In everyday conversations, humans can take on different roles and adapt their vocabulary to their chosen roles. We explore whether LLMs can take on, that is impersonate, different roles when they generate text in-context. We ask LLMs to assume different personas before solving vision and language tasks. We do this by prefixing the prompt with a persona that is associated either with a social identity or domain expertise. In a multi-armed bandit task, we find that LLMs pretending to be children of different ages recover human-like developmental stages of exploration. In a language-based reasoning task, we find that LLMs impersonating domain experts perform better than LLMs impersonating non-domain experts.

Fox News Flash top headlines are here. Check out what's clicking on Foxnews.com. ANCHORAGE, Alaska (AP) -- A headless robot about the size of a labrador retriever will be camouflaged as a coyote or fox to ward off migratory birds and other wildlife at Alaska's second largest airport, a state agency said. The Alaska Department of Transportation and Public Facilities has named the new robot Aurora and said it will be based at the Fairbanks airport to "enhance and augment safety and operations," the Anchorage Daily News reported. The transportation department released a video of the robot climbing rocks, going up stairs and doing something akin to dancing while flashing green lights.

In work presented at the 32nd International Joint Conference on Artificial Intelligence (IJCAI 2023), Kshitiz, Sonu Shreshtha, Ramy Mounir, Mayank Vatsa, Richa Singh, Saket Anand, Sudeep Sarkar and Sevaram Mali Parihar investigate using computer vision techniques to monitor large flocks of birds. In this interview, Kshitiz tells us more about this research. In our work, Long-term Monitoring of Bird Flocks in the Wild, published in IJCAI 2023, we delve into developing and applying computer vision techniques and datasets tailored for non-invasive monitoring and analysis of migratory bird flocks in their natural habitats. The aim is to understand the behavior and ecology of migratory birds through automated video analysis with minimal human intervention, thereby bolstering conservation initiatives. The core technical challenges associated with wildlife monitoring arise from the uncontrolled, outdoor nature of the imagery (both images and videos) capturing large flocks of migratory birds over several months.

Images have emerged of a strange drone shaped like a real bird that was found crashed in Somalia, leading to claims it was being used to spy on targets. The metal bird, which is shaped to mimic a large bird of prey with'feathered' wings, is reported to have been recovered in an area of the capital, Mogadishu. According to local reports, the unmanned vehicle, which has two propellers attached to its wings, may be a surveillance craft used by the Somali intelligence agency, NISA. The drone is reported to have crashed in Mogadishu, Somalia earlier this week. Low quality images show it to resemble a large bird of prey.

The best practice method for managing ecological systems under uncertainty is adaptive management (AM), an iterative process of reducing uncertainty while simultaneously optimizing a management objective. Existing solution methods used for AM problems assume that the system dynamics are stationary, i.e., described by one of a set of pre-defined models. In reality ecological systems are rarely stationary and evolve over time. Importantly, the effects of climate change on populations are unlikely to be captured by stationary models. Practitioners need efficient algorithms to implement AM on real-world problems. AM can be formulated as a hidden model Markov Decision Process (hmMDP), which allows the state space to be factored and shows promise for the rapid resolution of large problems. We provide an ecological dataset and performance metrics for the AM of a network of shorebird species utilizing the East Asian-Australasian flyway given uncertainty about the rate of sea level rise. The non-stationary system is modelled as a stationary POMDP containing hidden alternative models with known probabilities of transition between them. We challenge the POMDP community to exploit the simplifications allowed by structuring the AM problem as an hmMDP and improve our benchmark solutions.