Take any industry and you’ll see AI hailed as its next big thing. The cryptocurrency and blockchain industry is no exception.
And just like in other sectors, AI is surrounded by a lot of hype. More projects use this buzzword just for the sake of attracting funding (research shows that it brings 15-50% more investor dollars) than there are genuine applications of AI in crypto.
So, is it all just hype or is AI on its way to making human crypto traders obsolete? Here’s everything you need to know about the application of AI technologies in the crypto market.
Why bring AI and blockchain together?
AI and blockchain share many characteristics: they’re emerging technologies that have been around for a while and both deal with data and value. Blockchain brings a secure solution for storing and sharing data, while AI analyzes it and generates insights that create value.
It’s clear that these two technologies had to collide in some way. Some people believe that this would bring to life a more advanced machine learning blockchain-powered system.
Take Microsoft’s Decentralized & Collaborative AI on the Blockchain framework as an example. The project’s goal is increasing collaborations with the AI community and retraining machine learning models with valuable datasets on public blockchains. These models would be made free for public use.
3 facts about AI + crypto
Fact 1: The hype is real
There are very few companies that actually use AI in crypto projects as promised. Many projects out there are pure hype because small startups are way too inexperienced in the industry to be for real. We’re talking about bringing two types of cutting-edge technical expertise together. How can a startup attract such specialists (or pay their huge salaries)?
Fact 2: That hype can still lead to a bright future
Take a closer look at the AI + crypto companies, and you’ll see that many of them raise money via initial coin offerings (ICOs). This means that the solutions they’re looking to build have been evaluated just as a startup’s offering gets evaluated by VCs. So it’s not completely unlikely that these companies will become successful in the future.
Fact 3: It’s all about data
Companies use AI models to classify, analyze, and predict things using relevant data sets. Blockchain is all about making data accessible in the safest possible way using decentralization.
Data is the central component to both technologies that open the door to a collaborative and secure effort towards data sharing and processing. They both strive to make data trustworthy so that insights extracted from it actually correspond to reality.
How is AI applied in the cryptosphere?
According to CryptoSlate, AI coins – that is, cryptocurrencies, coins, and tokens that are in any way connected to the AI industry – represent $339.81 million in 24h volume.
AI holds a promise primarily in the area of automated trading. This is particularly true for two areas: high-frequency trading and trading via APIs connected to exchanges.
High-frequency trading (HFT)
It builds on the analysis of technical indicators across multiple exchanges and responds to key trades faster than the rest of the market. For instance, if a trader placed a large Bitcoin buy order on Kraken, the HFT algorithm would pick up on it and execute an order on another exchange to take advantage of the instant price spike.
Trading through APIs
Another area of automated trading where AI comes in handy is trading through APIs connected to leading exchanges. Imagine that you’re a trader. You can select the indicators like EMA or RSI that you want the software to take into account during its decision-making process and pick the desired timeframe. The AI then implements trades within the parameters you chose automatically. Afterward, you can backtest your settings and optimize them. AI’s performance is only going to improve because the algorithms will get exposed to more and more data.
Related: sentiment analysis
Another use case of AI relevant to crypto markets is software for sentiment analysis. It helps traders to sift through the social media chatter, understand how the crypto community feels about a project, and then consider these insights when making a decision.
Examples of AI cryptocurrencies projects
Based in the United Kingdom, Fetch.ai aims to integrate blockchain, data sharing, and AI to solve common, everyday problems. Backed by noteworthy advisors, Fetch.ai created the Open Economic Framework (OEF) – a decentralized search and value exchange platform for a broad range of industries. The company built its blockchain from the ground up and claims to use a smart ledger that can support more than 30k transactions per second (just perfect for mass adoption scenarios).
Potential use cases of Fetch.ai include:
- Financial services – Decentralized financial exchange
- Mobility and transportation – electric vehicle infrastructure, e-mobility, smart parking, traffic congestion management
- Energy – Measuring and analyzing energy consumption
- Supply chain
A protocol for coordinating, discovering, and transacting AI algorithms, SingularityNET created a decentralized global market for all kinds of AI services. All the parties of the marketplace own their data and the role of SingularityNET is helping to connect customers with developers working on AIs in niche areas.
Since a decentralized market comes at a low overhead cost, it can serve a lot of niche markets effectively. And that includes all kinds of AI algorithms. But that’s not everything. In SingularityNET, the AIs can exchange data and make requests between each other. It’s like a massive economy of AI minds.
In 2017, SingularityNET announced the Token Sale for the AGI Token, receiving over $150 million in pledged orders from circa 10k registrants. The project also won institutional backing from Blockchain Global, DigitalX, and Taas.Fund.
The goal of DxChain is to combine big data with machine learning to store, trade, and compute the said big data in a decentralized application. The DxChain platform works like a data market, allowing companies to quickly transfer data back and forth. This would enable them to build custom applications for a wide range of use cases, from business operations to intelligence. By combining blockchain, data storage, and AI, DxChain is looking to “change the fundamental meaning of the internet.”
The key competitive advantages of this project are Provable Data Computing, a chains-on-chain architecture that orchestrates a master chain and two side chains (data and computing), Hadoop migration and support for business data analytics requirements.
What are the potential use cases of DxChain?
- Smart cities – DxChain helps in developing solutions that monitor and manage smart city implementations using machine learning.
- Healthcare – Transferring and storage of healthcare records and information, encrypted and stored on the blockchain.
- AI model training – Developers can leverage the platform to build their own machine learning Dapps.
How to develop an AI cryptocurrency
The core of an AI cryptocurrency is a smart contract. This means that the first step to launching an AI crypto project is getting your smart contracts in order.
In a typical scenario, development teams follow this flow:
- Writing a smart contract
- Carrying out an internal code review to catch bugs and apply fixes early on
- Next, it’s time for internal QA and testing on the test networks
- Carrying out an external audit of the smart contract
- Deployment on a production network
The future of AI cryptocurrencies
Despite what many believe, AI isn’t a silver bullet that can be freely applied to any aspect of the cryptocurrency industry. It’s also not going to replace smart human traders overnight.
Still, the technology is slowly but surely penetrating the sector, and ignoring its growing influence would be a mistake. From enabling faster order execution and identifying bots to powering the development of AI cryptocurrencies, the AI overlords are here to stay.
We might soon see AI running on top of cryptocurrency systems with capabilities like the creation of brand-new financial products or increasing machine learning capacities.
What do you think about the emergence of AI in crypto? Do you think it’s just hype or the real deal? Share your thoughts in the comments section; I look forward to hearing your thoughts on this.