In an article in One Useful Thing, Ethan Mollick argues that AI agents have crossed the threshold of being able to complete real work with economic value. He had Claude Sonnet 4.5, using only the paper text and replication data, read the paper on its own, organize files, convert STATA statistical code to Python, and check each item one by one; it successfully reproduced multiple research results, which he then double-verified with GPT-5 Pro. He believes the improvement in agent capability stems from the fact that a small gain in accuracy is enough to complete more steps, and from new models' ability to self-correct; at the same time, he warns that using them without judgment may also produce 17 PowerPoints that nobody needs.
In an article in One Useful Thing, Ethan Mollick argues that AI agents have crossed the threshold of being able to complete real work with economic value. He had Claude Sonnet 4.5, using only the paper text and replication data, read the paper on its own, organize files, convert STATA statistical code to Python, and check each item one by one; it successfully reproduced multiple research results, which he then double-verified with GPT-5 Pro. He believes the improvement in agent capability stems
AI models reflect and even amplify real-world gender bias, and quantifying these biases is a prerequisite for mitigating the problem. The article reviews research on gender-stereotyped analogies in word embeddings, facial recognition systems' error rate of up to 34.7% for darker-skinned women (versus only 0.8% for lighter-skinned men), and coreference resolution models' tendency to associate occupations with male pronouns.
The Gradient published an article exploring neural algorithmic reasoning, that is, using deep neural networks to capture the computations of classical algorithms such as shortest paths and sorting. The article points out that MIT's "What Can Neural Networks Reason About?" demonstrates that the better the algorithmic alignment, the better the generalization; GNNs are naturally aligned with dynamic programming and outperform architectures with weaker inductive biases on DP execution benchmarks. The author also cautions that algorithmic alignment does not mean one can arbitrarily apply any highly expressive GNN, and out-of-distribution performance remains a key challenge.
The Gradient published an article exploring neural algorithmic reasoning, that is, using deep neural networks to capture the computations of classical algorithms such as shortest paths and sorting. The article points out that MIT's "What Can Neural Networks Reason About?" demonstrates that the better the algorithmic alignment, the better the generalization; GNNs are naturally aligned with dynamic programming and outperform architectures with weaker inductive biases on DP execution benchmarks. Th
A research team at North Carolina State University in the United States has published a new self-resetting soft ring jumping robot in the Proceedings of the National Academy of Sciences. It requires no manual resetting or complex control system, relying solely on the "physical intelligence" of liquid crystal elastomer materials and a ring structure, and jumps continuously more than 1,000 times under constant infrared illumination, with a maximum vertical jump height of up to 80 times its own height. The robot can also be adapted for "jump—swim—jump again" amphibious locomotion, and is expected in the future to be used for sensing and monitoring in complex environments such as grasslands, gravel, and shallow water.
A research team at North Carolina State University in the United States has published a new self-resetting soft ring jumping robot in the Proceedings of the National Academy of Sciences. It requires no manual resetting or complex control system, relying solely on the "physical intelligence" of liquid crystal elastomer materials and a ring structure, and jumps continuously more than 1,000 times under constant infrared illumination, with a maximum vertical jump height of up to 80 times its own hei