[caption id="attachment_18654" align="alignright" width="150"] Joe Baguley, VP and CTO, EMEA, VMware[/caption]
Since the dawn of time, organizations and individuals seek ways to gain an advantage. It’s why we’ve been in the “age of innovation” for more than 200 years. And speed is often the key to finding that edge.
You can look as far back as the 1830s to the tale of François and Joseph Blanc. The two found an elaborate (albeit illegal) way to influence how information was transmitted from Paris to the stock exchange in Bordeaux. As bankers traded in government bonds, they quickly realized they could make money by being one step ahead of their competition as information was passed along the mechanical telegraph system.
Optimistic technologists believe we are currently in the golden age of innovation. Digital technology is transforming the underpinnings of human existence. We’ve seen why with the various roles technology plays in our collective response to the pandemic. Now, with technologies like edge computing, artificial intelligence and machine learning, we can look to what’s possible as businesses, governments and communities plan their next moves.
Information + Speed
What links the above scenarios is the speed of decision-making: the right person having the right information to make decisions at the right time—and being able to act on this at speed.
And while a whole bunch of technologies are enabling this—cloud, mobile, networking, among many— the use of ML/AI and edge computing in support of application delivery. Getting that information intelligently into the hands of users to effect change can offer a wealth of possibilities in this new world we are trying to map out.
The intelligent computing that ML/AI brings, combined with the movement of compute and data to the location where it is needed can improve response times. This combination can also play a role in contact tracing and social distancing efforts to help people and businesses function as close to normal as is possible.
AI & Edge Use Cases for the “New Normal”
In healthcare, AI is being embraced in track, trace and test applications to deliver accurate and real-time insights. This type of app can support the appropriate restrictions for small geographical groups in society where there are COVID infection outbreaks, rather than bringing a whole country to a stand-still.
In surveillance and monitoring, AI is built into surveillance cameras. This can help construction firms, for example, monitor building sites to ensure workers are wearing face masks or protective clothing. Of course, this type of monitoring technology can also be used for more nefarious reasons. IBM recently said it would no longer develop general purpose facial recognition technology in a push to control the ethics of emerging technologies. So, we need to specific and considered in our way forward. Yet, we should move forward and show how these technologies can be a force for good.
Take any transport network, which are vital to getting economies running again. Managing massive volumes of people will be critical in a socially distanced world, such as:
- The number of passengers on a train platform.
- The number of people on a bus.
- The capacity of the arriving and departing vehicles.
These situations require split-second decisions about the number of people and the various routes they will take. This is a great use case for these emerging technologies. With this, however, an AI system combined with sensors could holistically assess demand management and identify pinch points in, effectively, real-time. Thanks to the edge positioning, the AI generated information could be relayed to staff. These experts could instantly implement rapid deployment of updates, re-routing or additional capacity, as needed. By moving the application away from a cloud-based data center to the edge, this can all be done in microseconds, rather than seconds or even minutes—the difference between the system working and not.
Elsewhere, shops are now open with strict social distancing measures. But managing large shopping malls and the volume and flow of people is a difficult proposition. Using multi-functional sensors, AI-enabled applications at the edge can reveal and predict which locations are most likely to experience high levels of shoppers. Teams can then put countermeasures into place, such as curbing the numbers entering the mall in real-time and automatically restricting access.
Finally, think about warehousing. A recent study among shoppers found that online orders increased by 96% from April 2019 to April 2020. Think about the pressure on backend warehousing and distribution. Even with minimal workers onsite, edge and AI can support the scaling of activity safely. These technologies can help distribute workers most effectively all the while ensuring social distancing. And through predictive maintenance, organizations can proactively detect minor anomalies and redirect resources before threats become real.
[caption id="attachment_22412" align="aligncenter" width="1024"] Hear what tech leaders think about opening back up offices at scale in this executive roundtable, featuring (left to right): Paul Green, CIO at Angel MedFlight; Jeremiah Chunge, head of alternative channels and technology at Genghis Capital Ltd; Bask Iyer, CIO and CDTO at VMware; Phares Kariuki, chief executive officer at Node Africa; and Didier Sabardu, deputy global head of digital workplace at Société Générale.[/caption]
An App-Centric World
What all of these use cases show is that more and more apps are being required by governments and businesses to solve problems that require fast decision-making. This is what this AI and edge, or indeed a combination of the two, can provide. Processing data closer to its source radically improves response times, reduces latency and saves bandwidth.
For decades, AI lived in data centers, where there was sufficient compute power to perform processor-demanding cognitive tasks. This works fine when immediacy is not paramount. The issue is that more and more applications require instant or near-instant reactions to the information. Moving front-end information gathering to the edge and then applying AI intelligence allows systems to use inference (how AI uses observation and background to reach a logical conclusion) for faster decision-making.
However, it is critical that we realize the security risks involved. By processing more data at the edge, an enterprise’s digital footprint expands. This must be secured. The rest of the application resides in a cloud or data center. In doing so, enterprises can deliver security and compliance, without hampering the overall experience.
Unprecedented, Edge-Enabled Decision Making
As we get closer to capturing and processing data at the point of need, organizations can offer unprecedented levels of genuine innovation—in healthcare, warehousing, rail networks and beyond.
But at each endpoint, there are security risks involved in the data transfer. That’s why edge isn’t going to replace cloud-based apps. It’s going to sit alongside it as a necessary complement to allow organizations to get the most from their applications and data. In doing so, organizations ensure that the right data is secure and compliant without compromising on the experience. Controlling and managing that in a secure, simplified manner will be key and having global consistency knits it all together.
Looking at edge as part of a whole means enabling the ability to:
- Make changes, as required.
- Secure applications, as appropriate.
- Bring the intelligence to make better decisions faster.
- Manage disruption in this new world.