What happened to blockchain?

The topic of blockchain began with the introduction of bitcoins in 2008 and has developed beyond cryptocurrency to become a transformative force in business life. At its core, blockchain is a technology for data storage in decentralized distributed networks that enables secure and transparent transactions without intermediaries. It has already revolutionized entire processes and IT landscapes and opened up great potential for new business models in a wide range of industries. Despite the recent hype, the term artificial intelligence has been around for around 70 years. The first AI program "Logic Theorist" was introduced in 1956. Since then, AI has developed rapidly, particularly through advances in machine learning, neural networks and deep learning algorithms. AI systems are able to analyze huge data sets, recognize patterns and make decisions with remarkable precision. From chatbots and virtual assistants to predictive analytics and autonomous vehicles, AI applications have become an integral part of modern businesses.

Synergies of blockchain and AI

In today's dynamic business landscape, the convergence of blockchain and artificial intelligence is transforming industries, driving innovation and redefining the way businesses operate. Both technologies individually provide powerful tools for
The combination of the two, however, is still in the early stages and sometimes encounters difficulties in terms of scalability, performance, data interoperability and compliance. However, the combination of the two is still in its early stages and sometimes encounters difficulties in terms of scalability, performance, data interoperability and compliance. The key characteristics of blockchain - decentralization, transparency and immutability - make the technology a powerful tool. However, as it becomes more widespread, the complexity and volume of managed data also increases. This can pose considerable challenges in terms of data processing and analysis. AI can offer significant improvements here. It automates complex processes, processes large volumes of data and recognizes patterns within it. It also continuously develops through learning. Generative AIs can even learn to write, speak or design graphics like humans using suitable training data.

They therefore offer many potential fields of application, automation options and efficiency gains. It is therefore obvious that the characteristics of blockchain and AI are highly complementary, which holds potential for enormous positive synergies. AI can optimize blockchain operations, improve data analysis and enhance security measures through advanced predictive analytics. AI, in turn, can benefit from the secure and transparent data management of blockchain to ensure the integrity of data for training AI models and support the development of decentralized AI systems.

Blockchain strengthens AI

The extensive learning capabilities of AI in relation to data structures enable automated decision-making and data analysis. However, the path that leads to a result and the result itself are often neither reproducible nor transparent. This lack of traceability can lead to trust and ethical problems. Linking AI and blockchain could solve some of these problems and improve the capabilities of AI at the same time.

Blockchains are suitable for creating a traceable, tamper-proof and therefore well-controlled database for AI training, which strengthens trust in the results of the AI. The use of cryptographic processes and the creation of consensus among independent parties who confirm the authenticity and immutability of data or digital values ensure a high level of security. Companies and their partners therefore retain full transparency about the data used to train their AI models. For example, they only authorize certain, particularly qualified users to share high-quality data sets through monetary incentives instead of ethically questionable training on social media data. In this way, they can drastically reduce the risk of false or inaccurate results. But that's not all - blockchain also enables the secure labeling and certification of AI results as well as watermarking, which strengthens the credibility of digital content and exposes misuse.

How blockchain wins through AI

Not only do AI models benefit from interaction with blockchains, but blockchains also benefit from the combination with AI. There are a whole range of advantages for blockchains, such as faster processing speeds: Traditional blockchain networks, such as Bitcoin, were often rather slow due to high demand. To improve performance, their developers therefore often use workarounds such as sharding. The idea behind this: In order to be able to process several processes in parallel, the overall network is segmented into several sub-networks (shards). AI, on the other hand, could also increase speed (and reduce energy consumption at the same time) by analyzing and predicting typical usage patterns. This allows the existing resources of the blockchain to be allocated and managed much better.

Another advantage is the optimized interoperability between different networks. Artificial intelligence not only ensures greater speed, but also helps to create new opportunities for cooperation between different blockchains. This is also made possible by machine learning algorithms that are trained to understand and translate information from different networks - and thus facilitate the exchange of data between them. However, it still needs to be clarified in advance how this data exchange can be reconciled with the right to informational self-determination. Cryptographic methods such as zero-knowledge proofs, homomorphic encryption or secure multi-party computation (SMPC) provide a certain degree of privacy protection, but are either insufficient or too complex for widespread use in companies. AI models that selectively conceal personal data may provide a remedy here. However, clear regulatory requirements that clearly define which of these models are permissible in principle would be desirable.

Better fraud detection: Predictive processes that use machine learning algorithms to search the transaction history of a blockchain for common patterns can also help to identify and prevent fraud attempts at an early stage (fraud prevention and detection). The system defines atypical transactions as indicators of possible fraud attempts, which it proactively warns users about.

Outlook: Convergence

As a result, the combination of AI and blockchain holds transformational potential for many industries. In finance, it can lead to more secure u DeFi platforms that use AI for dynamic interest rates and risk management. These are virtual, decentralized financial trading venues that operate independently of individual institutions and offer companies a wide range of financing instruments. In healthcare, blockchain-backed AI can improve patient care through personalized treatment plans based on immutable patient data. Supply chains can become more transparent and efficient with blockchain-based traceability and AI-driven predictive analytics, and this convergence leverages the strengths of both technologies - the secure, transparent and immutable ledger of blockchain and the ability of AI to analyze and learn from data - to create powerful new applications and use cases.

The industry continues to work tirelessly on innovative use cases and applications. These relate to a wide range of topics - from digital identities to product passports and digital certificates of authenticity, from shared data for cross-company transactions to IoT communication. This results in a wide range of new solutions for current challenges: for example, to make it easier to meet verification obligations in supply chain management or to facilitate digital trade through NFTs.

In the long term, these solutions offer plenty of scope for significant efficiency gains, new business models and options for data monetization. For companies, it is now only a matter of actively shaping this future and making intelligent use of the resulting opportunities. This requires not only the courage to embrace change but also a sound basis for decision-making. MHP supports companies in precisely evaluating the impact of innovative technologies on products, processes and business models. The focus here is on the actual economic added value. With comprehensive knowledge in the areas of industries, technologies and business assessments, companies receive the necessary basis to make well-founded investment decisions and secure their long-term competitiveness.

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