He has designed crossovers for homemade speakers all the way from the basic design to the PCB. He regularly repairs and repurposes old computers and hardware for whatever new project is at hand. He enjoys DIY projects, especially if they involve technology. He also uses Proxmox to self-host a variety of services, including a Jellyfin Media Server, an Airsonic music server, a handful of game servers, NextCloud, and two Windows virtual machines. He has been running video game servers from home for more than 10 years using Windows, Ubuntu, or Raspberry Pi OS. Nick's love of tinkering with computers extends beyond work. In college, Nick made extensive use of Fortran while pursuing a physics degree. Before How-To Geek, he used Python and C++ as a freelance programmer.
He has been using computers for 20 years - tinkering with everything from the UI to the Windows registry to device firmware. For example, if you type “o bir doktor” in Turkish, you’ll now get “she is a doctor” and “he is a doctor” as the gender-specific translations.Nick Lewis is a staff writer for How-To Geek. You’ll also get both translations when translating phrases and sentences from Turkish to English. Now you’ll get both a feminine and masculine translation for a single word-like “surgeon”-when translating from English into French, Italian, Portuguese or Spanish. For example: it would skew masculine for words like “strong” or “doctor,” and feminine for other words, like “nurse” or “beautiful.” So when the model produced one translation, it inadvertently replicated gender biases that already existed. Historically, it has provided only one translation for a query, even if the translation could have either a feminine or masculine form. Google Translate learns from hundreds of millions of already-translated examples from the web. Our latest development in this effort addresses gender bias by providing feminine and masculine translations for some gender-neutral words on the Google Translate website.
Over the course of this year, there’s been an effort across Google to promote fairness and reduce bias in machine learning.
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