More Like this

1. How do we know how smart AI systems are?

   https://doi.org/10.1126/science.adj5957  

   Mitchell, Melanie (July 2023, Science)

   In 1967, Marvin Minksy, a founder of the field of artificial intelligence (AI), made a bold prediction: “Within a generation…the problem of creating ‘artificial intelligence’ will be substantially solved.” Assuming that a generation is about 30 years, Minsky was clearly overoptimistic. But now, nearly two generations later, how close are we to the original goal of human-level (or greater) intelligence in machines? 

   more » « less
2. How We Code

   https://doi.org/https://doi.org/10.1007/978-3-030-67788-6_5  

   Shaffer, David W.; Ruis, Andrew R. (January 2021, Advances in Quantitative Ethnography: Second International Conference, ICQE 2020, Malibu, CA, USA, February 1-3, 2021, Proceedings)

   Ruis, Andrew R.; Lee, Seung B. (Ed.)

   Coding data—defining concepts and identifying where they occur in data—is a critical aspect of qualitative data analysis, and especially so in quantitative ethnography. Coding is a central process for creating meaning from data, and while much has been written about coding methods and theory, relatively little has been written about what constitutes best practices for fair and valid coding, what justifies those practices, and how to implement them. In this paper, our goal is not to address these issues comprehensively, but to provide guidelines for good coding practice and to highlight some of the issues and key questions that quantitative ethnographers and other researchers should consider when coding data. 

   more » « less
3. How mammals prevailed

   Brusatte, S. (June 2022, Scientific American)
4. Big data of tree species distributions: how big and how good?

   https://doi.org/DOI 10.1186/s40663-017-0120-0  

   Josep M. Serra-Diaz, Brian J. (January 2017, Forest Ecosystems)
5. How Many Elements Matter?

   https://doi.org/10.3847/1538-4357/ac5023  

   Ting, Yuan-Sen; Weinberg, David H. (March 2022, The Astrophysical Journal)

   Abstract Some studies of stars’ multielement abundance distributions suggest at least 5–7 significant dimensions, but others show that many elemental abundances can be predicted to high accuracy from [Fe/H] and [Mg/Fe] (or [Fe/H] and age) alone. We show that both propositions can be, and are, simultaneously true. We adopt a machine-learning technique known as normalizing flow to reconstruct the probability distribution of Milky Way disk stars in the space of 15 elemental abundances measured by APOGEE. Conditioning on T eff and log g minimizes the differential systematics. After further conditioning on [Fe/H] and [Mg/Fe], the residual scatter for most abundances is σ [ X /H] ≲ 0.02 dex, consistent with APOGEE’s reported statistical uncertainties of ∼0.01–0.015 dex and intrinsic scatter of 0.01–0.02 dex. Despite the small scatter, residual abundances display clear correlations between elements, which we show are too large to be explained by measurement uncertainties or by the finite sampling noise. We must condition on at least seven elements to reduce the correlations to a level consistent with the observational uncertainties. Our results demonstrate that cross-element correlations are a much more sensitive probe of a hidden structure than dispersion, and they can be measured precisely in a large sample even if the star-by-star measurement noise is comparable to the intrinsic scatter. We conclude that many elements have an independent story to tell, even for the mundane disk stars and elements produced by the core-collapse and Type Ia supernovae. The only way to learn these lessons is to measure the abundances directly, and not merely infer them. 

   more » « less