AI Must Respect Indigenous Knowledges

New Model Predicts Cancer Treatment Response

Scientists created a new model to predict how cancer patients respond to a type of treatment. The model is called COMPASS. It looks at the genes in a tumor to make predictions. The COMPASS model is better at making predictions than other methods. It was tested on 10,184 tumors from 33 types of cancer. The model improved accuracy by 8.5 percent. This matters because it can help doctors choose the right treatment for each patient. The model can also help patients live longer. Patients who were predicted to respond to the treatment did live longer. Next, the model will be used to design new trials and studies to help more patients.

11 Language Models Compared on Code Reorganization Task

A recent experiment compared 11 language models on a code reorganization task. The models were asked to propose how to untangle a complex node in a LangGraph agent. This matters because the node had 350 lines of logic, making it hard to explain, debug, and test. The results will help developers decide which model to use for generating and evaluating code proposals.

AI Helps Identify At-Risk Teens

Researchers are using AI to help doctors identify teens at risk of mental health crises. More than 40 percent of high school students feel persistently sad or hopeless. Nearly one in five teens seriously consider suicide. The AI model analyzes data from over 11 thousand children, including family conflict and health data. It can identify at-risk teens with 75 percent accuracy, up to a year before symptoms appear. This tool could help doctors spot trouble early and change lives. The Duke research team is now testing the AI tool in clinics to see how well it works outside the lab. The AI tool will be used to automate the process and analyze data in real-time, flagging which teens may be at risk during a routine checkup. Doctors will use this tool to help teens sooner.

Students Show Low 'Epistemic AI Literacy' When Using Generative AI for Coding

A new study reveals that most students lack "epistemic AI literacy" when using generative AI tools for programming. Researchers analyzed over 10,000 interactions between students and AI systems during coding tasks. They found that 78.8% of these interactions relied on non-mastery-oriented goals, with students often outsourcing work or seeking simple explanations rather than deeply understanding the AI's processes. The study highlights a significant gap in how students engage with generative AI. Only 11.1% demonstrated high epistemic engagement, combining mastery goals with advanced strategies like justifying their reasoning and carefully monitoring prompts. This suggests that most students are not effectively developing the critical thinking skills needed to work alongside AI systems. Looking ahead, educators will need to focus on teaching these advanced epistemic strategies to better prepare students for collaboration with generative AI tools in programming and other fields.

New Protocol Boosts AI Transparency and Auditability

A breakthrough protocol called Manifestation Units has been developed, enhancing how neural network components are analyzed and utilized. This system introduces a structured format that organizes component statistics into fields, allowing for easier querying and actionability. It supports various models like GPT-2 and CNNs, showing significant improvements over older methods in retrieval tasks. The protocol's key innovation is its typed structure, which outperforms unstructured approaches by making data more accessible and useful for auditing or intervening in AI systems. It also ensures that retrieved components meet causal criteria under controlled conditions, reducing redundancy and interference. This development marks a step forward in making AI mechanisms clearer and more manageable, with potential for broader applications. Future updates will focus on expanding its use across different models and refining its efficiency.