Digital Twin Applications Likely to be Commercial Before Many Metaverse Apps

Though consumer applications for metaverse seem to get more attention, digital twins are likely to produce more value, near term. 

A digital twin is a virtual representation of an object or system and is a type of “metaverse.”  It is updated from real-time data and uses simulation, machine learning and reasoning to help decision making, often to model processes and flows. 

That might be especially useful for complex systems, as when operators of such systems want to look at possible changes; predict breakdowns or predict maintenance activities. 

source: Leeway Hertz

When used to monitor and possibly avoid traffic congestion, digital twins might be useful to optimize traffic flows. Rerouting traffic around accident delays is one possible advantage, assuming the ability to manage traffic lights, open or close routes. 

In manufacturing, digital twins might be used to rapidly create prototypes or optimize supply chain support. In health care, assuming the knowledge base is refined enough, digital twins might help doctors evaluate outcomes from various treatments.  

Will Current AI Interest Produce Spring, or Another Winter?

Does our experience with mobile computing have relevance for our predictions about useful and commercial use cases for artificial intelligence? Possibly. 

Initially, mobile internet, for example, was sort of a “mobile version” of the desktop experience, allowing users to keep doing things they were doing at their desks. But the mobile experiences sometimes were "de-featured" versions of what could be done on desktop devices. Mobile email was a salient exception: mobile utility was higher and ease of use about the same.

The analogy is mobile voice, which allowed communications away from the desk or cordless phone. Lots of value was created.

Then we moved into an era where the mobile device was the preferred device for many use cases, including photo sharing and social media. 

The proliferation of mobile apps was a characteristic of that era, where hotel check-in, for example, was expected to be invoked on a mobile device. 

Now there are many use cases where mobile is itself the native environment, preferred to desktop use cases which do not work as well, if at all.  Turn-by-turn driving instructions or ride-hailing services provide examples. 

source: Medium 

So are there implications for adoption of practical AI? Maybe. 

The difference in the case of AI is that development has not been strictly linear. At least in terms of expectations, we have moved through alternating periods where expectations were greater or lesser. 

It might be quite fair to say that interest in the near-term value of artificial intelligence has moved through periods of inflated expectations before, even if we are at present in a period of high anticipation. 

source: Saegus 

Historically, interest in the immediate value of artificial intelligence has waxed and waned. 

source: Medium