Learning a little every day

This article almost got lost among all of the 144 (!) browser tabs on my phone. In it, Benedict Evans highlights how a few different technical developments are coming together allowing computers to see

The combination of a flood of cheap image sensors coming out of the smartphone supply chain with computer vision based on machine learning means that all sorts of specialized inputs are being replaced by imaging plus Machine Learning.

It will take some years, but computers will learn to understand and act upon visual inputs, instead of just capturing them. And that will change computing again.

In Put down the self-help books. Resilience is not a DIY endeavour, Michael Ungar explains which factors are important for personal resilience:

Striving for personal transformation will not make us better when our families, workplaces, communities, health-care providers and governments fail to provide us with sufficient care and support. The science shows that all the internal resources we can muster are seldom of much use without a nurturing environment. Furthermore, if those resources are not immediately at hand, we are better off trying to change our world to gain those resources than we are trying to change ourselves.

Do read the entire article.

Craig Newmark, nerd en oprichter van Craigslist, over social media:

“Outrage is profitable. Most online outrage is faked for profit”

Maar hij blijft ervan overtuigd dat het internet toch een positieve ontwikkeling is:

“It allows people of goodwill to get together and work together for common good. Bad actors are much louder, they make for more sensational news and we’re seeing a period of that now. The US, in a way, is lucky. Bad actors interfering with our elections may have had some success but their success is not complete and it means that people of goodwill are fighting back vigorously.”

Just read the W3C Ethical Web Principles:

The web should be a platform that helps people and provides a net positive social benefit. As we continue to evolve the web platform, we must therefore consider the ethical implications of our work. The web must be for good.


Cory Doctorow vat op Boing Boing onderzoek van Citizen Lab samen in ‘How Wechat censors images in private chat’:

Wechat maintains a massive index of the MD5 hashes of every image that Chinese censors have prohibited. When a user sends another user an image that matches one of these hashes, it’s recognized and blocked at the server before it is transmitted to the recipient, with neither the recipient or the sender being informed that the censorship has taken place.

Separately, all images not recognized in the hash database are processed out-of-band. This processing includes checking for bitmaps representing text (to catch things like photos of banned articles) and also to see whether it is a partial match for an already-banned image (if it’s been resized, transformed, etc). Anything that is found to be “harmful content” (including material critical of the Chinese state) is removed from the chat on the sender and recipients’ devices and the hash of that image is added to the blocklist.

In de analyse van Citizen Lab staan meer details:

  • WeChat implements realtime, automatic censorship of chat images based on text contained in images and on an image’s visual similarity to those on a blacklist
  • WeChat facilitates realtime filtering by maintaining a hash index populated by MD5 hashes of images sent by users of the chat platform
  • We compare levels of filtering across WeChat’s Moments, group chat, and 1-to-1 chat features and find that each has different images censored; we find that Moments and group chat are generally more heavily filtered than 1-to-1
  • WeChat targets predominantly political content including images pertaining to government and social resistance
  • WeChat’s image censorship is reactive to news events; we found censored images covering a wide range of events, including the arrest of Huawei’s CFO, the Sino-US Trade War, and the 2018 US Midterm Elections