A study by Oxford University and Yale University indicates that AI will outperform humans in many ways and will automate all human jobs in the next 120 years. By 2024, AI will be better than humans at translation, will write bestselling books by 2049, and will perform surgeries by 2053. Machine learning (ML), the proficiency of a machine to mimic human ability to accumulate knowledge and use it to drive insights, is generally considered the basis of AI.
Data is the driving force for AI
Although AI might depend on its machine learning abilities, we need to take a step back and realize ML doesn’t happen in a vacuum. ML is driven by big data, without which it can’t take place. Effectively, therefore, AI depends completely on the amount of data we can capture and the methods we use to process and manage it. For this reason, we need to pay more attention to data capture, transport, processing, and storage if we want to realize the promise of AI in the future.
Data Capture is pivotal
Capturing data is essential, whether it’s for software-based AI applications, smart robots based on AI, or machine learning. When AI products were initially designed, developers spent huge research and development resources collecting human behavioral data, both on the industry side and the consumer side.
The AI/ML Business Dilemma
AI/ML SaaS startups, companies, and enterprises share a common understanding of the value AI/ML can bring to automate human and business-centric processes. Yet there is a strong misunderstanding of how AI/ML can be implemented at the client with an alignment of interest.
Consider, for example, company A, a specialist in deep learning, has trained over years in a convolutional neural network for PDF document recognition. In fact, company A is a world leader in this domain and can help company B to automate finance and accounting by scanning invoices.
While both companies agree that A’s technology is beneficial for company B, they have conflicting interests regarding the deployment:
- Company A’s core intellectual property is the PDF network. Clearly, A is afraid of copycatting the model as the core business value. Infringement is something company A can detect, but it can hardly counteract given the merely infinite resources company B has.
- Company B’s interest lies in the protection of data. B is afraid that sensitive data is leaked via processing invoices like customer names, addresses, and transaction details which has severe consequences for business and customer relations.
So far the parties had to agree to run the network in A’s or B’s environment. Neither of the two choices meets the interests of both parties, which complicates the business relationship from the first day on.
Solution: AI/ML Vault
enclaive’s confidential computing technology can help here. For the very first time, it is possible to find an equilibrium between AI/ML experts and data owners. What companies A and B need is an AI/ML network vault.
How it works
AI/ML Vault Benefits
Using enclaive’s confidenital container technology to realize the vault has the following benefits:
- protection of data/code at any moment in time thanks to the run-time memory encryption comparable to a HW module
- develop, integrate and deploy the vault on-premise, in a public or public cloud
- real-time audibility to remotely verify data and model run in a confidential environment
- vault is linkable to a particular platform, enabling a clear licensing of the AI/ML network
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