We've previously written about the use of Ai to automate vehicle inspections, how it works and its advantages. Now, we will delve into some areas where it can be used and how it benefits said businesses. Many businesses can be improved and optimized with the use of automated vehicle inspection, all this through AI and machine learning. It will cut costs, vastly reduce time consumption, and increase reliability by eliminating human error. This are just a few examples of businesses that can be improved by this technology.

The Startup of the Week for this week is London-based insurtech startup Tractable! Using AI to help accelerate accident and disaster recovery. Tractable achieves this by performing visual damage appraisals with computer vision, essentially utilising AI technologies to analyse images of the damage done during an accident, and predicting the repair costs for this. This helps to speed up the process of insurance payouts significantly, improving efficiency throughout the insurance space overall. Initially going through Entrepreneur First, Tractable was founded in 2014 by Alexandre Dalyac, Adrien Cohen and Razvan Ranca.

In the U.S., 15,498 of the more than 88,000 dams in the country are categorized as having high hazard potential--meaning that if they fail, they could kill people. As of 2015, some 2,000 of these high hazard dams are in need of repair. With a hefty price tag estimated at around $20 billion, those repairs aren't going to happen overnight. A project out of the Columbia Water Center aims to help guide the process of repairing or decommissioning these dams. The team is pinpointing the riskiest dams, using climate models, GIS data, and artificial intelligence to predict the likelihood that rainfall will overtop a dam and cause significant downstream damages to population and critical infrastructure.

In the U.S., 15,498 of the more than 88,000 dams in the country are categorized as having high hazard potential--meaning that if they fail, they could kill people. As of 2015, some 2,000 of these high hazard dams are in need of repair. With a hefty price tag estimated at around $20 billion, those repairs aren't going to happen overnight. A project out of the Columbia Water Center aims to help guide the process of repairing or decommissioning these dams. The team is pinpointing the riskiest dams, using climate models, GIS data, and artificial intelligence to predict the likelihood that rainfall will overtop a dam and cause significant downstream damages to population and critical infrastructure.

Start-up-developed technology is offering'touchless' claims handling to the motor insurance industry through the use of AI-powered photo recognition. Tractable, which already provides the first-wave of its technology to UK insurer Ageas, has today announced the roll out of a new development to its AI system, which will see it able to provide a full estimate repair cost using photos in minutes. The company, which was founded in 2015 and has attracted more than $10 million (£7.5 million) in funding, says that its new'AI Estimating' technology will save time, cut costs, and transform the claims experience on more than 60% of motor claims. The system will streamline the claims process, from First Notification of Loss (FNOL) to an insurer-approved estimate, without the need for human intervention. "Today when you have an accident and you call your insurance company, the process to manage your claim is extremely slow, its expensive and its very manual," Adrien Cohen, chief commercial officer at Tractable, told Insurance Business.

The smartphone is Apple's most advanced one till date with features such as an edge-to-edge display and a FaceID phone unlocking system. But iPhone X is also the company's most expensive iPhone till date -- available at a price of $999 -- more expensive than other hotly anticipated flagship devices of other smartphone manufactuers this year, including Samsung Galaxy S8, Samsung Galaxy Note 8, Google Pixel 2 and LG V30. The reason for this is that Gorilla Glass displays, even though they are stronger than other smartphone displays, can still break if the phone falls at a certain angle. The only thing is this risk is double with iPhone X bcause there is glass at the front and the back, and the repair would be, needless to say, expensive. In case, your iPhone X breaks, get ready to shell out $279 if your phone is in warranty.

Recent work has raised the challenge of efficient automated troubleshooting in domains where repairing a set of components in a single repair action is cheaper than repairing each of them separately. This corresponds to cases where there is a non-negligible overhead to initiating a repair action and to testing the system after a repair action. In this work we propose several algorithms for choosing which batch of components to repair, so as to minimize the overall repair costs. Experimentally, we show the benefit of these algorithms over repairing components one at a time.