26:30 Brings up progress traps of this new technology

26:48

question How do we shift our (human being's) relationship with the rest of nature

27:00

metaphor - interspecies communications - AI can be compared to a new scientific instrument that extends our ability to see - We may discover that humanity is not the center of the universe

32:54

Question - Dr Doolittle question - Will we be able to talk to the animals? - Wittgenstein said no - Human Umwelt is different from others - but it may very well happen

34:54

species have culture - Marine mammals enact behavior similar to humans

• Unknown unknowns will likely move to known unknowns and to some known knowns

36:29

citizen science bioacoustic projects - audio moth - sound invisible to humans - ultrasonic sound - intrasonic sound - example - Amazonian river turtles have been found to have hundreds of unique vocalizations to call their baby turtles to safety out in the ocean

41:56

ocean habitat for whales - they can communicate across the entire ocean of the earth - They tell of a story of a whale in Bermuda can communicate with a whale in Ireland

43:00

progress trap - AI for interspecies communications - examples - examples - poachers or eco tourism can misuse

44:08

progress trap - AI for interspecies communications - policy

45:16

whale protection technology - Kim Davies - University of New Brunswick - aquatic drones - drones triangulate whales - ships must not get near 1,000 km of whales to avoid collision - Canadian government fines are up to 250,000 dollars for violating

50:35

environmental regulation - overhaul for the next century - instead of - treatment, we now have the data tools for - prevention

56:40 - ecological relationship - pollinators and plants have co-evolved

1:00:26

AI for interspecies communication - example - human cultural evolution controlling evolution of life on earth

• for: AVES, Bioacoustic foundation model

• for: animal communication, AI - animal communication, bioacoustic

• title: BEAN: The Benchmark of Animal Sounds

• author

  • Masato Hagiwara
  • Benjamin Hoffman
  • Jen-Yu Liu
  • Maddie Cusimano

• Abstract

  • The use of machine learning (ML) based techniques has become increasingly popular in the field of bioacoustics over the last years.
  • Fundamental requirements for the successful application of ML based techniques are curated, agreed upon, high-quality datasets and benchmark tasks to be learned on a given dataset.
  • However, the field of bioacoustics so far lacks such public benchmarks which cover multiple tasks and species to measure the performance of ML techniques in a controlled and standardized way and that allows for benchmarking newly proposed techniques to existing ones.
  • Here, we propose BEANS (the BEnchmark of ANimal Sounds), a collection of bioacoustics tasks and public datasets, specifically designed to measure the performance of machine learning algorithms in the field of bioacoustics.
  • The benchmark proposed here consists of two common tasks in bioacoustics:
    • classification and
    • detection.
  • It includes 12 datasets covering various species, including
    • birds,
    • land and marine mammals,
    • anurans, and insects.
  • In addition to the datasets, we also present the performance of a set of standard ML methods as the baseline for task performance.
  • The benchmark and baseline code is made publicly available at
    • \url{https://github.com/earthspecies/beans}
  • in the hope of establishing a new standard dataset for ML-based bioacoustic research.