Blockchain to Aid Artificial Intelligence for Overcoming Deep Learning Drawbacks

By CIOReview | Monday, January 28, 2019

Artificial intelligence (AI) and blockchain are in their research and development phase and are anticipated to dominate the industry with their capabilities. On one hand, AI has powered machines with complete automation through its subset machine learning, specifically deep learning; enabling machines to learn from its environment and respond accordingly.  On the other, blockchain promises to overcome the current data storage, integrity, and security challenges with its distributed and immutable ledger. Convergence of both seems to make a greater impact.

Addressing the Black Box Problem

Deep learning is a powerful yet complex machine learning model that comprises of thousands to millions of nodes grouped in multiple connected layers—input layer, output, and several hidden layers. A network learns means that these nodes are fine-tuned to produce the right result in the output layer. Still, after numerous research machines are not able to achieve 100 percent accuracy and scientist strive to know why exactly the particular node configuration was used to achieve the net result by machines. This problem is only known as the black box problem of machine learning and AI.

Blockchain can aid data scientists by giving an immutable and transparent record of all AI decisions. Having a track of how each decision was made will enable humans to intervene in required instances. With a better understanding of AI’s decision making procedure data engineers can create better models for AI to operate on and learn. Although, this will not solve the black box problem completely but will allow humans to understand and predict AI outcomes.

Data Reliability

Neural networks’ efficiency relies on the quantity and quality of data that is fed to it. Insufficient or Unreliable data will make the network less efficient and biased. Enormous data storage requires a secure, scalable, reliable, and agile solution. T which blockchain fulfills the entire requirement, as it, self- authenticates the data, secure with cryptography, and has high scalability which will ensure availability of sufficient data for analysis.


Huge data sometimes result in data overlap influencing networks decision making. Network learns from historical data but the continuous influx of new data can lead to overlapping. At times, data scientist intentionally overburden the model for increased efficiency—which is not good. Blockchain will allow tokenization of incentives for scientist impelling them to create models for prospective analysis instead of retrospective analysis, eliminating the attempt to overtrain AI models.