B20: What are the opportunities and challenges for your industry in a time of quickly advancing digitalization?
HW: The opportunities include:
The Industrial Internet is reshaping how we produce, distribute and maintain physical assets—from jet engines to small motors to entire manufacturing plants. As a growing, global network of connected assets, designed to apply split-second machine learning to big data, the Industrial Internet is expected to outpace the consumer Internet of Things (IoT) and deliver new value to digital businesses.
The Industrial Internet will fuel unprecedented gains in productivity and innovation for industrial companies, helping businesses extract valuable insights from assets to transform operations, enable innovation and open up new business models. GE Digital is the leading software company for the Industrial Internet, which is quickly becoming the most promising and lucrative market in the world for software.
HW: The challenges include:
Education, Skills & Culture. The pace of change is ever faster and organizations need to help their employees adapt. That’s why a Digital Transformation Playbook starts with great leadership and setting a great culture. Companies also need to think bigger – and beyond concepts like Industry 4.0. which is focused on manufacturing technologies alone. Companies need more than an Industry “version update.” They need a complete digital transformation, and that’s what the Industrial Internet provides.
B20: How is artificial intelligence changing the production for General Electric?
HW: Putting AI into context:
- Until recently apps such as Siri and Alexa were what most people thought of when it comes to AI - or machine learning. But the implications of this technology are much bigger. Today, AI is viewed in a much wider context which looks at connecting humans, machines and analytics to the internet and using sophisticated algorithms to optimise all interactions within this ecosystem. Essentially, machine learning is designed to augment work done by people / help them make better decisions.
- While we are confident that AI - or machine learning - will continue to generate a lot of value within the consumer and enterprise domains, we believe that its application across the Industrial Internet space represents a much bigger opportunity. Industrial AI is built as an end-to-end system, where data is generated by sensors on the edge, served to algorithms, modelled on the cloud, and then moved back to the edge for implementation. Between the edge and the cloud are supervisor gateways and multiple nodes of computer storage since the entire system must be able to run the right load at the right places.
HW: Why is industrial AI different to consumer AI? What are some examples of it in use?
- The stakes and responsiveness are much higher for industrial applications where millions of dollars and human lives can be on the line. In these cases, industrial features cannot be trusted to run on the cloud and must be implemented on location, also known as “the edge.”
- On a deep sea oil rig, a riser is a conduit which transports oil from subsea wells to a surface facility. If a problem arises, several clamps must respond immediately to shut the valve. The sophisticated software that manages the actuators on those clamps tracks minute details in temperature and pressure. Any mistake could mean disaster.
- In a manufacturing facility that crushes ores into platinum bars, bars that come out with the wrong consistency must be immediately detected in order to adjust the pressure at the beginning. Any delay means wasted material. Similarly, a wind turbine is constantly ingesting data to control operations.
- Despite the high volume of faulty data and limited processing power at the edge, industrial AI still needs to be incredibly accurate. If an analytical system on a plane determines an engine is faulty, specialist technicians and engineers must be dispatched to remove and repair the faulty part. Simultaneously a loaner engine must be provided so the airline can keep up flight operations.
- Finally, deep domain expertise in data science skills is changing the profile of people that we are hiring. We need real data scientists with great academic depth. Our European Foundry is one of our global hubs for these experts but we find them across all our businesses in Europe – and we’re going to need more.
B20: What would you want the G20 to do on cyber security?
Unlike the consumer Internet of Things with its increasingly ubiquitous devices, the Industrial Internet of Things is inherently business-to-business or business-to-government. Contracts are proving to be an effective means to address a range of cybersecurity issues, such as software updates and liability. Government policy should respect and promote freedom of contract, and the G 20 should make a clear statement and commitment in that regard.
With connected machines, such as aircraft engines and locomotives, routinely crossing national borders, the Industrial Internet is global. To avoid squandering the incredible economic promise of the Industrial Internet of Things era, governments should work with the private sector to adopt a global approach to cybersecurity and refrain from balkanizing cyberspace with incompatible national cybersecurity regulations.
GE and other Industrial Internet service providers place great emphasis on strong cybersecurity. Data stored in the Cloud can be as secure—if not more secure—than data stored on customer premises. Also, cybersecurity is also a shared responsibility. Both vendors and customers have responsibilities. A study conducted by Tripwire found that fewer than half of IT professionals had recently updated the router firmware in their homes. This is a great illustration that, even when vendors do their part, customer must do theirs.