The two most talked about tech trends in the last two years are artificial intelligence and blockchain. These technologies are getting so much attention for good reasons. AI promises to automate many tasks, and it is often more adept at modeling complex situations than humans. Blockchain, however, provides greater data security, and privacy, and reduces the overhead and central power of large institutions.
Combining AI and blockchain may seem counterintuitive or like a pitch to the next scam ICO. These two technologies may work well together, however, there are good reasons for them to be compatible. Each addresses the weaknesses of the other and balances the worst aspects of each. The future of AI may rely on distributed computing and blockchain-based innovations, as we will see. Blockchains could then use AI to monetize user-controlled data, create a marketplace of AI models and even create autonomous organizations.
This article will explore how these two technologies interact currently. Both AI and blockchain are still in their infancy. Over the next decades, there will be many innovations that transform the way we think about and discuss these technologies. Combining them will present challenges. It is true that blockchain's weaknesses as a data storage system and retrieval system make it more difficult to use than traditional databases for AI applications. It is also much easier to code a smart contract or decentralized organization than it is to write an AI capable of doing those things safely. However, there are many exciting opportunities if we get to that point.
AI & Blockchain: Trending Intersection
Contrasting Philosophies
Let's first discuss the difficulties of combining blockchain and AI before we dive into the potential uses of both. Blockchain and AI can be compared to oil and water. They operate in so many different ways and have philosophically opposing goals. It is the contrast between these philosophies that makes potential combinations so appealing. This is also the greatest obstacle to developing these new applications.
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Centralized vs Decentralized
The locus of power is the first significant philosophical distinction between AI and Blockchain. AI relies on massive, comprehensive data stores and huge computing power to train algorithms. AI is a benefit that only the companies with the greatest data and resources can reap the benefits of. They are then enriched by AI's advances and have more money to invest in better AI. AI is therefore a centralizing technology. It encourages data consolidation and computing power. Common concerns are that AI will make the wealthy richer, and it creates income inequality in the lives of the poor who don't have access to AI.
Blockchain could solve this philosophical problem of AI. Blockchain ledgers allow for the decentralization of computing resources and data control while making data and resources available to the entire network. This decentralization does come at the expense of network latency. If blockchain ledgers are to be used with AI, serious efforts will be required to speed up blockchain ledgers. There is some promise in the idea of consumers being able to retain their data and computing power, but rent it out to large corporations when necessary.
If user data were stored on a blockchain, smaller companies, governments, and NGOs could all benefit from it. Anyone could run AI models if they had access to decentralized computing power. Blockchain could allow anyone to access AI and enable them to create and use models using real data from all over the world.
Black Box vs. Transparent
A second philosophical difference between AI & blockchain is the way they approach transparency. Transparency is the founding principle of blockchain. Anyone can see the ledger of public blockchains. Although data has been anonymized it is still accessible on the ledger. The blockchain also provides transparency about transactions and how they are added. The trust created by the open peer network is a result of cryptography that can be understood publicly.
Machine learning algorithms and neural networks, on the other hand, are notoriously difficult to understand. Some types of statistical algorithms require a deep understanding of linear algebra. However, many other deep learning techniques can be understood by anyone, even researchers.
Blockchain can't solve the transparency problem in AI. This is simply due to the nature of deep learning and our current understanding of how algorithm training works. Future research may lead to a better understanding of these algorithms.
Blockchain can however solve the problem of public access to data that these models were trained on. This can reveal important information about the model's weaknesses and biases. An AI model can only be as good as the data on which it is trained. Blockchain data storage could allow for private or public blockchain, and public audits of the training data to ensure equitable AI.
Potential Applications of Blockchain and AI Combined
Philosophically, AI and blockchain pull in different directions. AI is focused on complex, fast insights that require large amounts of data and computing resources. Blockchain is able to provide these data and computing resources but it will take longer, be more transparent, and decentralized, and it will not be as fast. While speed is a problem, blockchain scaling solutions are currently in development that could help data to be served more quickly.
These powerful, but conflicting technologies can be combined to create many interesting applications. These applications are focused on making AI more accessible and affordable.
Distributed Computing for AI
Combining blockchain and AI in a powerful way is the use of mining networks to generate computing power for algorithms training. In order to train algorithms in AI and machine learning, you need to use a lot of CPU power. You will need to run thousands to millions of sessions to teach the algorithm how best to make decisions. The biggest stumbling block in machine learning development is waiting for algorithm training. Data scientists and AI researchers would be able to develop algorithms more quickly if they had access to more computing resources.
Many startups are exploring the possibility of connecting AI researchers to the thousands of GPUs that are currently mining digital tokens. Sometimes, AI researchers might be willing to pay more for cryptocurrency mining than miners. This could be a good idea for the GPU networks that power proof-of-work to power the next generation algorithm training.
These networks can be affordable and successful, making algorithm training accessible to everyone. Access to central server farms is required if you are to create serious AI algorithms. Instead, anyone could use decentralized GPU mining networks to have instant access to a supercomputer for training their algorithms.
Keeping Data Private
Blockchains make data anonymous. Although it is possible to determine who data belongs to whom, anonymization makes the blockchain ledger an excellent place to conduct research. Instead of Google and Amazon collecting data on millions of users, anyone can access anonymized data to create analyses, predict, or train algorithms.
The algorithms that are trained on this data may be less biased if they are based on wide-reaching, anonymous, decentralized data. Google's algorithms are no longer trained on Google's predominantly white and western user data. Instead, Google could use global anonymous blockchain data to create algorithms that are more representative of the entire global population.
Marketplace for Algorithms
AI will become a community-driven opportunity once you open up computing and data access. We already see open-source packages for common machine-learning models such as TensorFlow and PyTorch. Blockchain-based computing and data could accelerate this trend and create a market for algorithms. Companies could either pay data scientists to provide insights or purchase the algorithm for continued use.
Instead of AI being an internal and proprietary process, it could become an open market where everyone can contribute and participate. These marketplaces, as we have seen with open source software and the rise in infrastructure-as-a-service software, encourage innovation more than private development.
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A Data Marketplace
Blockchain records also allow users to have more control over their data. Google currently keeps your search history and all interactions with Google products such as YouTube, Google Maps, Android devices, and Google Search. Google now has access to that data for free.
Blockchain-based infrastructures could allow you to regain control over your data. In exchange for personalized services, you might share personal information with Google. Google would not own the data that you share and you can revoke your access at any point. This allows you to sell your personal data. In order to gain insights, corporations might offer consumers their data in exchange for access. This marketplace could offer an opportunity for anyone online to earn passive income from data that is already being created and sold.
Decentralized autonomous organizations
An autonomous organization is not only the most futuristic but also the most remote application of AI and blockchain combined, but it is also the most radical. Decentralized autonomous organizations (DAOs), which are already in existence, have rules that are hard-coded as smart contracts. Although the organization is capable of performing actions, it cannot make independent decisions. It follows only the rules that the smart contract developer created.
Independent decision-making is possible if AI algorithms are integrated into the DAO development process. These organizations could use market data to make business and philanthropic decisions. They could obtain resources and determine the best way to allocate them given the context. We are looking for companies and organizations that can operate themselves without external intervention.
These companies are almost impossible to shut down because they run on a distributed network. They are a risky idea. They can be a key to good governance and economic equality if they are well built.
Conclusion
Combining AI and blockchain has many potential applications. These technologies are still in their infancy. These hybrid applications may take many years to develop, or not at all, for most of them. These are powerful ideas. However, researchers and blockchain developers like Errna need to be careful when developing them. Both blockchain and AI have huge ethical implications. The stakes for society, governance, and the economy are very high.
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