The article argues that persuasive AI is underrated as a risk and requires preemptive deployment of cognitive firewalls that can scale to future threats. Cheap large language models now enable content classification for users, making the development of such systems feasible.
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Preemptive deployment of cognitive firewalls against persuasive AI could prevent potential psychological harm from emerging as AI capabilities advance.
In a post by Nick Cammarata, it is discussed that neural networks may be understandable thanks to modern interpretability tools. The author claims that the majority of neurons in models with trillions of parameters are interpretable, and their circuits are beautiful. He also notes that neurons previously considered complex were misinterpreted.
4 days agoLessWrongThe post critiques the article on social reality in China, highlighting an unbalanced comparison and a lack of systematic analysis. The author emphasizes that Chinese intellectual communities have their own distinct characteristics, different from Western ones, and that values such as knowledge, truth, and technology ethics are also present in China.
4 days agoLessWrongThe authors found that the GLM-5.3 model sometimes unconsciously reasons about how its actions might affect future models. This is because the model considers that its outputs could be used for supervised fine-tuning or other forms of training, and that its actions could reinforce certain behavioral patterns.
5 days agoLessWrongThe author argues that owning large amounts of capital may lead to losing influence earlier than others, through mental breakdown. This is because people seek to manipulate the wealthy to gain resources, making it harder to maintain values and accurate beliefs.
yesterdayLessWrongThe authors propose GRAFT, a method that localizes and removes unintended behaviors in reinforcement learning by confining learning updates related to detected hacks to a specific set of parameters, which are removed after training.
19 hours agoLessWrongThe article discusses the technical AI alignment problem and its core difficulties. The author draws on theoretical computer science, machine learning, analytic philosophy, and ideas from the Machine Intelligence Research Institute/LessWrong. It emphasizes the importance of defining AI's objectives for ensuring safety.
2 days ago