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Programming: Biases, Markdown, LLVM and PyCharm

Submitted by Roy Schestowitz on Thursday 12th of September 2019 06:54:41 PM Filed under
Development
  • Bias and ethical issues in machine-learning models

    The success stories that have gathered around data analytics drive broader adoption of the newest artificial-intelligence-based techniques—but risks come along with these techniques. The large numbers of freshly anointed data scientists piling into industry and the sensitivity of the areas given over to machine-learning models—hiring, loans, even sentencing for crime—means there is a danger of misapplied models, which is earning the attention of the public. Two sessions at the recent MinneBOS 2019 conference focused on maintaining ethics and addressing bias in machine-learning applications.

    To define a few terms: modern analytics increasingly uses machine learning, currently the most popular form of the field broadly known as artificial intelligence (AI). In machine learning, an algorithm is run repeatedly to create and refine a model, which is then tested against new data.

    MinneBOS was sponsored by the Twin Cities organization Minne Analytics; the two sessions were: "The Ethics of Analytics" by Bill Franks and "Minding the Gap: Understanding and Mitigating Bias in AI" by Jackie Anderson. (Full disclosure: Franks works on books for O'Reilly Media, which also employs the author of this article.) Both presenters pointed out that bias can sneak into machine learning at many places, and both laid out some ways to address the risks. There were interesting overlaps between the recommendations of Franks, who organized his talk around stages, and of Anderson who organized her talk around sources of bias.

    When we talk about "bias" we normally think of it in the everyday of sense of discrimination on the basis of race, gender, income, or some other social category. This focus on social discrimination is reinforced by articles in the popular press. But in math and science, bias is a technical term referring to improper data handling or choice of inputs. And indeed, the risks in AI go further than protected categories such as race and gender. Bias leads to wrong results, plain and simple. Whether bias leads to social discrimination or just to lost business opportunities and wasted money, organizations must be alert and adopt ways to avoid it.

  • An introduction to Markdown

    For a long time, I thought all the files I saw on GitLab and GitHub with an .md extension were written in a file type exclusively for developers. That changed a few weeks ago when I started using Markdown. It quickly became the most important tool in my daily work.

    Markdown makes my life easier. I just need to add a few symbols to what I'm already writing and, with the help of a browser extension or an open source program, I can transform my text into a variety of commonly used formats such as ODT, email (more on that later), PDF, and EPUB.

  • Intel Tightens Up Its AVX-512 Behavior For The LLVM Clang 10 Compiler

    When targeting Skylake-AVX512, Icelake-Client, Icelake-Server, Cascadelake, or Cooperlake with the LLVM Clang compiler where AVX-512 is supported, it will now default to preferring the 256-bit vector width rather than 512-bit with AVX-512. Unless 512-bit intrinsics are used in the source code, 512-bit ZMM registers will not be used since those operations lead to most processors running at a lower frequency state. On current generation processors, the performance gains of AVX-512 can often times be negated due to the AVX-512 frequency hits.

  • 2019.3 EAP 1

    The first Early Access Program (EAP) for PyCharm 2019.3 is now available to be downloaded from our website!

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