10 Matching Annotations
  1. Jun 2024
    1. the inference efficiency improved by nearly three orders of magnitude or 1,000x in less than 2 years

      for - stats - AI evolution - Math benchmark - 2022 to 2024

      stats - AI evolution - Math benchmark - 2022 to 2024 - 50% increase in accuracy over 2 years - inference accuracy improved 1000x or 3 Orders Of Magnitude (OOM)

    2. there is essentially this Benchmark 00:09:58 called the math benchmark a set of difficult mathematic problems from a high school math competitions and when the Benchmark was released in 2021 gpt3 only got 5%

      for - stats - AI - evolution - Math benchmark

      stats - AI - evolution - Math benchmark - 2021 - GPT3 scored 5% - 2022 - scored 50% - 2024 - Gemini 1.5 Pro scored 90%

  2. Oct 2023
    1. Beyond just audio recordings so for that reason two of our senior 00:15:02 researchers Benjamin Hoffman and Maddie cusumano have also developed a biologer benchmark data set and so a biologer is an animal born tag like the one in the image on the right here 00:15:14 and these produce very valuable data because they can inform us about animal ecophysiology and allow us to improve conservation by monitoring animal movements and behaviors with very high 00:15:27 resolution
      • for: BEBE, biologger Ethogram Benchmark
    2. beans and 00:13:54 this is a benchmark of animal sounds and it's a collection of audio recordings from more than 250 species and this large aggregate data set is a way to 00:14:07 test tools for classification and detection and these are outstanding problems in bioacoustics that we desperately need solutions to
      • for: BEANS, Benchmark of Animal Sounds
  3. Sep 2023
      • 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
        • in the hope of establishing a new standard dataset for ML-based bioacoustic research.
  4. Dec 2022
  5. Feb 2020
  6. Jan 2020
    1. targeting one of three TSH ranges (0.34 to 2.50, 2.51 to 5.60, or 5.61 to 12.0 mU/L)

      Note that they did not have a mild hyperthyroidism group, whereas they did have a mild hypothyroidism group.

  7. Apr 2017