When we talk about real-time data, what we refer to is information that becomes available as soon as it’s created and acquired. Rather than being stored, data is forwarded directly to an application as soon as it’s collected and is made immediately available – without any lag – to support live, in-the-moment decision-making.
Real-time data is at work in virtually every aspect of our lives already, powering everything from bank transactions to GPS to emergency maps created when a disaster occurs.
The defining characteristic of real-time data is time sensitivity. Real-time data and its associated insights expire incredibly quickly. So, to make the most of it, it must be analysed and capitalised on without delay.
One example is nautical navigation software, which must gather hundreds of thousands of data points per second to provide weather, wave, and wind data that is accurate to the minute. To do otherwise is to endanger people whose lives depend on this data, like ship crews.
Another example is patient monitoring at a major hospital. Devices transmit patient data – like heartbeat and respiratory rate, blood pressure, or oxygen saturation – to cloud-based software. If any of these vital indicators drop below a certain threshold, then alerts must go out to hospital staff, who can then respond quickly to the issue and decide how to proceed.
By providing more actionable insights, real-time data and analytics empower organisations to make better decisions more quickly.
Let’s imagine a stock trading algorithm that’s mis-timing the market and selling too late or purchasing too early. Without real-time data, this issue would only be detected and resolved after it occurred. But with real-time data and analytics, the problem can be identified and fixed almost immediately.
Real-time data for autonomous decisions
While real time data is already a part of our lives, there is still a lot of room for improvement—and there’s a lot of promise regarding its integration with other hot technologies, like blockchain and AI.
By combining the three technologies, it’s possible to create potentially game-changing applications that not only understand what’s happening in the world immediately, but can actually make decisions and take action on those events, in a fully automated and, better yet, decentralised way. It’s the promise of truly autonomous, intelligent applications that require little to no human input.
Today’s blockchain networks already host autonomous applications that make use of ‘smart contracts,’ which are self-executing agreements programmed to take actions when specific conditions are met. The most popular applications for this technology can be found in decentralised finance, like a lending and borrowing protocol that enables anyone to take out a cryptocurrency loan by depositing collateral into a smart contract.
As soon as the collateral is deposited, the funds will be loaned to the user automatically. Should the borrower default on the repayments, the underlying smart contract will liquidate the loan, distributing the collateral among those who provided funds to the protocol’s liquidity pool.
Decentralised applications are intriguing because of the way they make use of real-time data autonomously, eliminating the middleman. Yet their potential has so far been held back by a major limitation. The smart contacts that power them just aren’t that smart, as they can only receive and act on blockchain-based data.
This is where artificial intelligence systems come into play, paving the way for a new kind of innovation known as ‘intelligent contracts’ powered by large language models.
This is the concept behind GenLayer, a new blockchain project that’s integrated with generative AI. Its intelligent contracts are similar to traditional smart contracts, but the difference is they really are quite smart. They can process natural language as well as code; they can access the internet and know what’s going on in the real world; and they can use what they learn to make subjective decisions.
To explain the difference between smart and intelligent contracts, GenLayer draws a comparison between a simple vending machine and a personal assistant. With a vending machine, you simply insert a coin (the input), select the product you want (action), and wait for the machine to spit out the item (output) according to how the machine has been programmed.
The vending machine has only been designed to perform one specific action and it can only follow its pre-programmed instructions. On the other hand, a personal assistant can do more. Being human (and intelligent), they can understand instructions in different forms and execute an almost-unlimited range of commands based on those instructions. So, unlike the vending machine, the personal assistant can adapt and take different actions—without being pre-programmed to do anything.
Intelligent contracts make intelligent apps
Using intelligent contracts, the opportunities for dApp (distributed applications) developers are almost endless. They’ll be able to build dApps that can search the internet, understand the world around them, and respond to events in local weather reports, sports results or financial markets—and much more besides.
Possible examples include an insurance protocol dApp that automatically pays out damages to claimants in real-time, based on the real world information it receives to verify their claim. Or, a sports betting app could immediately pay out the winnings to a punter who bets on the correct horse. In DeFi, the applications of intelligent contracts extend to on-chain verification, uncollateralised lending, and interest rates that automatically adjust based on market conditions.
AI, blockchain, and real-time data have proven to be revolutionary technologies, and it’s only recently that the technology industry has begun to explore what can happen when the three technologies intersect.
It’s a nascent sector that’s sure to be the subject of much attention in the months and years to come, but already, GenLayer’s intelligent contracts are paving the way for some truly innovative use-cases.
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