Digital Twin Technology:

What is a digital twin?

Put simply, a digital twin copies a physical product, process, or service through various IoT devices, pairing the virtual and real worlds. The idea of twins is nothing new. The concept was used as far back as NASA’s pairing technology in the Apollo 13 project. But digital twin technology will only be able to show its full potential after IoT devices become widespread and affordable.

Digital twins, however, shouldn’t be confused with digitization. A digital twin does not substitute a physical item or process with a digital one to make it more accessible, efficient, or secure. It’s a precise replica of the physical object and a means of testing and monitoring it without needing to access to or testing on the real thing.





Using IoT data with digital twins enables us to gather data in ways that were never possible before such as with drones or directly from failing components in complex systems. This new data will give our companies the ability to determine how our products are being used, and how they can be improved.


To build effective digital twins, we need expertise in sensors, mobility, computing, IoT, and other areas, but the payoff is potentially enormous. From bridges and buildings to autonomous vehicles, we need to understand how products are being used in the real world and what challenges and failures those products are most likely to see. Digital twins give us that ability, and as a result, they are likely to be an important technology as the world becomes more connected by IoT.


Gartner predicts that by 2021, half of large industrial companies will use digital twins, resulting in those organizations gaining a 10% improvement in effectiveness. So this is the best time to start investing in digital twin technology. In next few years our company can expect digital twin development to trickle down into consumer goods and reach a higher state of adoption.

Who are the digital twin vendors?

There’s no clear understanding of the technology required for large-scale deployment of digital twins, their integration with other systems, and the management of hundreds of different types of twins. Ian Skerrett attempted to define the major features that digital twin platforms should possess. According to Skerrett, a digital twin should:
be able to manage the digital twin lifecycle
be able to be updated to reflect the exact state of each separate digital twin
have an open API that allows any system and IoT device to interact with a digital twin
provide a means for visualization and analysis
include process management features for scheduling checks, maintenance status updates, etc.
manage access and information sharing, enable cooperation within the system, and provide information on ownership, management, responsibility, etc.

General Electric, IBM, Siemens, and Microsoft are among the top players in the digital twin market. From GE Digital (with its Predix products) to Microsoft (with its Azure Digital Twins), tech companies are offering commercial digital twin solutions for different levels of production:
Components
Assests
Systems and units
Processes










1
https://www.challenge.org/insights/digital-twin-risks/
https://www.identitymanagementinstitute.org/digital-twin-technology-benefits-and-challenges/


2
https://www.gartner.com/smarterwithgartner/confront-key-challenges-to-boost-digital-twin-success/


4
https://www.intellias.com/digital-twin-technology-a-guide-for-2019/


5
https://www.intellias.com/digital-twin-technology-a-guide-for-2019/
https://www.gartner.com/en/newsroom/press-releases/2019-02-20-gartner-survey-reveals-digital-twins-are-entering-mai




8
http://usblogs.pwc.com/emerging-technology/digital-twins/


9
https://www.identitymanagementinstitute.org/digital-twin-technology-benefits-and-challenges/






g

AI (artificial intelligence)

Types of artificial intelligence

Arend Hintze, an assistant professor of integrative biology and computer science and engineering at Michigan State University, categorizes AI into four types, from the kind of AI systems that exist today to sentient systems, which do not yet exist. His categories are as follows:

• Type 1: Reactive machines. An example is Deep Blue, the IBM chess program that beat Garry Kasparov in the 1990s. Deep Blue can identify pieces on the chess board and make predictions, but it has no memory and cannot use past experiences to inform future ones. It analyzes possible moves -- itsown and itsopponent -- and chooses the most strategic move. Deep Blue and Google's AlphaGOwere designed for narrow purposes and cannot easily be applied to another situation.
• Type 2: Limited memory. These AI systems can use past experiences to inform future decisions. Some of the decision-making functions in self-driving carsare designed this way. Observations inform actions happening in the not-so-distant future, such as a car changing lanes. These observations are not stored permanently.
• Type 3: Theory of mind. This psychology term refers to the understanding that others have their own beliefs, desires andintentions that impact the decisions they make. This kind of AI does not yet exist.
• Type 4: Self-awareness. In this category, AI systems have a sense of self, have consciousness. Machines with self-awareness understand their current state and can use the information to infer what others are feeling. This type of AI does not yet exist.

Today, businesses of all types need to know how to implement artificial intelligence (AI), as the technology has changed the way organizations do business across the world, and will continue changing it into the foreseeable future. Those businesses that don’t capitalise on the transformative power of AI risk being left behind. So our company should start investing in artificial intelligence technology, to cope up with the other leading industries in this technology.

Mastering AI and data is one of the most important things that our company can do to transform ourselves into exponential enterprises, achieve exponential growth, and avoid disruption. The companies leading the way in AI also lead the way in breakthrough results in this time of disruption and accelerating change. By taking a focused approach to building a world-class AI capability, our company can join the ranks of the exponential leaders as well in next 3-5 years.  In near future smarter technologies/devices produced in our company and connected machines that will interact, visualize the entire production chain and make decisions autonomously will revolutionize the advancements in our business.  And it will improve the quality of life for the world’s population and raise income levels.

1.

Link: http://www.okapi.ai/lagging-behind-the-risks-of-not-using-ai-in-manufacturing-and-the-advantages-of-using-it/

2.

https://medium.com/@ryankhurana/the-benefits-of-artificial-intelligence-outweigh-the-risks-e7e7fd5c11ea

3.

https://codebots.com/ai-powered-bots/6-technologies-behind-ai

4.

https://venturebeat.com/2019/06/15/amazon-sends-alexa-developers-on-quest-for-holy-grail-of-voice-science/

5.

https://towardsdatascience.com/the-15-most-important-ai-companies-in-the-world-79567c594a11

6.

https://ankura.com/insights/regulatory-compliance-in-the-age-of-artificial-intelligence/

7.

https://www.stradigi.ai/blog/the-key-legal-issues-in-ai/

8.

https://www.centralbanking.com/regulation/3509606/artificial-intelligence-the-future-of-regulation

9.

https://towardsdatascience.com/security-and-privacy-considerations-in-artificial-intelligence-machine-learning-part-4-the-d02a2fa3f665

10.

https://medium.com/@sa_mous/ethics-in-ai-424919af7d3