Something really is wrong on the Internet. We should be more worried. The Washington Post

“Something is wrong on the internet,” declares trending in tech circles. But the issue isn’t Russian ads or Twitter harassers. It’s children’s videos.

The piece, by tech writer James Bridle, was published on the heels of a report from the New York Times that described disquieting problems with the popular YouTube Kids app. Parents have been handing their children an iPad to watch videos of Peppa Pig or Elsa from “Frozen,” only for the supposedly family-friendly platform to offer up some disturbing versions of the same. In clips camouflaged among more benign videos, Peppa drinks bleach instead of naming vegetables. Elsa might appear as a gore-covered zombie or even in a sexually compromising position with Spider-Man.

The phenomenon is alarming, to say the least, and YouTube has said that it’s in the process of implementing new filtering methods. But the source of the problem will remain. In fact, it’s the site’s most important tool — and increasingly, ours.

YouTube suggests search results and “up next” videos using proprietary algorithms: computer programs that, based on a particular set of guidelines and trained on vast sets of user data, determine what content to recommend or to hide from a particular user. They work well enough — the company claims that in the past 30 days, only 0.005 percent of YouTube Kids videos have been flagged as inappropriate. But as these latest reports show, no piece of code is perfect

Ethics, AI, Children, Bias, Biased DecisionsBrigitte BellanNovember 16, 2017Washington Post, Christine Emba, algorithms, you Tube, Machine Learning

Why AI Needs a Dose of Design Thinking - Deloitte/WSJ.com

Artificial intelligence technologies could reshape economies and societies, but more powerful algorithms do not automatically yield improved business or societal outcomes. Human-centered design thinking can help organizations get the most out of cognitive technologies.

Today’s artificial intelligence (AI) revolution has been made possible by the big data revolution. The machine learning algorithms researchers have been developing for decades, when cleverly applied to today’s web-scale data sets, can yield surprisingly good forms of intelligence. For instance, the United States Postal Service has long used neural network models to automatically read handwritten zip code digits. Today’s deep learning neural networks can be trained on millions of electronic photographs to identify faces, and similar algorithms may increasingly be used to navigate automobiles and identify tumors in X-rays. The IBM Watson information retrieval system could triumph on the game show “Jeopardy!” partly because most human knowledge is now stored electronically.