Mesa County
DNA forensics helps identify remains found in Colorado freezer as teenager missing for nearly 20 years
Harvey Castro talks about how AI could be used in cold cases and the symbiotic relationship between AI and a detective. A human head and set of hands found inside a freezer at a western Colorado home recently sold before the discovery in January have been discovered as those of a 16-year-old girl who went missing almost 20 years ago. On Jan. 12, people were cleaning out a Grand Junction, Colorado, home, located nearly 200 miles west of Denver, when they discovered a human head and hands inside a freezer. On Friday, the Mesa County Coroner's Office announced that, through DNA testing, the victim was identified as Amanda Leariel Overstreet. The Mesa County Sheriff's Office said Overstreet is believed to have been about 16 years old when she disappeared, adding that she had not been seen or heard from since April 2005.
TravelPlanner: A Benchmark for Real-World Planning with Language Agents
Xie, Jian, Zhang, Kai, Chen, Jiangjie, Zhu, Tinghui, Lou, Renze, Tian, Yuandong, Xiao, Yanghua, Su, Yu
Planning has been part of the core pursuit for artificial intelligence since its conception, but earlier AI agents mostly focused on constrained settings because many of the cognitive substrates necessary for human-level planning have been lacking. Recently, language agents powered by large language models (LLMs) have shown interesting capabilities such as tool use and reasoning. Are these language agents capable of planning in more complex settings that are out of the reach of prior AI agents? To advance this investigation, we propose TravelPlanner, a new planning benchmark that focuses on travel planning, a common real-world planning scenario. It provides a rich sandbox environment, various tools for accessing nearly four million data records, and 1,225 meticulously curated planning intents and reference plans. Comprehensive evaluations show that the current language agents are not yet capable of handling such complex planning tasks-even GPT-4 only achieves a success rate of 0.6%. Language agents struggle to stay on task, use the right tools to collect information, or keep track of multiple constraints. However, we note that the mere possibility for language agents to tackle such a complex problem is in itself non-trivial progress. TravelPlanner provides a challenging yet meaningful testbed for future language agents.