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The Unparalleled AI Platform using High-Performance Computing to Scale Industrial Operations

Periodic Reporting for period 1 - AIASGA (The Unparalleled AI Platform using High-Performance Computing to Scale Industrial Operations)

Reporting period: 2018-08-01 to 2018-11-30

The machine learning (ML) and large-scale data analytics areas are recognised as enabling technologies across a wide range of industry sectors and software applications. They underpin Industry 4.0 and drive the ‘big data’ agenda, but their application in the process industries is complicated by the huge volume, variety and velocity of data being collected. Existing solutions focus on either machine learning or large-scale analytics. The objective of this Phase 1 feasibility study was to evaluate the viability of integrating a scale-up architecture with an ML-based engine capable of handling huge-scale data workloads, while making sense of the collected data in real-time.
Over the past 4 months we have evaluated the technical, commercial and financial viability of our product to confirm the feasibility of our project. We have performed a detailed market analysis, verifying the commercial potential to support our go-to-market activities. We have undertaken a global Freedom-To-Operate analysis and on the technical side, we have continued to verify the specification of our product. We have developed an execution plan for further development, including a detailed business plan, financial plan and, projections based on the marketing and price policy. The business idea remains intact and we believe that AIA MFlux will be a disruptive innovation within the High-Performance Computing and Machine Learning sector, particularly within the segment focused on the Upstream Oil and Gas sector.
As the population grows and the elderly account for a larger proportion, we believe the global challenge will be to ‘create more with less’. When commercialised, our product will drive operational and resource efficiency across multiple industry sectors, positively impacting the global agenda surrounding scarcity of resources. Furthermore, we will create a credible EU based capability in machine learning to provide a regional alternative to the solutions coming out of the US and China.