Environment Animals Wildlife Birds The world's oldest wild bird has a new grandchick Biologists have been tracking Wisdom, the roughly 75-year-old Laysan albatross, since the 1950s. Albatross chicks are getting stronger. Breakthroughs, discoveries, and DIY tips sent six days a week. The U.S. Fish and Wildlife Service is shining a light on a new member of a famous feathered family--that of the world's oldest known breeding bird, a Laysan albatross called Wisdom. The agency posted a video on social media featuring a scruffy looking hatchling seemingly yawning as it hangs out in the sand in close contact with a giant bird --presumably one of its parents.

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The massive seabirds' powerful beaks can be surprisingly gentle when preening their babies. Breakthroughs, discoveries, and DIY tips sent six days a week. As thousands of birds nest in the warm sun of Midway Atoll, some tend to their new chicks. In a video posted by Friends of Midway Atoll (FOMA), one of the newest Mōlī (Laysan albatross) chicks gets a careful "beak preen" from its parent. According to FOMA, their beaks are essential survival tools, but can also be used with "precision and gentleness, applying only the pressure needed to tend to a fragile chick."

As many as 75,000 mating pairs are waiting for eggs. Breakthroughs, discoveries, and DIY tips sent six days a week. While winter is raging in an unusually large swath of the United States, the weather is balmy for the birds nesting on the Pacific Ocean's Midway Atoll. As many as 75,000 pairs of Laysan albatrosses (or mōlī in Hawaiian) are nesting in the wildlife refuge on the northwestern edge of the Hawaiian Archipelago. Now you can watch these brilliant snow-white birds while avoiding the actual snow with a 24/7 live cam.

Community science observational datasets are useful in epidemiology and ecology for modeling species distributions, but the heterogeneous nature of the data presents significant challenges for standardization, data quality assurance and control, and workflow management. In this paper, we present a data workflow for cleaning and harmonizing multiple community science datasets, which we implement in a case study using eBird, iNaturalist, GBIF, and other datasets to model the impact of highly pathogenic avian influenza in populations of birds in the subantarctic. We predict population sizes for several species where the demographics are not known, and we present novel estimates for potential mortality rates from HPAI for those species, based on a novel aggregated dataset of mortality rates in the subantarctic.

Veterinarians successfully removed the debris from the juvenile Salvin's albatross. Breakthroughs, discoveries, and DIY tips sent every weekday. A rare seabird is recovering from a successful and life-saving surgery. A fisherman from Anconcito, Ecuador, found the juvenile Salvin's albatross after he noticed that it appeared unwell. The bird had ingested four large fishing hooks and some fishing line and was brought to Puerto Lopez for rehabilitation and care.

Large language models (LLMs) are a promising venue for natural language understanding and generation. However, current LLMs are far from reliable: they are prone to generating non-factual information and, more crucially, to contradicting themselves when prompted to reason about relations between entities of the world. These problems are currently addressed with large scale fine-tuning or by delegating reasoning to external tools. In this work, we strive for a middle ground and introduce a loss based on neuro-symbolic reasoning that teaches an LLM to be logically consistent with an external set of facts and rules and improves self-consistency even when the LLM is fine-tuned on a limited set of facts. Our approach also allows to easily combine multiple logical constraints at once in a principled way, delivering LLMs that are more consistent w.r.t. all constraints and improve over several baselines w.r.t. a given constraint. Moreover, our method allows LLMs to extrapolate to unseen but semantically similar factual knowledge, represented in unseen datasets, more systematically.

Large language models (LLMs) are a promising venue for natural language understanding and generation tasks. However, current LLMs are far from reliable: they are prone to generate non-factual information and, more crucially, to contradict themselves when prompted to reason about beliefs of the world. These problems are currently addressed with large scale fine-tuning or by delegating consistent reasoning to external tools. In this work, we strive for a middle ground and introduce a training objective based on principled probabilistic reasoning that teaches a LLM to be consistent with external knowledge in the form of a set of facts and rules. Fine-tuning with our loss on a limited set of facts enables our LLMs to be more logically consistent than previous baselines and allows them to extrapolate to unseen but semantically similar factual knowledge more systematically.

The combination of self-play and planning has achieved great successes in sequential games, for instance in Chess and Go. However, adapting algorithms such as AlphaZero to simultaneous games poses a new challenge. In these games, missing information about concurrent actions of other agents is a limiting factor as they may select different Nash equilibria or do not play optimally at all. Thus, it is vital to model the behavior of the other agents when interacting with them in simultaneous games. To this end, we propose Albatross: AlphaZero for Learning Bounded-rational Agents and Temperature-based Response Optimization using Simulated Self-play. Albatross learns to play the novel equilibrium concept of a Smooth Best Response Logit Equilibrium (SBRLE), which enables cooperation and competition with agents of any playing strength. We perform an extensive evaluation of Albatross on a set of cooperative and competitive simultaneous perfect-information games. In contrast to AlphaZero, Albatross is able to exploit weak agents in the competitive game of Battlesnake. Additionally, it yields an improvement of 37.6% compared to previous state of the art in the cooperative Overcooked benchmark.

A study of bird songs conducted in the Sierra Nevada mountain range in California generated a million hours of audio, which AI researchers are working to decode to gain insights into how birds responded to wildfires in the region, and to learn which measures helped the birds to rebound more quickly. Scientists can also use the soundscape to help track shifts in migration timing and population ranges, according to a recent account in Scientific American. More audio data is coming in from other research as well, with sound-based projects to count insects and study the effects of light and noise pollution on bird communities underway. "Audio data is a real treasure trove because it contains vast amounts of information," stated ecologist Connor Wood, a Cornell University postdoctoral researcher, who is leading the Sierra Nevada project. "We just need to think creatively about how to share and access that information."