AI can help build more efficient crypto markets
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Artificial intelligence (AI) has gained tremendous traction in the last couple of months. Since the end of 2022, AI has become a household topic due to the mainstream adoption of OpenAI’s chatbot “ChatGPT” and its immediate, worldwide impact across industries and people’s lives.
In 2022, consultants at McKinsey found that AI adoption had stagnated in recent years. However, with the arrival of ChatGPT, adoption has increased significantly. According to OpenAI’s founder, Sam Altman, ChatGPT crossed over 100 million users in just two months, a milestone it took Facebook 4.5 years, Instagram 2.5 years and Twitter five years to achieve.
This article is part of CoinDesk’s “BUILD Week.” Marcello Mari is the CEO of SingularityDAO and Rafe Tariq is a senior quantum researcher at SingularityDAO Labs.
As we enter 2023, we see Microsoft and Google engaged in a fierce battle for AI dominance. They’re competing with rival chatbots, search optimization, and more—and Microsoft seems to be leading the way. The software giant gave OpenAI $1 billion in the initial stages of ChatGPT’s development, took a 46% stake in the company, and plans to integrate ChatGPT into the Edge browser and the Bing search engine, both of which are likely to revolutionize search and web browsing.
When you think about it, AI could finally allow Microsoft to overtake Google in an area the latter has dominated for years. OpenAI predicts that ChatGPT will generate revenues of $200 million by the end of 2023 and $1 billion by the end of 2024. It is quite possible that by 2030 AI will become the number one industry in terms of revenue generation and market capitalization.
See also: Crypto AI needs a showcase to know what is real | Opinion
As we move towards a future where AI is everywhere, inevitably replacing many human jobs, it is interesting to consider how this powerful form of computing can be used to maximize opportunities in the crypto industry. AI can be used to make crypto more efficient, and blockchain technologies can also be used to solve problems unique to machine learning.
Traditional AI methods applied to crypto
Sentiment analysis and cognitive distortion detection in social media
Sentiment analysis is a technique where natural language processing (NLP) algorithms are able to analyze text and attribute meaning to it, helping people understand whether there is a positive or a negative sentiment regarding a particular asset class.
In traditional finance, sentiment analysis was usually conducted over news media. But in the crypto market, by the time an update hits the news, it’s usually already too late to make money trading. This may explain the saying “buy the rumor, sell the news”, which means that a new market trend must be discovered on social media when it happens or even before it happens.
As we know, crypto markets without volatility would not be as attractive. The unpredictable movements of the crypto market play a decisive role in its dynamics. Therefore, there is a need for further development of AI and data frameworks to facilitate price prediction studies and applications.
These frameworks should be able to aggregate sentiment data from various channels, whether crypto-related or not, and should have an AI analytics framework that can integrate the latest developments in sentiment analysis research. It should also be able to distinguish a real person from a robot, as well as real conversations from orchestrated ones.
These frameworks will be able to detect so-called cognitive distortions on social media, such as catastrophizing (exaggerating the importance of a negative event: “because of this everything will dump”), prediction (pretending to know about the future: ” this will definitely happen”) and mind reading (pretending to know what others are thinking: “everyone knows it.”)
Predict market movements
AI has been used for decades in traditional finance to detect market dynamics before they occur. Traditionally, this has been achieved through sentiment analysis. But in cryptocurrency we can rely on statistical correlation between major coins or categories of coins. For example, in localized ecosystems like the decentralized exchange basket or AI-focused SingularityNET, which has multiple tokens, we see lag and correlative trading patterns emerge.
Due to rapid technological advances in hardware used to secure and mine decentralized networks (ie, the rise of GPU-based computing), the use of large deep learning models has become increasingly valuable for understanding price fluctuations. Extending machine learning and deep learning methods used in traditional finance to predict price swings or identify market regimes (ie whether we are in a bear or bull market) is one of the key areas of exploration for AI use cases in crypto.
A further area of research concerns the use of reinforcement learning, an AI technique that learns without human supervision (aka unsupervised learning) to better understand the impact of actions. This has applications in predicting slippage and price impact when assets are traded.
Trading bots/AI-based market creation
The AI team at SingularityDAO has conducted exploratory studies in market simulation and backtesting to improve the state of the art in quantifying market dynamics. A promising technology we have explored is the “adaptive multi-strategy agent” (AMSA) for market making. This basically provides an environment where different AI algorithms can buy and sell assets and backtest those trades, while evaluating the performance and effect trades have on the market.
These self-reinforcing trading algorithms can be seen as the next step in the evolution of traditional trading robots that are already widely adopted by traders and market makers on centralized exchanges. In other words, AI is being developed to help create more sophisticated automated market making systems. This contributes to the adoption of more robust decentralized trading systems, and can help traders rebalance their multi-asset portfolios.
Kryptonative AI problems
Effective monitoring of dynamic position and unit risk
Due to the increasing frequency in crypto markets of black swans (unpredictable events with potentially serious consequences), traditional methods of evaluating risk in trading positions have become outdated. In crypto, analysts must assess risks associated with liquidity movements across protocols, and this is virtually impossible to do manually given the large amount of data to be analyzed.
An AI approach can in turn augment human decision-making. AI algorithms can be used in conjunction with other methods commonly used to monitor the health of on-chain positions across all protocols, such as analysis of large wallet holders and liquidation risk. By gaining expertise and experience in both AI and decentralized finance (DeFi), it is possible to create new calculations that can provide easy-to-read signals of risk exposure taken across different protocols.
Furthermore, AI offers a significant amount of value and support to human analysts as the crypto industry becomes increasingly multi-protocol (with cross-blockchain development occurring even in bear markets), leading to a significant increase in complexity. Predictive and correlational risk methods are essential to prevent future Black Swan events, such as those that occurred with crypto exchange FTX and lending platform Celsius Network.
An emphasis on flow analysis, correlation and predictive analysis
After the fallout of Celsius and FTX, there was an increased need to develop methods to monitor events and factors that could lead to similar cases. Cryptoanalysts and data scientists explored a variety of approaches, from classic warning signals based on wallets and devices to more advanced AI-based capital flow aggregations.
Twitter alert are already using AI-based analytics platforms to uncover news stories before they cross over into mainstream crypto news. However, much can be done to simplify and expand these tools to be adopted by the wider market.
AI techniques for malicious device tagging and on-chain detection
In the crypto market, there is a constant game of identifying malicious entities on the chain, which requires the use of extremely large datasets. AI plays a critical role in this transparency effort, using state-of-the-art clustering, genetic programming and neural networks to trace these malicious entities to their on-chain aliases.
See also: Why crypto trading is important to the cryptocurrency industry | Opinion
As malicious users become more sophisticated at hiding their commitments to a device, we rely on advanced AI algorithms along with geographic and behavioral data to identify these wallets.
Far away and here today
Although AGI (artificial general intelligence) or an AI that is sentient is still a long way off, progress in the field in recent years has been remarkable. I strongly believe that in the future artificial intelligence will manage our crypto assets and ensure the safety and health of our wallet.
The integration with major language models such as ChatGPT has significantly accelerated this process and will make it easy and accessible to everyone. Crypto has the potential to create a new inclusive financial ecosystem and we have a once-in-a-lifetime opportunity to lead the way and compete with Big Tech companies.
