We present POTATO, the Portable text annotation tool, a free, fully open-sourced annotation system that 1) supports labeling many types of text and multimodal data; 2) offers easy-to-configure features to maximize the productivity of both deployers and annotators (convenient templates for common ML/NLP tasks, active learning, keypress shortcuts, keyword highlights, tooltips); and 3) supports a high degree of customization (editable UI, inserting pre-screening questions, attention and qualification tests). Experiments over two annotation tasks suggest that POTATO improves labeling speed through its specially-designed productivity features, especially for long documents and complex tasks. POTATO is available at https://github.com/davidjurgens/potato and will continue to be updated.

In 2016, World Economic Forum (WEF) founder and Executive Chairman Klaus Schwab proclaimed the fourth industrial revolution as a distinct evolution from its predecessor because of the rapid onset of ubiquitous change. This revolution -- the current environment in which disruptive technologies such as artificial intelligence, cloud computing and the "internet of things," among others -- is profoundly changing the way we live and work. The complexity and scale of such change have seen the need for new means and approaches to linking intelligence, understanding and specialists at the global level. Transformation Maps, a collaborative digital tool developed by the WEF available in English, Mandarin, Spanish, Arabic and Japanese that harnesses knowledge, charts interactions and analyzes links between industries, countries and issues that are shaping the world, may very well be the platform to do so. According to Jeremy Jurgens, managing director and head of knowledge and digital engagement at the Swiss-based non-profit, Transformation Maps are certainly a solution made only available because of the accelerated change brought about as part of the current industrial revolution.

Unfortunately, the WEF has withheld some of the tool's coolest features--including a "Dynamic Briefing" button that generates a multi-page dossier on a given subject--from the public release, although Jurgens says some of these may be available to paying users in the future.

Stanford University AI researchers have created a "socially equitable" natural language processing (NLP) tool they say improves upon off-the-shelf AI solutions used today that fail to account for things like regional dialects, slang, or the natural way people talk when they regularly speak more than one language. In a paper published late last week, researchers found Equilid to be more accurate than commonly used identification tools like langid.py and Google's CLD2. Popular language identification tools, the paper argues, draw on a "European-centric corpora" of the written word, as well as websites, Wikipedia, and newswires, methods that may not best represent the way people actually talk. Language identification is a form of NLP used for things like serving up Google search results or even tracking social media chatter to make predictions. Equilid was made to better understand slang, regional dialects, and the natural way people communicate online when they speak more than one language, like, say, the 90 million English speakers in the Philippines who may regularly switch between English and Tagalog.