- Components of recorded intelligence
- Enhancing the data asset
Here’s a thought: Smart data (data that has been enhanced with information that allows it to be connected to other data in the Tinker Toy manner) may be fungible in ways that dumb data is not.
Can data be “fungible” for the sake of trade and reuse? Consider the following snippet from a Cointelegraph.com explainer:
“Divisible and non-unique fungible tokens or assets.” Fiat currencies, such as the dollar, are fungible: A $1 bill in New York City is equivalent to a $1 bill in Miami. A fungible token, like Bitcoin, may also be a cryptocurrency: 1 BTC is worth 1 BTC regardless of where it is issued.
“On the other hand, non-fungible assets are one-of-a-kind and non-divisible.” They should be regarded as a form of deed or title of possession to a one-of-a-kind, non-replicable item. A flight ticket, for example, is non-fungible since there cannot be another of the same sort due to its unique data. Because they are one-of-a-kind, a home, a boat, or a vehicle are non-fungible physical assets.”
Part of the issue with this concept of fungibility is its close relationship with currency. Assume you have a data flow. That data stream might contain one-of-a-kind, fungible assets.
In other words, unique assets that may be exchanged could be mixed in with non-unique data in the same data stream. Why would you want to conduct this type of mixing in the first place? Perhaps you’d like to encourage people to consume that info stream. Assume an advertiser wishes to display an advertisement or a pitchman wishes to present a pitch. Perhaps an NFT would be promised in exchange for listening to the pitch and offering feedback.
Behaviour and data assets
Data is certainly an asset, unless it is just exhaust that is not repurposed.
The AI Dilemma, written by Art Kleiner and Juliette Powell, is set to be published in August 2023. It is a book for business professionals.
I provided an interview for the book, and we had several email discussions about AI and data trends during the book’s creation and production phase. Recently, Art, whom I knew as the Editor-in-Chief of Strategy & Business, made the following observations:
If my reading is right (although it may not be), you’re stating that a data-centric design implies that “all data are assets and must be tagged as assets.” “What you reveal is automatically yours.” Whether you monetize it or not, it is connected back to you in a world that recognises data as an asset.
This would begin with corporations controlling their data as the decision point (the new unified data-first IT replacing the old fragmented algorithm-first IT), but it would not stop there. As data travelled through supply chains and transactions, data holders would feel obligated to embrace the standard in order to become interoperable.
Art and Juliette’s insights taught me that businesses can help technologists in more ways than they realise, especially when it comes to data.
Data as an organic, developing, and flowing resource
To many data scientists, “data” is silica, to use a material metaphor. It is both inert and inorganic. To produce electrically active silicon wafers, metal and heat must be added. Otherwise, it’s simply sand, and sand isn’t a renewable resource.
The organic, dynamic, interactive portrayal of the living world is made possible by increased intelligence in the data.
This is referred to as the Mirrorworld by Wired Co-Founder Kevin Kelly. Twitter, LinkedIn, and other social networks have served as launching pads for this type of interacting, growing, farming, and harvesting environment.
In some respects, the name “Mirrorworld” is misleading. Data does not simply reflect what it represents. It is essential to the digital environment.
These people are leveraging web concepts to wrap the planet in a changing, multi-use operating and resource sharing system. Data is a constantly changing resource that informs this operational and resource sharing system.
According to Roy Roebuck, an independent knowledge graph expert, the data evolution problem that organisations face has numerous levels. “Information is provided by data with added context or meta.” Each layer of awareness is constructed by providing context to the preceding layer. Humans, and perhaps A.I. constructions, would build their awareness beyond the knowledge layer.”
Components of recorded intelligence
Instead of thinking about how to automate business tasks by delegating them to machines and third parties, consider a ubiquitous, machine-assisted human-in-the-loop situation.
The challenge therefore becomes, how can we empower humans while also benefiting from machine-based efficacy and efficiency?
Enhancing the data asset
Data can represent its own utility, contextual relevance, monetization and exchange parameters, and other qualities. Agents and humans working together can be the actors who nurture and use data.
After all, the intelligence, relevance, and interoperability details may be found in the data, with the functions enabled by agents mediating interactions between machine and human actors. From an efficiency standpoint, the ideal situation is to construct intelligent data execution agents built to live and thrive in the online environment, using the capability of the broader environment.
Designing and creating once and using it everywhere benefits all ecosystem players. So, why not have a single ecosystem, or a virtualized representation of what existing, so that we can measure and manage each of its constituent pieces for efficiency, efficacy, and net impact on the living world?
