The Robots are Coming!
by Andreas Francois Vermeulen
The world is positioning massive industrialized machine learning capabilities into core technology areas like banking, healthcare, manufacturing, and citizen services. The use of soft and hard robotics with machine learning executing the decisions is powering the industrialization of the future of the human race. Research shows that during Q1 of 2019, $5.1 billion (and $7.1 billion for Q2) was invested into artificial intelligence, commonly known as AI or machine intelligence. The investments into AI will push over the $10+ billion mark with ease in Q1 2020.
I have a number of personal projects across Europe that are demanding deployments of machine learning into healthcare facilities to improve the quality of healthcare across the European Union. The introduction of Internet of Things via medical devices transforms the face of healthcare by empowering healthcare providers to support patients outside the current ecosystems. The deployment of remote monitoring devices and near-real-time machine learning will enable patients to return to work while still been actively monitored to improve productivity of the people. Long-term care will be performed at home to release hospital beds for more urgent emergency care. The introduction of machine learning into healthcare is augmenting the capability of existing healthcare facilities to improve the quality of life for European Citizens. During my interaction with other global companies, I have discovered that this matches the evolution in healthcare publicized in Asia, Africa, North America, South America, Antarctica and Australia.
The next area that is changing rapidly is banking. The deployment of automatic authorizations of interactions between banks and their customers are growing the proficiencies of financial products, like auto-approved small loans. This area will expand this ecosystem and enable the future of banking to be an invisible force that will handle total end-to-end transactions. At this time, there are 100 million customers using internet-only banks, run by machine learning to expedite back office processing without any human interventions.
However, the most far-reaching impact AI has had on any ecosystem is manufacturing. Machine learning is now handling physical proficiencies to manipulate its surroundings via robotic arms and machine interfaces. This capability for machine learning to control the next generation of manufacturing environments is aggressively processing data into insights for immediate action within the fourth industrial revolution.
Manufacturing of hyper-personalized products is possible when the supply-chain can feed back requirements to personalize the product. Products adapt while in manufacturing.
The at-scale services from several governments worldwide are now converting onto autonomous machine learning for support. Citizens are directly interacting with machine learning models that evaluate what public services each citizen needs. Smart cities are managed by state-of-the-art machine learning that monitors and delivers critical citizen services on-demand.
Rapid Information Factory to Drive the Machine Learning
To achieve these advantages from industrial machine learning, you are required to use the standard rapid information factory as discussed in Apress books Practical Hive: A Guide to Hadoop's Data Warehouse System and Practical Data Science: A Guide to Building the Technology Stack for Turning Data Lakes into Business Assets to pre-process the raw data ready for machine learning decisions.
The introduction of the Six Zone Data Lake (workspace, raw, structured, curated, consumer, and analytic) and the Rapid Information Factory processing methodology supports the Retrieve-Assess-Process-Transform-Organize-Report data pipeline to convert raw data into business-insights. The resulting Time-Person-Object-Location-Event (T-P-O-L-E) data vault hubs are formulating from the harmonized data within the data lake zones. This typical data lake is the base for the enhanced machine learning. This supports better processing of any data stored in future of data lakes. This T-P-O-L-E data lake delivers an enhanced base for any approved processing via industrial machine learning models.
In my latest book Industrial Machine Learning: Using Artificial Intelligence as a Transformational Disruptor I show you how to find and use these improved processing capabilities across a range of industrial areas. All that is needed now are skilled data engineers and data scientists to convert this knowledge into Industrial Machine Learning to discover new business insights.
Are you ready to learn the future?
About the Author
Andreas François Vermeulen is the Chief Data Scientist and Solutions Delivery Manager at Sopra-Steria and he serves as part-time doctoral researcher and senior research project advisor at University St. Andrews on future concepts in health care systems, Internet-of-Things sensors, massive distributed computing, mechatronics, at-scale data lake technology, data science, business intelligence, and deep machine learning in Health informatics. Maintains and incubates the “Rapid Information Factory” data processing framework. He is active in developing next-generation data processing frameworks and mechatronics engineering with over 36+ years of global experience in complex data processing, software development, and system architecture. Andre is an expert data scientist, doctoral trainer, corporate consultant, and speaker/author/columnist on data science, business intelligence, machine learning, decision science, data engineering, distributed computing, at-scale data lakes. He holds expert industrial experience in various areas (finance, telecommunication, manufacturing, government service, public safety and health informatics).
Andre received his bachelor degree at the North West University at Potchefstroom, his Master of Business Administration at University of Manchester, Master of Business Intelligence and Data Science degree at University of Dundee, and Doctor of Philosophy at University of St Andrews.
This article was contributed by Andreas François Vermeulen, author of Industrial Machine Learning.