When Blockchain Meets Artificial Intelligence
Blockchain and Artificial Intelligence (AI) are two of the hottest technology trends right now. Even though the two technologies have highly different developing parties and applications, researchers have been discussing and exploring their combination.
Blockchain is a distributed, decentralized, and immutable ledger used to store encrypted data. On the other hand, AI is the engine or the “brain” that will enable analytics and decision making from the data collected.
Each technology has its own individual degree of complexity, but both AI and blockchain are in situations where they can benefit from each other, and help one another.
AI and Blockchain are proving to be quite a powerful combination, improving virtually every industry in which they are implemented. These technologies can be combined to upgrade everything from food supply chain logistics and healthcare record sharing to media royalties and financial security.
AI can effectively mine through a huge dataset and create newer scenarios and discover patterns based on data behavior. Blockchain helps to effectively remove bugs and fraudulent data sets. New classifiers and patterns created by AI can be verified on decentralized blockchain infrastructure and verify their authenticity. This can be used in any consumer-facing business, such as retail transactions. Data acquired from the customers through blockchain infrastructure can be used to create marketing automation through AI.
In simple terms, we can say that AI development and evolution can significantly improve blockchain technology and the crypto industry, from security to trading. From performing simple commands on smartphones using Alexa or Siri to high-end technical operations in big tech firms, “ease” is a necessity in the modern human experience.
The 21st century has marked a rapid advancement of technology in every aspect of human life and interactions. Despite being around for many decades, the replication of human intelligence in machines, AI has now become popularized.
With many tech companies rushing to adopt the technology, the market size is expected to grow to $126 billion in 2025.
Additionally, blockchain can also make AI more coherent and understandable, and we can trace and determine why decisions are made in machine learning.
Detailed Explanation of Blockchain and Artificial Intelligence
Blockchain uses cryptography to ensure that data, transactions, and identities can be:
Incorruptibly, securely and irreversibly recorded
Verified as trustworthy while remaining private — participants can verify the veracity of data without needing to look at the data, and only see what they are authorized to see
Easily shared so that everyone in a blockchain network has an identical copy of the entire ledger, including updates as they occur
Artificial Intelligence involves using computers to do things that require human intelligence. AI models can be used to analyze, classify, and make predictions from data. Unlike traditional software, AI models can also improve (learn) over time as they are fed new data.
Data is central to AI effectiveness, and blockchain enables collaborative and secure data sharing. Blockchain can ensure the trustworthiness of data and can enable more data to be securely shared before AI extracts insights from it.
In this new world, AI will create and trade digital investment assets over high-speed private blockchains. Institutional investors will buy these assets because they trust the ability of the issuing firms.
How AI can Add on to Blockchain
The confluence of AI in blockchain creates perhaps what is the world’s most reliable technology-enabled decision-making system that is virtually tamper-proof and provides solid insights and decisions. It holds several benefits like:
Improved business data models
Globalized verification systems
Innovative audits and compliance systems
Intelligent predictive analysis
Digital Intellectual Property Rights
Technical Enhancements that AI can enable
Artificial Intelligence can provide many improvements in multiple spheres. Some of these are given here:
Security: With the implementation of AI, Blockchain technology becomes safer by making secure future application deployments. AI algorithms that are increasingly making decisions about whether financial transactions are fraudulent and should be blocked or investigated is a good example of it.
Efficiency: AI can help optimize calculations to reduce miner load which results in less network latency for faster transactions. AI enables to reduce the carbon footprint of blockchain technology. The cost that is applied upon miners would also be reduced together with the energy spent if AI machines replace the work done by miners. As the data on blockchains grows by the minute, AI’s data pruning algorithms can be also be applied to the blockchain data which automatically prunes the data which is not required for future use. AI can introduce even new decentralized learning systems such as federated learning or new data-sharing techniques that make the system much more efficient.
Trust: The iron cast records of blockchain is considered one of its USP. Applied in conjunction with AI means users have clear records to follow the system’s thinking process. This, in turn, helps the bots trust each other, increasing machine-to-machine interaction and allowing them to share data and coordinate decisions at large.
Better Management: When it comes to cracking codes, human experts get better over time with practice. A machine learning-powered mining formula can eliminate the requirement for human experience because it may nearly outright sharpen its skills if it’s fed the correct coaching knowledge. So, AI additionally helps in managing blockchain systems higher.
Privacy and New Markets: Making private data secure invariably leads to it being sold, resulting in data markets/model markets. The markets get easy, secure data sharing that helps smaller players gain Blockchain’s privacy can be more increased by executing “Homomorphic encryption” algorithms. Homomorphic algorithms are the ones using which operations can be performed on encrypted data directly.
Storage: Blockchains are ideal for storing the highly sensitive, personal data which, when smartly processed with AI, can add value and convenience. Smart healthcare systems that make accurate diagnoses based on medical scans and records are a good example of that.
Applications of AI and Blockchain
Now let’s see some of the joint applications of AI and Blockchain:
Smart Computing Power: If you were to work a blockchain, with all its encrypted knowledge, on a laptop you’d like massive amounts of process power. The hashing algorithms used to mine Bitcoin blocks, for example, take a “brute force” approach – which consists of systematically enumerating all possible candidates for the solution and checking whether every candidate satisfies the problem’s statement before confirmatory a dealing. AI affords the USA the chance to maneuver faraway from this and tackle tasks in a very a lot of intelligent and economical approach. Imagine a machine learning-based algorithm, which could practically polish its skills in ‘real-time’ if it were fed the appropriate training data.
Creating Diverse Data Sets: Unlike computing based-projects, blockchain technology creates suburbanized, transparent networks that can be accessed by anyone, around the world in a public blockchain network situation. While blockchain technology is the ledger that powers cryptocurrencies, blockchain networks are now being applied to several industries to create decentralization. SingularityNET combines blockchain and AI to create smarter, decentralized A.I. Blockchain networks that can host diverse data sets. By making Associate in Nursing API of APIs on the blockchain, it’d allow the communicating of AI agents. As a result, various algorithms may be designed on various knowledge sets.
Data Protection: Through knowledge, AI receives data regarding the globe and things happening thereon. Knowledge feeds AI, and through it, AI will be able to continuously improve itself. On the opposite aspect, blockchain is essentially a technology that allows for the encrypted storage of data on a distributed ledger. It allows for the creation of fully secured databases that can be looked into by parties who have been approved to do so. When combining blockchains with AI, we have a backup system for the sensitive and highly valuable personal data of individuals. The development of artificial intelligence applied to big data together with the security offered by blockchain technology creates the perfect combination for the management of large databases. Medical or financial data are too sensitive to hand over to a single company and its algorithms. Storing this data on a blockchain, which can be accessed by an AI, but only with the permission and once it has gone through the proper procedures, could give us the enormous advantages of personalized recommendations while safely storing our sensitive data.
Data Monetization: Another turbulent innovation that might be doable by combining the two technologies is that the validation of information. Monetizing collected data is a huge revenue source for large companies, such as Facebook and Google. Having others decide how data is being sold to create profits for businesses demonstrates that data is being weaponized against us. Blockchain permits the USA to cryptographically defend our knowledge and have it utilized in how we tend to see work. This additionally lets the USA legitimatize knowledge in person if we elect to, without having our personal information compromised. This is important to understand to combat biased algorithms and create diverse data sets in the future. The same goes for AI programs that require our knowledge. For AI algorithms to learn and develop, AI networks will be required to buy data directly from its creators, through data marketplaces. This will create the whole method a way more truthful method than it presently is, without tech giants exploiting its users. Such a knowledge marketplace also will open AI for smaller corporations. Developing and feeding AI is implausibly pricey for corporations that don’t generate their knowledge. Through suburbanized knowledge marketplaces, they will be able to access otherwise too expensive and privately kept data.
Trusting AI Decision Making: As AI algorithms become smarter through learning, it will become increasingly difficult for data scientists to understand how these programs came to specific conclusions and decisions. This is because of AI algorithms are going to be ready to method implausibly massive amounts of information and variables. However, we must continue to audit conclusions made by AI because we want to make sure they’re still reflecting reality. Using blockchain technology, there are immutable records of all the data, variables, and processes used by AIs for their decision-making processes. This makes it far easier to audit the entire process. With the appropriate blockchain programming, all steps from data entry to conclusions can be observed, and the observing party will be sure that this data has not been tampered with. It creates trust within the conclusions drawn by AI programs. This is a necessary step, as individuals and companies will not start using AI applications if they don’t understand how they function, and on what information they base their decisions.
Three Major Benefits of Combining AI and Blockchain
AI and encryption work very well together: Blockchains are ideal for storing the highly sensitive, personal data which, when smartly processed, can unlock so much value and convenience in our lives. Think of smart healthcare systems that make accurate diagnoses based on our medical scans and records, or even simply the recommendation engines used by Amazon or Netflix to suggest what we might like to buy or watch next. AI has plenty to bring to the table in terms of security, too. An emerging field of AI is concerned with building algorithms that are capable of working with (processing, or operating with) data while it is still in an encrypted state. As any part of a data process that involves exposing unencrypted data represents a security risk, reducing these incidents could help to make things much safer.
Blockchain can help us track, understand, and explain decisions made by AI: Decisions made by AIs can sometimes be hard for humans to understand. This is because they are capable of assessing a large number of variables independently of each other and “learning” which ones are important to the overall task it is trying to achieve. For example, AI algorithms are expected to increasingly be used in making decisions about whether financial transactions are fraudulent, and should be blocked or investigated. For some time though, it will still be necessary to have these decisions audited for accuracy by humans. And given the huge amount of data that can be taken into consideration, this can be a complex task. Walmart, for example, feeds a months’ worth of transactional data across all of its stores into its AI systems which make decisions on what products should be stocked, and where. If decisions are recorded, on a datapoint-by-datapoint basis, on a blockchain, it makes it far simpler for them to be audited, with the confidence that the record has not been tampered with between the information being recorded and the start of the audit process. No matter how clearly we can see that AI offers huge advantages in many fields, if it isn’t trusted by the public, then its usefulness will be severely limited. Recording the decision-making process on blockchains could be a step towards achieving the level of transparency and insight into robot minds that will be needed in order to gain public trust.
AI can manage blockchains more efficiently than humans (or conventional computers): Computers have been very fast, but very stupid. Without explicit instructions on how to perform a task, computers can’t get them done. This means that, due to their encrypted nature, operating with blockchain data on “stupid” computers requires large amounts of computer processing power. AI is an attempt to move away from this brute force approach, and manage tasks in a more intelligent, thoughtful manner. Consider how a human expert on cracking codes will, if they are good, become better and more efficient at code-breaking as they successfully crack more and more codes throughout their career. A machine learning-powered mining algorithm would tackle its job in a similar way – although rather than having to take a lifetime to become an expert, it could almost instantaneously sharpen its skills if it is fed the right training data. Clearly, blockchain and AI are two technological trends that, while ground-breaking in their own rights, have the potential to become even more revolutionary when put together. Both serve to enhance the capabilities of the other, while also offering opportunities for better oversight and accountability.
How Blockchain benefits from AI?
Distributed ledger technology and cryptocurrencies have exceeded everyone’s expectations and are looking to compete with traditional fiat currencies.
With AI and cryptocurrencies to become formidable fortresses, let’s take a look at how artificial intelligence impact distributed ledger technology:
Cryptocurrency trading: After the emergence of blockchain technology in the post-crisis era, the industry has continued to attract a significant number of investors and prove doubters wrong. In December 2017, Bitcoin (BTC) climbed to an all-time high price of $20,000. Cryptocurrency trading continued to gain more attention, and the industry now has a market capitalization of over $339 billion. As more investors join the blockchain space, the impact of AI becomes increasingly significant to crypto trading. By developing fast, effective, and impartial AI trading bots, cryptocurrency traders have avoided slippage and performed accurate technical and fundamental analyses to make better trading decisions. Ultimately, many traders have increased their profits while seeing considerably low losses.
Blockchain security: The blockchain industry has suffered a high level of malware, phishing, and 51% attacks, among other corrupt endeavors. An estimated $9 million is lost to cryptocurrency scams every day. So long as the technology exists, security, and a means of protecting users from fraud will always be a hot topic. Blockchain hacks and attacks are usually very time-sensitive, with the first response to the hack being critical. However, many crypto exchanges haven’t been lucky in dealing with hacks. Fortunately, AI-based cybersecurity systems are designed to identify threats in real-time, understand the nature of the threat, and prevent future attacks by blacklisting its source. Unlike traditional cybersecurity systems, AI is designed to improve with every threat because of its ability to detect patterns, study them, and become better at dealing with them.
Bitcoin mining: To maintain the blockchain’s integrity, transactions are verified and added by Bitcoin miners. Bitcoin miners are, in turn, rewarded in cryptocurrency. However, the process is painstaking, sensitive, and energy-consuming, and ultimately requires many graphic processing units or GPUs. In a bid to conserve energy and maximize computing power, many mining companies turn to AI-based GPUs. Some mining companies have created AI-based ecosystems where miners can share and economize computing power while making considerable profits. With these unique AI algorithms, Bitcoin mining becomes faster, more efficient, and more profitable. In 2017, a popular blockchain manufacturing company, Bitmain, increased its operations to include AI in the chips of its application-specific integrated circuits, or ASICs. Without a doubt, while technology, in general, is largely shifting toward machine learning and artificial intelligence, blockchain and cryptocurrencies are equally benefitting from the evolution of artificial intelligence.
Positioning Organizations for Blockchain and AI Convergence
Image: Data Driven Investor
Specific use-cases for combining blockchain and AI will depend on company needs but the underlying theme will be data. Blockchain will ensure that data is secure, private, and trustworthy. AI models will use this data to become more effective.
Companies can prepare themselves to develop combined AI and blockchain solutions by improving their digital and data capabilities.
We can say that executives must first determine the specific business needs and determine whether blockchain and AI can address these needs. If they already have AI initiatives in place, they can explore how blockchain could improve them. Alternatively, companies sitting on valuable data could monetize it by joining a blockchain ecosystem and sharing data with people building AI models.
For example, an autonomous car company could store data collected by its cars on a blockchain. When self-driving cars go mainstream, they will collect huge amounts of driving data from on-board cameras and sensors. This data is used to improve the neural networks powering self-driving functions.
Companies can prepare themselves to develop combined AI and blockchain solutions by improving their digital and data capabilities. Digital transformation is a precursor to AI and blockchain adoption. Managing data and business processes using digital systems provides AI initiatives with firm-wide data, enabling AI implementation at scale.
The Future of AI and Blockchain – What Can Businesses Expect?
Here are some factors for business leaders to understand how AI and blockchain might disrupt businesses in the future:
Due to the enormous disruptive potential in the combination of AI and blockchain technologies, business leaders might need to understand the new ways in which AI services could be used in the future.
Projects like SingularityNET and DeepBrain Chain, AI services, and cloud-computing resources seem to be moving towards a marketplace ecosystem. Business leaders can probably expect more granularity in AI services. These services could move from identifying objects in an image to specifically identifying moving objects in real-time.
Many of the projects are highly nascent and on the road to commercialization:
The technical complexity involved in AI or blockchain individually by themselves is significant enough for businesses to undertake projects in. Applying these technologies jointly for solving B2B business issues at scale still seems to be at least five years away at this point.
AI vendors and manufacturers might also find future opportunities using blockchain technologies.
It might be common in the future to see cases where AI might help blockchain projects such as improving scalability or security. There also seem to be several opportunities for blockchain to improve existing AI projects like in helping humans and AI understand why a particular decision was made by the AI engine or in lowering the capital costs for AI services through marketplaces.
The combination of blockchain technology and Artificial Intelligence is still a largely undiscovered area. Putting the two technologies together has the potential to use data in ways never before thought possible. Data is the key ingredient for the development and enhancement of AI algorithms, and blockchain secures this data, allows us to audit all intermediary steps AI takes to draw conclusions from the data, and allows individuals to monetize their produced data.
Both AI and blockchain involve technical complexity and there seems to be a sense of agreement among experts that these technologies will have serious business implications in the next five to ten years.
The underlying blockchain technology and AI methods already exist. We simply need increased blockchain activity, improved AI capabilities, and corporate adoption.
Thus, AI can be incredibly revolutionary, but it must be designed with utmost precautions — blockchain can greatly assist in this. Its potential for true disruption is clearly there and rapidly developing.
Cover Image: CityAM
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