Decentralized applications (Dapp) are revolutionizing the way we approach decentralized Web3. They get built on the infrastructure of Web3. Usually, they are programs and protocols that a centralized authority can't control. These applications have helped change the scope of Web3 development.
However, considering this scope, there are spots or instances where the effectiveness and efficiency of decentralized applications could experience different drawbacks with maintenance, where updates are frequently needed.
Also, there is the scaling issue where the required level of security, integrity, transparency, and reliability, the computational power needed, would be great, and this is where artificial intelligence (AI) and machine learning (ML) could play a significant role.
Artificial Intelligence (AI) mirrors human intelligence, the ability to reason and infer by computerized systems. AI systems can self-learn, self-correct, reason, and also display creativity. We could improve effectiveness and efficiency faster when aligned with Web3 and in application to decentralized applications.
By feeding these AI systems data and through machine learning, there could be an adaptation to peculiar situations without the need for explicit instructions using statistical models and algorithms that work with given data.
This way, AI could perform in a more efficient way, far better than humans could. Below are different applications of AI and machine learning in Web3.
One of the drawbacks of decentralized applications is security, and there have been reports of different types of exploits and frauds. For example, bad actors could, through the use of phishing links, make unsuspecting individuals carry out transactions against their will.
Fraudulent transactions could be identified, traced, and retrieved using anomaly detection models; this can help improve the trust and efficiency in dapps and reduce the number of those who fall into fraudulent scams and attacks.
We could apply artificial intelligence and machine learning to risk management in Web3 and decentralized applications. Users often need a way to access the risk of certain transactions they take and can only take a wild guess; this leads to situations where users sign transactions that give malicious actors access to their dapps.
However, using AI and ML, there can be a predictive risk assessment of any transaction you want. Users can then decide whether transactions are safe to carry out, reducing the frauds they fall into.
There have been different cases of smart contract exploitation, from Poly Network to Ronin; all of this has resulted in a lapse in the security of their contracts. Artificial intelligence and machine learning can help reduce these sorts of events.
We can detect vulnerabilities in smart contracts using artificial intelligence and machine learning. By seeing these vulnerabilities, smart contract engineers and programmers can improve the overall security of a contract by implementing correct logic.
Also, using ML classifiers for smart contract auditing could help smart contract auditors do a faster and better job than they would naturally.
Using AI, developers could allocate resources optimally to achieve a desired goal. Unlike when AI uses available data to make decisions quickly, it could take longer for humans, with more margins for error and bias, to reduce the overall robustness of resource optimization.
Also, applying this to load balancing in decentralized networks. AI can help distribute network traffic equally across multiple pools that support a decentralized application.
In addition, it could get used to maximize the performance of distributed storage and computing.
There's a lot of content in Web3, from seemingly good ones to others that are useless or targeted at misleading.
Using personalized machine learning-powered recommendations for content and suggestions for marketplaces and Web3 tools could reduce the time people spend researching.
These recommendations would reduce the number of frauds or scams people fall victim to and help them have better options for making choices.
In decentralized computing infrastructure, one of the lapses is the time it takes to individually carry out functions from different networks working together for a system.
Although in contrast to the centralized computing infrastructure, it’s known to be more effective because the performance of each network pars the needs of any application.
We could deploy AI and ML models to handle the workloads on this infrastructure further to enhance the performance of this decentralized computing infrastructure, making it operationally faster and more effective.
Challenges in Using AI and Machine Learning to Build More Effective Decentralized Applications for Web3
There are different challenges the use of AI and ML for decentralized applications is bound to face due to the nature of decentralization itself, and we go through a few here.
One of the challenges is the collection of valuable data in a decentralized environment. Since there is no centralization, accessing data and collecting would be threefold tricky due to the inherent structure of decentralization itself.
Individually scraping out and cleaning data from different independent networks would take a while and might prove unnecessarily tiresome for many.
There's a challenge with the security of models deployed on the blockchain, which is honest. What chances are models deployed won't get corrupted by malicious actors? A model inversion poses the greatest threat to the security of models.
In an attack, malicious actors aim to expose the privacy of training data which can hurt confidentiality in models.
Also, there could be data poisoning, where AI models get trained with data manipulated to predict this AI's behavior. An attack such as this could alter, for example, a personalized recommendation system by AI models, where fraudulent marketplaces get shown as options for users to choose from, increasing the possibility of them falling for scams.
Training models using distributed datasets could be cumbersome and time-consuming due to labeling and cleaning.
Although, it could be fast when the model training is done concurrently for each dataset and could help for time-sensitive tasks.
There are different future possibilities that AI and ML present for decentralized Web3, and we touch on a few possible below.
We could train AI to engineer smart contract development and make the whole development cycle from scratch to deploy easier, faster, and more secure. Also, decentralized applications could be run solely by AI-trained models, reducing the possibility of interference by centralized malicious actors.
We could do dapps that self-learn and take inputs from user experience to help make the experience of users more smooth and more effective.
For example, if a user encounters a bug when using a decentralized application, it could learn from that and help to make other users not face the same bug that caused the previous user to have a horrible experience.
There has been a recent fear of losing out (FOMO) on AI and what it poses for the future of humans and technology.
However, through AI alignment in decentralized applications for decentralized Web3, there could be advances that would prompt robust advancement and progress.
However, this isn't without challenges, as there are security, data collection, and training using distributed data sets that could make this goal harder to reach.
Regardless, there are bright prospects that the alignment of AI in decentralized Web3 poses to bring to the user experience and technological advancement over time.