From Chatbots to Crypto: Can AI help the crypto ecosystem?
In the ever-evolving digital world, the rise of artificial intelligence (AI) is becoming an essential tool for many businesses to enhance their operations and secure a competitive advantage. As the popularity of AI-based applications like ChatGPT continues to rise, it will be interesting to see how this cutting-edge technology could revolutionise the way we conduct business and perhaps even our day-to-day lives.
It all started over 25 years ago, when IBM’s Deep Blue supercomputer beat the world chess champion, Garry Kasparov, for the first time. This milestone motivated researchers to explore new ways of using machines to tackle complex problems and augment decision-making capabilities.
Since then, the artificial intelligence (AI) landscape has undergone a transformational change, from innovative projects like Google’s DeepMind to OpenAI’s ChatGPT, providing users with opportunities to engage in regular human-machine interactions.
The launch of ChatGPT back in November rejuvenated the entire AI narrative, catalysing a remarkable rise in the value of crypto-based AI initiatives.
Price percentage increase in AI-based crypto tokens between 30 November (ChatGPT launch) and 23 March. Source: TradingView
In this article, we will dive into the fundamentals of AI, its advantages, challenges and its role in assisting the crypto world. More below.
Artificial intelligence is more than just timesaving
When we hear the term AI, we usually think of its ability to save time, analyse vast amounts of data and minimise human error. Today, AI has become an indispensable tool for things like medical diagnoses and data-driven analysis to virtual assistants and self-driving cars.
Even financial institutions have their skin in the game, with Goldman Sachs using AI tools to assist its developers with writing code, while hedge funds like Bridgewater have started adopting the technology to generate investment ideas, assist with strategy, asset allocation and even trading executions. In fact, a recent survey found that 90 percent of hedge fund traders plan to use AI to achieve returns this year.
What is AI in simple terms?
Artificial intelligence is the use of machines or software programs that can simulate human intelligence. These machines learn from vast amounts of data, identify correlations and use this knowledge to perform tasks, make predictions and solve problems.
The programming behind AI focuses on three cognitive skills: learning, reasoning and self-correction.
Learning involves processing data to create algorithms (or rules), which guide the machine to perform specific tasks
Reasoning helps the AI choose the best algorithm to achieve the desired outcome
Self-correction ensures that the algorithms are continuously fine-tuned for accuracy
AI can be categorised into two basic types: narrow and general. Narrow AI is created to solve one specific problem, like a chatbot designed to answer questions and engage in conversations (more below). General AI is a more future-based theoretical application where the AI can respond to different environments and adapt accordingly.
Today, most AI applications are narrow based. They are supervised to avoid bad decisions and reinforce good ones. The ultimate goal, however, is to self-learn without human intervention.
Crypto and ChatGPT
By now, many of you have heard of ChatGPT, the popular chatbot known for its human-like responses and over 100 million users worldwide just two months after its launch. It can assist with many tasks, from generating code to creative writing, like poetry and lyrics.
In the crypto space, ChatGPT is proving to be a useful tool for novices and advanced users alike. It can answer educational questions about blockchain technology and can also be leveraged to help code trading bots and trading terminals.
Even the latest version, ChatGPT-4, is being “tested” to perform token reviews and smart contract audits by Coinbase, and Morgan Stanley is exploring its use as an intelligent assistant to financial advisors, despite most banks currently inhibiting its use - but this is not without good reason. The chatbot is far from perfect and may provide misinformed answers, so it is best to use the application in a controlled environment and for specific tasks only.
Crypto and AI – a perfect match?
Now in the crypto economy, we have a unique situation where the entire ecosystem relies on data – which is essentially what fuels AI. Technically, over USD 1.1 trillion assets are safeguarded by the reliability of protocols and smart contracts. For instance, things like cross-chain bridges, which use smart contracts to connect protocols, are exceptionally vulnerable and make up the majority of DeFi hacks last year.
Optimising workflows: For developers and companies using AI tools, building protocols and deploying code requires data-driven research on specific features and user needs, defining smart contract rules, testing them and identifying errors and bugs - which are necessary to mitigate any potential vulnerabilities in the future. They must continuously evaluate past workflows, performance metrics and smart contract data to determine the most viable improvement proposals for securely scaling blockchains.
This sounds like an ideal application for AI technology – leveraging data-driven analysis to achieve the best possible outcomes.
Optimising Web3 ecosystems: Combining AI and blockchain can potentially create a highly secure and responsive infrastructure. For instance, AI can detect and respond to potential threats, monitor transaction activity and network traffic, while blockchain can provide a tamper-proof record of network activity, distribute data storage and improve data augmentation, creating a more robust data economy for AI.
Combining blockchain and AI is especially important for AI itself, as the accuracy of its outputs (responses) is dependent on the reliability of the datasets it uses. Currently, AI operators are centralised, opaque and lack a trustless nature behind their data collection and processing. In fact, we have no way of determining whether any data has been tampered with or not.
By creating a robust and tamper-proof record of verifiable data, blockchain can provide the necessary transparency to fuel AI. This will not only improve the data’s integrity and the AI’s output, but also help address the biggest issue in its current usage – trust.
Crypto-based AI initiatives
In recent years, the synergy between AI and crypto has also led to a diverse range of initiatives in the space. Here are a few examples below.
The Graph: is a decentralised protocol that uses AI for indexing and querying data from blockchains, helping over 30 dapps in data retrieval including Uniswap, Aave, Balancer and Decentraland.
Numerai: is an AI-based blockchain network acting as a hedge fund, using AI to help make investment in equities on the Ethereum blockchain. Numerai gathers financial market data from the top 5000 stocks worldwide.
SingularityNET: offers a decentralised marketplace for AI, allowing users to build, share and monetise AI services.
Ocean protocol: is a decentralised data exchange built on the Ethereum blockchain. The exchange unlocks data for AI usage, which relies heavily on robust and reliable data inputs.
Render token: is a decentralised 3D rendering service and marketplace. It aims address the challenge of scaling AI systems that use significant computing power.
Other projects include Fetch.ai, an AI platform for automating business tasks, and Alethea AI, creating “intelligent” NFTs (iNFTs) that interact with people as digital avatars using AI.
Challenges to AI integration
While many businesses are (in some form) incorporating AI into their operations, there are significant challenges to its integration efforts - including regulatory compliance, energy consumption and ethical concerns.
To achieve widespread adoption, safety, trust, computation power and job loss concerns need to be addressed, alongside the substantial expense of integrating AI and the reliability and security of the data it uses.
AI suffers greatly from bad data and embedded bias, centralised storage concerns, and even data privacy violations – more so by the fact that we do not know how AI operators collect, secure and verify their datasets.
This is why blockchain, clear regulatory frameworks and ethical guidelines are needed to ensure AI development is monitored, secured and used for good purposes.
Pay close attention to the trend
So where does all of this lead us? Even though AI-based crypto is still in its early stages, the integration of these two emerging technologies is an exciting prospect.
Investors should be paying close attention to the AI trend. Advancements can happen quickly, as it is likely to become – if not already – a competitive tool in the digital world.
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