On the Evolution of Thinking

What if we are becoming the very same Artificial Intelligence that we are trying to design? The doubt has has been raised by Nicholas Carr in an article published one year ago on The Atlantic and now published on Le Monde. The theory is intriguing and the discourse goes, in the words of developmental psychologist Maryanne Wolf, more or less in this direction:

We are not only what we read, We are how we read.

So, learning directly from the voice of Socrates is not the same as learning from the Internet. The way we approach new ideas and knowledge influences how we assimilate them and how we develop our thinking. The risk is that our mind might find so attractive the effectivness of the Google’s algorithm to try to replicate it forgetting all the ambiguity that has made us what we are. What we are so far.

Update: Have a look at this article on Le Monde about the influence of the new information technologies on culture.

Friday, 5 June 2009

tweets


Twitter: frbailo

links


blogroll


RSS r-bloggers.com

  • AdaOpt classification on MNIST handwritten digits (without preprocessing)
    AdaOpt classification on MNIST handwritten digits (without preprocessing)
  • RStudio Shortcuts and Tips
    Updated: May 2020 by Appsilon Data Science How to Work Faster in RStudio In this article we have compiled many of our favorite RStudio keyboard shortcuts, tips, and tricks to help increase your productivity while working with the RStudio IDE. We’ll also provide information about supplemental tools and techniques that are useful for data scientists […]
  • How to Safely Remove a Dynamic Shiny Module
    Despite their advantages, Dynamic Shiny Modules can destabilize the Shiny environment and cause its reactive graph to be rendered multiple times. In this blogpost, I present how to remove deleted module leftovers and make sure that your Shiny graph observers are rendered just once. While working with advanced Shiny applications, you have most likely encountered […]
  • Version 0.9.1 of NIMBLE released
    We’ve released the newest version of NIMBLE on CRAN and on our website. NIMBLE is a system for building and sharing analysis methods for statistical models, especially for hierarchical models and computationally-intensive methods (such as MCMC and SMC). Version 0.9.1 is primarily a bug fix release but also provides some minor improvements in functionality. Users of […]
  • April 2020: “Top 40” New CRAN Packages
    One hundred forty-eight new packages made it to CRAN in April. Here are my “Top 40” picks in nine categories: Computational Methods, Data, Machine Learning, Medicine, Science, Statistics, Time Series, Utilities, and Visualization. Computational Methods JuliaConnectoR v0.6.0: Allows users to import Julia packages and functions in such a way that they can be called directly […]

RSS Simply Statistics

  • Asymptotics of Reproducibility
    Every once in a while, I see a tweet or post that asks whether one should use tool X or software Y in order to “make their data analysis reproducible”. I think this is a reasonable question because, in part, there are so many good tools out there! This is undeniably a good thing and […]
  • Amplifying people I trust on COVID-19
    Like a lot of people, I’ve been glued to various media channels trying to learn about the latest with what is going on with COVID-19. I have also been frustrated - like a lot of people - with misinformation and the deluge of preprints and peer reviewed material. Some of this information is critically important […]
  • Is Artificial Intelligence Revolutionizing Environmental Health?
    NOTE: This post was written by Kevin Elliott, Michigan State University; Nicole Kleinstreuer, National Institutes of Health; Patrick McMullen, ScitoVation; Gary Miller, Columbia University; Bhramar Mukherjee, University of Michigan; Roger D. Peng, Johns Hopkins University; Melissa Perry, The George Washington University; Reza Rasoulpour, Corteva Agriscience, and Elizabeth Boyle, National Academies of Sciences, Engineering, and Medicine. […]

RSS Statistical Modeling, Causal Inference, and Social Science

  • An open letter expressing concerns regarding the statistical analysis and data integrity of a recently published and publicized paper
    James Watson prepared this open letter to **, **, **, and **, authors of ** and to ** (editor of **). The letter has approximately 96,032 signatures from approximately 6 continents. And I heard a rumor that they have contacts at the Antarctic Polar Station who are going to sign the thing once they can […]
  • Blast from the past
    Lizzie told me about this paper, “Bidirectionality, Mediation, and Moderation of Metaphorical Effects: The Embodiment of Social Suspicion and Fishy Smells,” which reports: As expected (see Figure 1), participants who were exposed to incidental fishy smells invested less money (M = $2.53, SD = $0.93) than those who were exposed to odorless water (M = […]
  • This is not a post about remdesivir.
    Someone pointed me to this post by a doctor named Daniel Hopkins on a site called KevinMD.com, expressing skepticism about a new study of remdesivir. I guess some work has been done following up on that trial on 18 monkeys. From the KevinMD post: On April 29th Anthony Fauci announced the National Institute of Allergy […]