Artificial intelligence (AI) has received a lot of attention over the past few months. Since the end of 2022, with the explosion of ChatGPT (launched by artificial intelligence research lab OpenAI) and its impact on industries and People's Daily lives, artificial intelligence has become a household topic.

ChatGPT greatly increased the adoption rate of AI. In just two months, ChatGPT hit 100 million users, according to OpenAI founder Sam Altman. It took Facebook four and a half years, Instagram two and a half years, and Twitter five years to achieve this.

Artificial intelligence unleashes great application value

As 2023 begins, we see Microsoft and Google battling for dominance in artificial intelligence. Microsoft is bringing AI chatbots to its iPhone and Android Bing apps, and Google is showing off new AI chatbot search tools.

For now, Microsoft seems to be in the lead. The software giant invested $1 billion in OpenAI in 2019, giving it a 46% stake in the company, and plans to integrate ChatGPT into the Edge web browser and Bing search engine.

Come to think of it, Microsoft could end Google's dominance of search with artificial intelligence. OpenAI predicts ChatGPT will generate $200 million in revenue in 2023 and $1 billion by the end of 2024. By 2030, AI is likely to be the largest industry in terms of revenue and market capitalization.

In the future, artificial intelligence will be ubiquitous and replace many human jobs. In this context, it is interesting to consider how this powerful form of computing can be used to maximise opportunities in the crypto industry. AI can improve encryption efficiency, and blockchain technology in turn can help solve problems specific to machine learning.

Innovative applications of artificial intelligence in encryption

1. Effectively monitor dynamic positions and physical risks

Due to the increasing frequency of black swan events (unpredictable events with potentially serious consequences) in crypto markets, traditional methods of assessing the risk of trading positions have become obsolete. In the crypto space, analysts need to assess the risks associated with cross-protocol liquidity movements, which is almost manually impossible given the volume of data to analyze.

Ai can once again extend the range of human decision-making. Combined with other commonly used methods, AI can monitor the health of chain positions across all agreements and alert potential risks through easy-to-read signals.

In addition, because of the increasing number of protocols in the crypto industry, which makes the analysis work more complex, artificial intelligence can provide a lot of help to human analysts to ease the difficulty of the work.

2. Emphasize traffic analysis, correlation and predictive analysis

In the wake of Celsius and FTX events, there is an urgent need for the crypto industry to develop methods to monitor events and factors that could lead to similar situations. To this end, crypto analysts and data scientists have explored a range of approaches, such as representative wallet - and entity-based alerting signals, and AI-based capital flow summarization.

In addition, AI technology can be used to identify malicious operations on the chain.

Traditional AI use cases are introduced into the crypto space

1. Emotional analysis and cognitive distortion detection in social media

Sentiment analysis is a technique in which natural language processing (NLP) is able to analyze text and assign meaning to it to help humans understand whether they have positive or negative emotions about a particular asset class.

The traditional financial world often analyzes financial market sentiment based on news reports. But that doesn't work in the crypto industry, and if investors wait for the news to come out before investing, it's already too late. That might explain the meaning of the adage "buy the rumor, sell the news" -- that any new market trend is something to anticipate.

Crypto markets are notoriously attractive because of their vagaries. The unpredictable nature of the crypto market is an important driver of its growth. Therefore, there is a need to further develop artificial intelligence and data frameworks to drive price forecasting research and applications.

AI and data frameworks need to be able to collect sentiment data from a variety of sources, whether they are encrypted or not, and to integrate the latest developments in sentiment analysis research through an AI analytics framework. It also needs to be able to distinguish between real people and robots, real conversations and choreographed conversations.

In addition, the AI will be able to detect so-called cognitive distortions on social media, such as exaggerating the impact of negative events, thinking they can predict the future, and thinking they can read minds.

2. Predict market trends

For decades, AI has aided the development of traditional finance by predicting market dynamics. In the past, this was done through sentiment analysis. But in cryptocurrencies, we can predict market movements based on statistical correlations between major currencies or classes of currencies. For example, in localized ecosystems such as Curve, a decentralized exchange with multiple tokens, and SingularityNET, with an AI focus, we can see lagging and correlated trading patterns emerge.

Due to the rapid development of hardware technologies to secure and mine decentralized networks (i.e., the rise of GPU-based computing), the large-scale use of deep learning models becomes increasingly necessary in terms of understanding price fluctuations. Extending the machine learning and deep learning methods used in traditional finance to predict price movements or identify market mechanisms -- that is, whether we are in a bear or bull market -- will be a key use case for AI in crypto.

There is also the application of reinforcement learning, which is an artificial intelligence technique used to describe and solve the problem of agents learning strategies to maximize returns or achieve specific goals in the process of interacting with the environment. Different from supervised learning and unsupervised learning, reinforcement learning does not require any data to be given in advance. Instead, it obtains learning information and updates model parameters by receiving environment's reward (feedback) for actions. Reinforcement learning can be applied to predicting slippage points (where the transaction price differs from the expected price at the time the order was placed) and price shocks when an asset trades.

3. Trading bots/AI-based market making

The AI team at SingularityDAO has conducted exploratory research in the areas of market simulation and backtesting, improving the technology for quantifying market dynamics. One of the techniques we explored for making markets is called Adaptive Multi-strategy Agents (AMSA). It provides an environment where different AI algorithms can buy and sell assets, backtest those trades, and assess the value of the trades and their impact on the market.

You can think of these self-reinforcing trading algorithms as a step up from traditional trading bots. In other words, AI was developed to help create more sophisticated automated market maker systems. This facilitates the development of more robust decentralized trading systems and helps traders rebalance their multi-asset portfolios.

summary

While we're still a long way from true AGI (general artificial intelligence) or sentient AI, there's no denying that the field has evolved rapidly over the past few years. So there's reason to believe that one day, AI will be able to manage our crypto funds and keep our wallets safe.

Integration with large language models such as ChatGPT will greatly accelerate this process, making encrypted networks easily accessible to everyone and potentially creating a new inclusive financial ecosystem.