An interesting article, original link
Ever heard of Tim Berners-Lee? Of course you have. After all, he invented the internet.
What else you might know about Tim is that he invented Internet 3.0… also known as Web 3.0… also known as the semantic web… also known as the spatial web. Here is Sir Tim’s take on the future of the internet:
“I have a dream that the web [the computers on it] will be able to analyze all the data on the web – the content, links, and transactions between people and computers. A ‘semantic web,’ which should make this possible, has not yet emerged, but when it does, the day-to-day mechanisms of trade, bureaucracy and our daily lives will be handled by machine talking to machine. The intelligent agents that people have touted for ages will have finally arrived.”
What exactly is Web 3.0?
The diversity of terminology is only fitting for Web 3.0 today. In fact, many thought leaders are still working to clearly understand the full meaning of Web 3.0. Some define it as
“…a term used to describe the future of the World Wide Web”
while others provide more specific criteria, such as semantic, artificial intelligence, 3D and ubiquity.
Still others provide an even narrower definition, more focused on the ways current business models can leverage spatial technologies to exploit this future state (I’ll explore some of these definitions in more detail below).
No matter what you want to call it, no matter how you want to define it, there are some major efforts underway to begin realizing Tim’s prophecy for the future of the World Wide Web, and it has some important implications for data scientists.
I stared into the abyss and began to understand Web 3.0 and its potential impact on the profession I love. Looking back, I saw a glimmer of opportunity. A love letter from the future, yearning to be realized, pleading for us to pave the way.
My current understanding
Dramatics aside, my understanding of Web 3.0 continues to evolve as I learn and as I see new products entering the space with Web 3.0 marketing labels.
After reading several articles by Tim Denning about creating content using Web 3.0, I first began to notice this concept, like this article here (hey, look, another clever Tim 😊).
Tim has also written about one of the most prominent use cases of Web 3.0 today, which is cryptocurrency. Cryptocurrencies are a good example of Web 3.0’s potential because they are a decentralized and completely transparent means of value exchange.
Cryptocurrency eliminates the need for central banks or governments that control the flow of money. This lets us re-understand what Web 3.0 really means.
Two definitions, at least
In my view, there are two different definitions beginning to take shape. One definition is an idealistic vision of the future state of internet technology (Web3 technology stack). This is the ideal that most closely aligns with Berners-Lee’s vision.
The second is a more direct application of existing technologies that have not yet become the norm in how enterprises deliver value to consumers.
Let me address the latter first, because I don’t think it’s where data scientists will find the most value.
Several prominent consulting firms like Deloitte are pushing the argument that Web 3.0 is synonymous with the spatial web. Essentially, the spatial web is a future state where enterprises can connect customer data to Internet of Things devices and connect the geographic locations where customers live to three-dimensional space. Think Pokémon Go increasingly becoming the norm.
It makes sense why consulting firms selling services to existing enterprises prefer this definition. It’s achievable with today’s technology and also fits into existing business models, where enterprises continue to hoard user data to monetize.
In this case, blockchain is still useful, with enterprises working with IoT providers to build blockchain services that connect these devices, but enterprises still leverage their centralized data to mix and deliver user experiences mediated by internally-derived insights.
It is this hyper user-driven and immersive experience that has led some to label existing companies like Amazon and Salesforce as Web 3.0 companies, but they are not.
Nor is this the idealized case that Berners-Lee envisioned. The idealized state of Web 3.0 is much more subtle and requires major changes to the existing web infrastructure, the applications that run on it, and traditional business models.
Because data is now stored in a distributed way across the internet, AI can be deployed to more comprehensively understand user needs by developing language models, because queries are associated with user interactions.
In other words, users could allow AI solutions to access their data to enrich and further personalize their experience. In this case, AI would have access to data the user deems relevant, rather than data available in a central repository held by a company.
This is where a data scientist could have tremendous opportunity in this idealized future state.
Pairing data scientists with Web 3.0
You see, Web 3.0 is entirely user-centric, with user data distributed across blockchain-enabled storage technology. Applications are distributed across these same blockchain platforms, so users can choose to allow these applications (called dApps) to access their data, thereby creating richer, more relevant experiences. Users no longer need to ask enterprises for data, because it is already under their control and stored on the blockchain.
Just as this new user-level data ownership could benefit content creators like Tim Denning, it could also benefit data scientists. For example, a future consortium of data scientists could work with users to purchase access to data that was previously owned by companies, to use that data to build models that enable new experiences. Data can be blended across dApps and devices because the data is all stored on the blockchain and connected to the user, not individual companies, making it possible to tailor solutions specifically for the user.
In turn, these AI solutions could be sold as dApps to users who could benefit from using them. In this way, both the data generator (the user) and the data “understander” (the data scientist) benefit from the relationship.
But is this future too far away to trouble all the weak ones with?
A company I’m watching is making a major stride for data scientists toward Web 3.0, and it’s Ocean Protocol. To be clear, I have no association with this company. I simply find their platform of interest to data scientists.
Ocean Protocol provides a marketplace for enterprises and data aggregators like data scientists to jointly buy and sell data assets within a decentralized framework.
Furthermore, Ocean Protocol enables private enterprises to sell their data assets on the marketplace without sharing the data outside their firewall. Ocean Protocol employs a “compute-to-data” orchestration that allows AI models to be trained on private data.
Imagine being able to train disease models using data from multiple major hospital networks without accessing the data itself, only the metadata.
Final thoughts on Ocean Protocol and similar platforms
It all comes down to the potential for individual data scientists to play a larger role in the global economy as content/model/data creators, and they can be compensated for their individual efforts on marketplaces like Ocean Protocol.
It also means that AI development itself could become decentralized.
Thus, the next major breakthrough in AI within this new distributed and user-owned data framework might look more like meta-AI. That is, AI that can use and organize other AI models like a brain is organized around different functional regions, interconnected networks around different functional areas.
To push the metaphor too far, data scientists become the new neurons of internet architecture, which will work to organize these neurons into functional areas (functionally equivalent AI model groups), connect them to other functional areas to coordinate them, and help us solve increasingly complex problems.
My understanding… tomorrow
As I said, I’m still learning. I’m certain I’ve missed something, and may even have misunderstood some things in this new space. For me, writing is a form of understanding, so I share with you my understanding now, and understand that tomorrow may not be my understanding.
Web 3.0 is still very new, and much change will certainly come. I will continue to watch and participate in this new framework. Experimenting with the potential value of data science that Web 3.0 may enable.