Realtime modeling that includes realtime data is starting to be called digital twinning in various industries. E.g. each car off a line has its specific torques of each bolt traveling along with it as a digital twin.
Power plants with sensors on all the equipment trying to predict preventive maintenance is being called digital twinning too.
Is this only applied to 'real time' modelling or is 'near real time' also included?
Curious because I used to write software for IIOT predictive analysis (3 years ago?) and I never heard the term 'digital twin.' Curious if it applies to NRT or if this is a different beast.
I work in a field adjacent to all this. My org used to use the term "real time" to mean anything up to and including near-real time. We've recently switched to calling our work "in time" prediction to clarify that we're not referring to real-time in the RTOS sense. Digital twins are a hot topic in aerospace maintenance right now.
I think I've figured out the IBM-style buzzwords like "mobile productivity cloud", and I'm making good progress on all the ones in SJC airport ads, but the one that's still got me is "digital transformation". Shows up all the time in my promoted tweets.
Didn't everyone digitally transform in like the 90s?
You can't just affix the word realtime to everything and expect it to make sense. And of course every car manufactured in the last 30 years used tools with exact torques, no worker is pressing on a drill until it feels just right.
The exact torque measurement within a tolerance is what's recorded, which is different for each car. Less trivially, the vibrations, driving history, etc can be put on the digital twin.
It’s the new buzz word for non-tech Gartner/EY/Deloitte consultants. It’s basically the new blockchain/RPA/BigData/AI. So far only RPA has brought us any value, and it comes with a ton of technical dept and huge amounts of panic when the Human Resources have been relocated and the robot suddenly fails.
Is it bizarre? It's a general term that can be applied in many contexts.
A digital twin is a high-detail, precise, simulacrum of a physical object or objects and their relationships. It's more than modeling traffic flow, it's a way to refer to the practice of digitizing the state of objects.
I've heard the term before in the context of manufacturing, so it doesn't sound odd to me.
It's an odd term, but one that inadvertently brings attention to concerns such as privacy and identity persistence.
Such concerns are far less obvious to laypeople when terms like "real-time modeling of traffic flows" are used. Perhaps, the choice of term is fortunate after all.
Though the term digital twin was occasionally in use in the virtual reality and manufacturing community as a concept in the mid-1990s, Dr.Michael Grieves explicitly defined the term in 2001 for a digital version of a physical system as part of an overall product lifecycle process.
The term has been widely used in manufacturing since then and has been a key sales messaging for companies like IBM, Dassault Systems, Siemens amongst others. United Technologies for example have been demonstrating digital twin concepts for routing in flight engine telematics into 3D virtual engine simulations for trouble shooting and maintenance since 2005.
The term regularly crops up in manufacturing, maintenance, smart cities, IoT and more recently AI related proposals and projects and generally means a virtual simulation that uses real data to simulate a complex system.
As pointed out by many over the years - the concepts are also described by Prof.David Gelernter in his book 'MIrror Worlds: Or the Day Software Puts the Universe in a Shoebox...How It Will Happen and What It Will Mean' first ipublished in 1991.
SO perhaps less bizarre and more just less familiar on HN.
IIRC it's called "digital twin" because they basically use real-world data to then create similar plausible fake data and then use the "real-ish" data to model things. It's thought to reduce privacy risks since "real-ish" data is not tied to any actual person, so you restrict the exposure of real, sensitive, de-anonymizable data to the model generation.
I have no idea if this actually works out in practice, though, in regards to both how much this actually protects privacy and whether or not "real-ish" data proves to be accurate enough to be useful.
To me, the term "digital twin" evokes memories of "big data"
* Big data - falls prey to the fallacy that quantity of data can allay concerns on quality, sampling bias and unclear directions of causality
* Digital twin - falls prey to the fallacy that if you simulate EVERYTHING it will solve ALL YOUR PROBLEMS AT ONCE
...both the sort of thing that computer scientists come up with before they learn something about empirical science (and I should know, having made that mistake myself).
People model things because they need to predict stuff. To make a good model you need to know what it is you're predicting, and outside of physics, a model designed to predict X will rarely be great at predicting Y because the map is not the territory and a model designed specifically for Y will do better.
The term "digital twin" however implies that someone is mistaking the map for the territory. Ok I see the point of trying to predict lots of outcomes from one model, X,Y,Z,A,B,C... if the model was explicitly designed to do this then "digital twin" is just a buzzword for a package of models predicting XYZABC, with standardized data formats, not duplicating effort, and hopefully taking care to avoid all the pitfalls we've learned the hard way from 70 years of transport planning.
But if it's anything more than a buzzword it carries the implication that we just build a magic "twin" and can then predict anything we care to name, which I suspect is not going to work. That said, I'd be interested to hear a counterpoint from anyone more involved in one of these.
