If you think that virtual robots are not useful to solve the problems of people all over the world, well one created by the company DoNotPay contradicts you, because in 2 years he managed to get a lot of drivers to cancel 160.000 fines for parking and exempted him from paying several million dollars.
The virtual robot is available for free and was created to ask a series of questions to drivers who have received parking fines, based on the answers they are also offered advice that can help them cancel the parking fine, the success rate of the service being of 64% among those who used it in the last 2 years.
DoNotPay is currently active only in London and New York, metropolises with over 20 million inhabitants, and in 2 years over 250.000 people have used the service, the success rate of 64% being surprising, considering that we are talking, basically, about a robot that only asks you predefined questions and gives you predefined answers.
The creator of DoNoPay claims that his service will continue to be available for free, and its existence has definitely affected the earnings of some lawyers who offered advice for a fee to people who wanted to get rid of parking fines, the success in the two big cities motivating the company to expand availability in other areas as well.
"I think the people getting parking tickets are the most vulnerable in society. These people aren't looking to break the law. I think they're being exploited as a revenue source by the local government. I feel like there's a gold mine of opportunities because so many services and information could be automated using AI, and bots are a perfect way to do that, and it's disappointing at the moment that it's mainly used for commerce transactions by ordering flowers and pizzas."
DoNotPay is a very clear proof of the fact that certain services offered by people can be automated to be offered much faster by various virtual robots, and based on the success of this company there will certainly be other similar services that will enjoy a degree of use at least as high.
