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How AI and Machine Learning Converge with Distributed Ledger Technology

By: Carla Chinski

Twitter: @@carlachinski

Post Date: 2024-01-19

AI-Driven Customization in DLT: A Comprehensive Guide


As Distributed Ledger Technology (DLT) converges with AI and Machine Learning (ML), one of the emerging trends is the creation of customized enterprise generative AI models. Unlike the vast, general-purpose models like ChatGPT, these specialized models are being developed to cater to specific business needs. This approach is particularly beneficial in industries like healthcare, finance, and legal, where the requirements are unique and complex. Organizations are adapting existing AI models to their specific domains rather than building new models from scratch, which is more resource-efficient. This customization not only makes these tools more relevant to specific industry needs but also enhances privacy and security, as it allows organizations greater control over their data, a crucial aspect in a world increasingly concerned with data privacy.


Existing research has explored blockchain designs with proof-of-useful-work mechanisms, potentially beneficial for machine learning (ML) model training. Such mechanisms have been seen in various blockchain-based cryptocurrency systems, though their practical relevance is limited due to low market capitalization. Future research avenues include analyzing the economic and security aspects of proof-of-useful work in AI, especially considering the potential risks from attackers with high computational power.


Unlike conventional transaction systems, Distributed Ledger Technology (DLT) systems are situated among companies, which helps prevent the duplication of transaction records and facilitates data interoperability. As a result, DLT ensures reliability by eliminating intermediaries without central authority [10, 26]. Leveraging these features, a distinctive digital identity that mirrors its real-world equivalent can be established, influencing auditing methods and compliance practices.


When these two technologies integrate, and according to research, industries and businesses “have the potential to overcome the drawbacks of centralized control, market power, valuation, privacy, and security concerns by providing a decentralized infrastructure that enables both, secure data-enabled learning, and data valuation without an intermediary.”


Even if the predictability of the benefits of this approach is low, there is no denying that data valuation–paying according to data quantity, quality, and type of exploitation–can benefit from Machine Learning to automatize these processes. When taking into account that there are many ways to value data (it can be cost-oriented, market-price-oriented, risk-oriented, and usage-oriented), not having intermediaries can help with key business decisions; such as who have ownership of specific datasets; tradeable rights; and types of data transactions in NFT (Non-Fungible Token) form. 


How We Get There: Learning About (and With) DLT and ML and AI


With the integration of AI and ML into DLT systems, there's a burgeoning demand for professionals skilled in designing, training, testing, and maintaining these complex systems. This demand extends beyond the realm of big tech companies to virtually every sector that relies on IT and data. The skill set required is not just limited to AI programming or data analysis but also includes MLOps (Machine Learning Operations), which focuses on deploying, monitoring, and maintaining AI systems in real-world settings. The challenge lies in the current scarcity of such specialized talent, highlighting the need for more focused education and training in this field.


With Machine Learning specifically, it could be used to predict server downtime, and data costs, predict usage types, and how to implement said datasets for a specific action. If and when this becomes a reality, businesses should be mindful of having a loss margin and profit margin within the cost structure of the model. 


This can also act as a space for data management, governance and visualization as a series of services based on DLT. In short, the data business model is, itself, decentralized:  

“a decentralized data fabric with built-in services such as a digital twin visualization service, tokenization, federated machine learning capabilities, and an access and authority management, as well as a DLT-enabled data valuation meta space”.


The issues present in ML and DLT must be recognized, no doubt: on the one hand, the difficulty surrounding optimizing and adjusting an ML model to be used with DLT, which could cause blockchain forks and for security to be compromised. The proof-of-useful work for AI needs an ML architecture, training data, and test data, which is a challenge in itself. Some researchers have proposed a hybrid model in which the data itself is not stored on the blockchain, but hashes of the data are.


Other Projections for DLT and AI Integration: 2024 and Beyond


There's also a focus on off-chain computation in Trusted Execution Environments (TEEs) for maintaining data integrity. TEEs offer a balance between computational power and confidentiality and are used in both AI and DLT fields for tasks like ML model training and enhancing smart contract performance. Further, DLT-based federated learning protocols currently lack integrity assurance in model training calculations. Future research should explore this area, especially in semi-trusted consortia. Additionally, the application of game-theoretic mechanisms to encourage honest computation in ML model training is a promising field. Advancements in cryptographic technologies, like homomorphic encryption, are needed for secure computation on encrypted data, which currently faces challenges due to high computational overhead. 


Another significant trend is the emergence of "Shadow AI" within organizations. This phenomenon refers to the use of AI tools and applications without formal approval or oversight from IT departments. As AI tools become more accessible and user-friendly, employees across different functions are experimenting with these technologies, often bypassing official channels. While this showcases a proactive and innovative spirit, it also introduces risks related to security, data privacy, and compliance. Organizations in 2024 are expected to address this challenge by implementing governance frameworks that balance innovation with security and privacy concerns, and this can be achieved through Distributed Ledger Technology.


The field of AI is also seeing remarkable advancements in creative and multitasking domains. For instance, the development of text-to-video AI models is transforming the entertainment and marketing industries. These models are capable of generating short video clips with quality comparable to major animation studios, opening up new possibilities in filmmaking and advertising. Furthermore, the application of generative AI techniques is enabling the development of more versatile robots capable of performing a range of tasks, moving away from the traditional approach of designing robots for specific, singular tasks.


In summary, the convergence of DLT with AI and ML in 2024 is paving the way for more customized, efficient, and versatile applications across various industries. However, this advancement brings with it challenges in talent acquisition, data privacy, and the ethical use of technology. The potential of these technologies is immense, but it requires a balanced approach that considers the ethical, regulatory, and practical aspects of their implementation.

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