In between meeting with customers, crowdchatting with our communities and hosting theCUBE, the research team at Wikibon, owned by the same company as SiliconANGLE, finds time to meet and discuss trends and topics regarding digital business transformation and technology markets. We look at topics from the standpoints of business, the Internet of Things, big data, application, cloud and infrastructure modernization. We use the results of our research meetings to explore new research topics, further current research projects and share insights. This is the sixth summary of findings from these regular meetings, which we plan to publish every week.

The combination of faster, cheaper and memory-rich hardware, coupled with unprecedented streams of data, has renewed interest in an old favorite: artificial intelligence.

But this time AI and its progeny, “machine learning” and “deep learning,” are generating real returns in a wide array of industries and applications. We’ve written about a number of them at Wikibon: machine learning systems that extend the useful life of ERP systems in the grocery business; digital twin software that can dramatically improve automation in complex operations; and rapidly evolving technologies for accelerating productivity in information technology operations management, or ITOM, without which advances in other digital business domains would be impossible.

Last week, Apple announced iOS 11, and with it a new security feature based on facial recognition. This function is an application of deep learning. That got the Wikibon research team thinking: Where will deep learning processing take place? Is all data going to be moved to central cloud locations for processing? Or, as Wikibon’s Jim Kobielus observes, will deep learning be baked into all edge endpoints?

We believe that the architecture for deep learning, machine learning and other data rich variants of AI will be:

* Centralized training and testing.

* Distributed, edge-based execution.

Again, Jim Kobielus explains:

Why? Because the costs of moving data in real-time are extreme — to the point of being impossible, where latency is a problem. Moreover, the rapid advances in hardware technologies that are powering the development of the cloud are also reshaping computing possibilities at the edge, in local machines and human-friendly, mobile devices. Wikibon’s David Floyer explains:

If advanced software function for data-rich applications is going to be processed at the edge, closer to the point of action, what does that mean for the future of devices? It means we’re going to see a lot of demand for increasingly powerful clients, across a lot of form factors — many of which, as Jim Kobielus explains, don’t immediately evoke the notion of computer:

All of this will be brought to you by increasingly diverse and specialized roles, some — like data scientists — are nascent, while others — like application development — are about to undergo rapid transformation to tackle these new challenges. As Jim Kobielus observes:

Ultimately, these “intelligent” technologies will catalyze an accelerating demand for deeper business collaboration among an expanding array of disciplines. Without smart people working together to conceive of, properly architect in terms of data realities and build or buy intelligent systems, we’ll end up spending a lot of money on a brand-new generation of high-tech paperweights.

Action item: Business leaders must explore the new generation of artificial intelligence technologies, which will have profound product, operations and customer experience implications in all industries. Many hold a false hope that adopting these technologies will be made easier by centralizing all function in public clouds. This is wrong. The distributed edge is where most of the intelligence will land, which will place new emphasis on hardware technology and technology management disciplines.

Image: geralt/Pixabay