A digital twin is a virtual representation of an object or system that is updated from real-time data, and uses simulation, machine learning and reasoning to help decision-making. The first practical definition of digital twin originated from NASA in an attempt to improve physical model simulation of spacecraft in 2010.
With the help of a digital twin, companies can test and validate a product before it even exists in the real world. Generally, by creating a replica of the planned production process, a digital twin enables engineers to identify any process failures before the product goes into production.
The use of cognitive computing increases the abilities and scientific disciplines in the digital twin. Technologies and techniques such as Natural Language Processing (NLP), machine learning, object/visual recognition, acoustic analytics, and signal processing are just a few of features used for creating digital twins.
Going forward there will be a transition towards cheaper, more accessible, and easier to use digital twins culminating in its democratization. Individuals and smaller companies will have access to the benefits as well as larger companies, increasing the use of the technology rapidly.
Hosts: Em Tombash, MD, Todd Ponsky, MD & Ramy Shaaban, MD
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