The Battle of AI Supremacy

As we step into the dawn of 2023, we witness a no-holds-barred battle between two tech behemoths, Microsoft and Google, for the crown of AI supremacy. But it seems that Microsoft, long seen as the underdog in the battle for tech supremacy, may have finally found the trump card it needs to dethrone Google: The integration of ChatGPT, a language model that has set the industry abuzz with its remarkable ability to generate almost any type of text, with its search engine Bing. In a bold gambit, the software behemoth splurged a cool $1 billion in ChatGPT’s development, securing a mammoth 46% stake in OpenAI.

Indeed, ChatGPT is projected to rake in a staggering $200 million in revenue by the close of 2023, with that figure ballooning to a mind-boggling $1 billion just one year later. This is no mere flash in the pan — analysts predict that AI could very well emerge as the undisputed king of revenue generation by 2030.

The crypto industry is no stranger to disruption, and AI could be the catalyst that takes things to the next level. By leveraging the power of machine learning, it’s possible to automate a variety of tasks, from algorithmic trading to fraud detection, making the entire crypto ecosystem more efficient and secure.

Improving Existing AI Methods to Boost Efficiency of Crypto Trading

Making Sense of Sentiment Analysis

To identify the behavioral traits of the market, a research method called Sentiment Analysis is usually used. Sentiment analysis, powered by natural language processing algorithms, helps investors make informed investment decisions based on the overall sentiment, whether positive or negative, towards a particular asset class.

But with the crypto market moving at warp speed, traditional sentiment analysis methods relying on news media are as outdated as a flip phone. Also, the crypto market is incredibly volatile and can rapidly change the sentiment dynamics. That’s why we need to develop new AI frameworks to facilitate more accurate sentiment predictions (and thus price prediction to certain extent).

We also must tread carefully in this social media jungle, where bots and trolls lurk around every corner, ready to pounce on unsuspecting traders. For every genuine conversation, there are ten bots spewing nonsense and twenty trolls spreading FUD. (And when it comes to crypto, FUD can be more contagious than a pandemic).

To distinguish genuine opinions from counterfeit ones, we must construct AI trading frameworks that can identify such distortions on social media, while incorporating the latest advancements in sentiment analysis research.

Cognitive distortions, such as catastrophizing — where the impact of a negative event is exaggerated OR narrative of “everything will nuke”, fortune-telling — where individuals feign knowledge of the future, and mind-reading — where people pretend to know what other investors are thinking, often lead to skewed perceptions that can affect the market significantly. Thus, frameworks capable of detecting such distortions on social media are necessary.

But the challenge is “Who gets to decide what is a “distortion” and what is not”? “Could such frameworks potentially be used to silence dissenting voices or unfairly sway market sentiment?”

Other factors such as regulatory changes, geopolitical events, and technological advancements can have a significant impact on the market that cannot be captured through sentiment analysis alone.

A possible approach to improving sentiment analysis is to incorporate data beyond just text-based content. For example, models could be trained on images, videos, and audio content to capture a more holistic understanding of market sentiment.

Lastly, there could be opportunities to use sentiment analysis for more than just price prediction. For instance, sentiment analysis could be used to identify potential reputational risks for companies in the financial market based on public sentiment towards their activities or products.

Forecasting Market Movements With New Methods

In traditional finance, sentiment analysis has been the go-to method for predicting market trends. But we need more advanced methods to predict the price dynamics in crypto markets.

1. Statistical correlation

While traditional markets tend to have a limited number of assets that are largely uncorrelated, the crypto space is a different story altogether. Here, one can find statistical correlations between major coins or even categories of coins. This is especially true in localized ecosystems such as the AI-focused SingularityNET, where multiple tokens exist and lagging and correlative trading patterns often emerge.

2. Deep Learning

As computer hardware technology continues to advance, we are finding new ways to use powerful tools to understand the complex crypto market. One such tool that has been gaining popularity is “deep learning”, which is a form of AI that uses complex algorithms to identify patterns and make predictions. It can predict if prices will go up or down, and even identify market trends such as whether we are in a bear or bull market. It’s like having a crystal ball that not only tells you if prices will go up or down, but also if you should book a new yacht or hold off for a little while. Consider it as a personal psychic, but without all the weird crystal balls and tarot cards.

3. Reinforcement learning

Reinforcement Learning (RL), an unsupervised AI technique, is about choosing the optimal path to obtain maximum reward. The potential of applying RL in comprehending its actions and predicting slippage and price impact during trading, could be a game-changer in market predictions.

4. Network Analysis

Network analysis is a promising new approach that uses complex algorithms to identify relationships and patterns between nodes in a network. In the crypto market, this can be applied to identify the connections between different cryptocurrencies, exchanges, and wallets, and how they affect each other.

5. Analyze Whitepapers using NLP

Whitepapers of crypto projects can be analyzed using NLP methods. With the help of NLP,, we could better analyze the potential of new cryptocurrencies and predict their market performance before their launch.

Identifying malicious entities

In the murky depths of on-chain transactions, the crypto market has become a breeding ground for malicious activities, presenting a huge challenge due to the sheer scale of involved datasets.

Thankfully, we have AI on our side — a superhero with X-ray vision, able to see through the facade of anonymity that these malicious entities hide behind.

With its sophisticated clustering and neural networks, AI can pinpoint malicious entities with remarkable precision. For example, Anchain investigates machine learning-based wallet behavior and leverages AI-powered auto tracing.

If AI can detect malicious entities in the crypto market, could it also be used to identify and neutralize malicious actors in other areas of society, such as politics or business? Who knows.

But as with any new technology, there are challenges that must be overcome. For instance, it can be difficult to distinguish between legitimate and malicious entities, and false positives can have serious consequences. Thus, it’s crucial to prioritize the development of “ethical” AI that can accurately distinguish between right and wrong.

Evaluating Liquidation risks

Traditional methods of assessing risk in trading positions have failed to keep pace with the rapidly changing landscape of Web 3. This could mean more ‘black swan’ events like the ones witnessed with the FTX and Celsius Network.

As the financial industry scrambles to adapt to this new landscape, one area of focus is the risks associated with liquidity movements across protocols. The vast amount of data to be analyzed makes this a daunting task for analysts. But by analyzing large wallet holders and liquidation risks, AI can provide valuable insights that were previously unattainable.

Crypto Twitter’s masked on-chain detective, @zachxbt, is already one step ahead of the game. Using AI-based analytics platforms, he’s able to identify and break news stories before they hit mainstream crypto news.

But, hold on a second.

If AI is the solution, then why aren’t more analysts using it? Is it because they’re scared of change or because they’re too busy playing Candy Crush? And what about the risks of AI itself? Could AI be like the Terminator, turning against us and causing a financial doomsday?

Regardless of these concerns, the fact remains that AI and DeFi are a match made in heaven. Together, they can create new metrics that provide a clear picture of risk exposure across different protocols, making it easier to identify and mitigate liquidation risks

Power of Artificial General Intelligence (AGI)

Although Machine learning and optimization technologies have made tremendous strides in recent years, what if we could create a machine that could learn anything and everything? Imagine a machine that could surpass the intelligence of any human, and maybe even outsmart its creators. That’s the idea behind “Artificial General Intelligence” (AGI), and it’s both fascinating and terrifying.

Ben Goertzel, a prodigy who started university at 15 and received his doctorate at just 22, was the man who popularized the term AGI. He foresees an imminent risk in creating a machine that can learn everything, as it could eventually learn to reprogram itself and become exponentially more intelligent than any human being. However, the creation of such a machine is inevitable, and the question we should be asking ourselves is, how do we control it?

Although AGI could certainly change our future, the processing requirements and data volumes involved in the development of AGI are enormous. Blockchains, although seen as a promising technology, are not capable of handling this level of processing.

Thus, there is a sense of urgency to develop a solution that can scale blockchain technology quickly to meet the demands of AGI. While scaling solutions like zero-knowledge rollups have shown potential, their bandwidth limitations hinder their effectiveness.

As we approach a time when AGI could be 100 times smarter than humans, the issue of control becomes increasingly critical. If we don’t get this right, we could end up like the chimpanzees at a zoo, mere observers of our new overlords. Is it worth the risk of losing control over something we created? That’s the question for another time.

From traditional AI Bots to AMSA

The financial world is no stranger to the idea of AI and automated trading, but SingularityDAO’s latest development takes it to a whole new level.

Enter the Adaptive Multi-Strategy Agent (AMSA), a self-reinforcing machine that can assess market movements and make trades with the accuracy of a seasoned trader.

With AMSA, various AI algorithms can work together in a unique environment to buy and sell assets, while simultaneously back-testing those trades to assess their performance and their effect on the market.

However, AMSA does not provide a specific trading strategy of its own but it divides the trading activity into smaller sub-periods and then performs internal back-testing on historical data on each of the sub-periods. These cutting-edge systems can rebalance multi-asset portfolios and maintain their competitive edge in the market.

But the question is: Will the rise of AI-based market making systems lead to the demise of human expertise in finance? Or will it simply change the way we think about trading and investing?

Final thoughts

The seamless integration of AI with crypto will revolutionize the financial industry and create an inclusive ecosystem for all. With the ability to automate tasks and predict trends, AI-controlled crypto funds have the potential to offer investors a level of safety that traditional finance simply cannot match.

With AI algorithms, even novice investors can make informed investment decisions, reducing the knowledge gap that often excludes people from participating in traditional finance. Such an approach can lead to more diverse investment portfolios and a broader distribution of wealth.

However, to safeguard against the misuse of AI, development of AI-controlled crypto funds should be underpinned by a strong ethical framework, which prioritizes transparency and accountability.

This article was written by Raviyank Patel on behalf of COMB Finanical