Ever heard of Tim Berners-Lee? Sure, you have. After all, he invented the internet.
What you may also be coming to know about Tim, is that he invented the internet 3.0…aka Web 3.0…aka the Semantic Web…aka the Spatial Web. Here’s what Sir Tim says about the future of the internet:
"I have a dream for the Web [in which computers] become capable of analyzing all the data on the Web – the content, links, and transactions between people and computers. A ‘Semantic Web’, which should make this possible, has yet to emerge, but when it does, the day-to-day mechanisms of trade, bureaucracy and our daily lives will be handled by machines talking to machines. The ‘intelligent agents’ people have touted for ages will finally materialize."
But what exactly is Web 3.0?
The diversity in terminology only speaks to the adolescence Web 3.0 finds itself in today. In fact, many thought leaders are still developing a clear understanding of what all entails Web 3.0.
Some define it as broadly as
"…a term used to describe the future of the World Wide Web"
While others provide more concrete criteria such as semantics, AI, 3D, and ubiquity.
Others still provide even narrower definitions focused more exclusively on the way in which current business models may be able to take advantage of this future state using spatial technologies (I’ll dive into some of these definitions in more detail below).
Whatever you want to call it, however you want to define it, there are some significant efforts underway to begin to realize Tim’s foretelling of the future of the worldwide web and it has some important implications for data scientists.
I peered into the abyss to begin to get a handle on Web 3.0 and its potential implications for the profession I love. What looked back at me was the glimmer of an opportunity. A love letter from the future, yearning to be realized, begging us to pave the way.
My Understanding…For Now
All drama aside, my understanding of Web 3.0 is still evolving as I learn and as I see new products enter the space with Web 3.0 marketing tags.
I first began to take notice of the concept after reading a few articles by Tim Denning on content creation with Web 3.0, like this one here (hey look, another smart Tim 😊 ).
Tim also writes about one of the most prominent use-cases for Web 3.0 today, cryptocurrencies. Cryptocurrencies are good examples of the potential of Web 3.0 because they are a decentralized and fully transparent means of exchanging value.
Cryptos remove the need for a centralized bank or government controlling the flow of currencies. Which gets us back to understanding what exactly Web 3.0 means.
Two Definitions, At Least
As I see it, there are two different definitions beginning to take shape. One definition is an idealistic vision of a future state of the technology of the internet (the Web3 Tech Stack). It is this ideal that most closely aligns with Berners-Lee’s vision.
The second is a more direct application of existing technologies that have yet to become the norm for how businesses deliver value to consumers.
Let’s tackle the latter first because it is not where I see the most value for data scientists.
Several prominent consulting groups like Deloitte, are pushing the narrative that Web 3.0 is synonymous with the Spatial Web. In essence, the Spatial Web is a future state whereby businesses can connect customer data to IoT devices, and the geographies customers live in to bring experiences into 3-dimensional space. Think Pokémon Go becoming more the norm.
It makes sense why consulting firms that sell services to existing businesses would prefer this definition. It’s achievable with today’s technology and it also fits within existing business models, where businesses continue to hoard user data for monetization.
Blockchain would still be useful in this scenario whereby businesses work with IoT providers to build blockchain services that connect those devices, but businesses still leverage their centralized data to blend and deliver user experiences that are mediated by internally derived insights.
It is this hyper-user driven and immersive experience that have led some to label existing companies like Amazon and Salesforce as Web 3.0 Companies, they are not.
This also isn’t the ideal that Berners-Lee envisions. The ideal state for Web 3.0 is much more nuanced and requires significant changes to existing web infrastructures, the applications that run on them, and to traditional businesses models.
The ideal state includes a decentralized store of data like blockchain, where users control their own information and provide tokens of access to the businesses who want to use that data. Blockchain’s cryptographic and distributed technology ensures both security and data privacy. But in order to be Web 3.0, it must be ubiquitous for all user data.
Because data are now stored in a distributed fashion, across the entirety of the internet, AI can be deployed to understand user needs more fully by developing language models that bring semantic understanding because queries are tied to user interactions.
In other words, users can allow an AI solution access to their data in order to enrich and further personalize their experience. In this context, the AI would have access to the data deemed relevant by the user rather than the data available from a central repository held by a company.
And this is where data scientists may have a huge opportunity in this idealized future state.
Matchmaking Data Scientists with Web 3.0
You see, Web 3.0 is all about user-centrism where user data is distributed across blockchain enabled storage technologies. Applications are distributed across these same blockchain platforms and so users can opt to allow those apps (or Dapps as they’re called) access to their data, creating richer, more relevant experiences. Users no longer need to request data from businesses because it is already controlled by them and stored on the blockchain.
Just as this new user-level ownership of data benefits content creators like Tim Denning, it may also benefit data scientists. For example, a future consortium of data scientists could work with users to purchase access to data that used to be owned by companies to build models with that data that enable new experiences. The data can be blended across dApps and devices because the data are all stored on a blockchain and connected to a user, not separate companies, and therefore solutions can be tailored specifically to the user.
These AI solutions can, in turn, be sold back as dApps to users who may benefit from using them. In this way both the data generators (users) and the data "understanders" (data scientists) monetarily benefit from the relationship.
But is this future too far away to get all weak-in-the-knees about?
One company I am following that is making some significant strides towards Web 3.0 for data scientists is the Ocean Protocol. To be clear, I am in no way affiliated with this company. I just find their platform to be of interest for data scientists.
The Ocean Protocol is providing marketplaces for data aggregators like businesses and data scientists to come together to buy and sell data assets in a decentralized framework.
Moreover, Ocean Protocol is enabling private businesses to sell their data assets on the marketplace without having to share the data outside of its firewalls. The Ocean Protocol employs a "Compute-to-Data" orchestration that allows AI models to train on private data.
Imagine being able to train a disease model using data from multiple major hospital networks without ever having to access the data itself, just the metadata.
Another exciting opportunity for data scientists in this new data protocol is the opportunity to buy data, blend it with other data, enhance it with machine learning models and sell it back in its enhanced form.
One Final Thought on Platforms Like the Ocean Protocol
What this all comes down to is the potential for individual data scientists to play a bigger role in the global economy as content/model/data creators who can be compensated for their individual efforts in marketplaces like the Ocean Protocol.
It also means that AI development may itself become decentralized.
Thus, the next great advancement in AI in this new distributed and user-owned data framework may be something more akin to meta-AI. That is, AI that can consume and organize other AI models much like the brain is organized around an interconnected network of different functional regions.
To push this analogy one small step too far, data scientists become the new neurons of the architecting of the internet that will work to organize those neurons into functional areas (groups of functionally equivalent AI models), connect them with other functional areas to coordinate them, and help us to solve ever more complex problems.
My Understanding…Tomorrow
As I said, I am still learning. I am certain I have missed something or may even have misinterpreted something about this new space. For me writing is understanding and so I share with you my understanding for now with the understanding that it may not be my understanding tomorrow.
Web 3.0 is still very new and lots of changes are certain to come. I will continue to follow and participate in this new framework. To experiment with the potential value for Data Science that Web 3.0 may help realize.
Like engaging to learn more about data science? Join me.