.
“Relentlessly proactive” is how AI researcher Simon Willison described it. Another descriptor might be “creative”. Experienced AI developers have had that combination of creativity and proactivity since last year, but Fable puts it within easy reach of everyone.
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