The Long Hard Road To Web 3.0
Welcome to this series of articles about the technical aspects behind Catalyst Network, written by the members of the Engineering team. Check out our work on GitHub and join us!
My name is James Kirby. I’ve been a developer for 15 years at various multinational companies, and currently, I’m the head of engineering at Atlas City and project lead of the Catalyst Network.
For the past year, we have been building a new project called the Catalyst Network, which is designed to be a full-stack Web3 solution, allowing users to do more than send transactions and restrictive smart contract logic.
Web3 is a new movement born out of the current broken web, and the rise in popularity in consensus protocols, where users are now demanding more privacy, more security and more control over their data.
How did we get here?
A brief evolution of the web
Web 1.0
A one-to-many online platform where a few businesses, organisations and individuals held a one-way dialogue with people over the web. They could pass on information in a variety of ways, but the interaction was limited. Users of Web 1.0 were mainly just consumers of media.
Web 1.0 predominantly funded itself through un-sophisticated ad-tech like banner ads and pop-ups.
Towards the end of the web1.0 era users got sick of this type of advertising, to the extent wherein 2003 lawsuits were filed to try and force web companies to stop pushing ads to users, but judges ruled in favour of the website operator.
Web 2.0
The central concept of Web 2.0 was the many-to-many user-curated content or social media. Websites became “platforms” in which users themselves curated content, and uploaded it to the platform, and shared them amongst their peers.
These platforms typically offer their services under the guise of “free to use”: users don’t pay a monetary value to use the platform, nor did they fund their operations through un-targeted ads.
For instance, YouTube and Facebook didn’t show their first ads until 2007. However, instead of money, we gave up something more significant.
Our data
Our privacy
And our freedom
In doing so, champions of Web 2.0 started the process of commodification of user data on their platforms.
Now here’s a quote:
Albeit this quote is from satire site The Onion; they do have an uncanny talent for saying it exactly how it is.
Retrospectively what is hilarious from this piece was that it published back in 2011, two years before the Snowden’s revelations. More on that in a bit.
Each time we interact over these Web 2.0 platforms, copies of your digital data go to these centralised services stored in walled gardens of servers and the cloud. Can we trust those people and institutions that store and manage this data against any form of corruption — internally or externally, on purpose or by accident?
Let’s look at us retail giant Target.
Target effectively data mined its way into the womb of every female customer.
In 2012 New York Times journalist Charles Duhigg spoke with Target data analysts, revealed Target assigns every customer a unique tracking ID number, tied to their credit card, name, or email address that becomes a bucket that stores a history of everything they’ve bought across the web and in-store.
Using that, the data analysts looked at historical buying data for all the ladies who had signed up for Target baby registries in the past.
After analysing the datasets, useful patterns started to emerge.
Lotions, for example.
Lots of people buy lotion, but analysts found that women on the baby registries were buying larger quantities of unscented lotion around the beginning of their second trimester. Another analyst noted that sometime in the first 20 weeks, pregnant women loaded up on supplements like calcium, magnesium and zinc.
Many shoppers purchase soap and cotton balls, but when someone suddenly starts buying lots of scent-free soap and extra-big bags of cotton balls, in addition to hand sanitisers and washcloths, it signals they could be getting close to their delivery date.
Analysts then could identify about 25 products that, when analysed together, allowed them to assign each shopper a “pregnancy prediction” score. More creepily, they could also estimate a pregnant woman’s due date to within a small window, all so that Target could send coupons timed to particular stages of her pregnancy.
A target analyst disclosed an example to the researcher.
“Take a fictional Target shopper named Jenny Ward, who is 23, lives in Atlanta and in March bought cocoa-butter lotion, a purse large enough to double as a diaper bag, zinc and magnesium supplements and a bright blue rug. There’s, say, an 87 per cent chance that she’s pregnant and that her delivery date is sometime in late August. And perhaps that it’s a boy based on the colour of that rug.”
So, Target started sending coupons for baby items to customers according to their pregnancy scores.
Sounds too good to be true, but it turns out it’s not so anecdotal, and an angry farther went to target once after his high school daughter received baby coupons in the mail, demanding to know why Target was sending these and saying it shouldn’t be encouraging teenage pregnancy and demanded an apology.
After confronting his daughter, it turns out she was pregnant and proceeded to apologise profusely to target.
Now, this is creepy enough, but can we go far as to say that data collection from web 2.0 platforms can influence people’s free thought.
Well, it can and has introducing Facebook the most prominent social experiment lab on the planet.
Let’s leave it here for now. We’ll carry on next week with the second part of this series.
This is my first article on Medium, so I’d appreciate your feedback.
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This article has been edited by Tony Vazz and sourced by all the team members of Catalyst Network.
