The 'Google Steering Wheel Dilemma’ or why full autonomy may not be possible or desirable in smart machines

Brian Prentice of Gartner shares tips to handle the ‘control versus benefit’ challenge all smart machine initiatives must face.

Comments

Brian Prentice at the 2016 Gartner Symposium/ITxpo in the Gold Coast.

The trick is to figure out what is actually controllable and limit smart machines to that which can be accurately modeled and managed

Brian Prentice, Gartner

By 2020, smart machines will be a top five investment priority for more than 30 per cent of CIOs, according to Gartner.

With smart machines moving towards fully autonomous operation for the first time, balancing the need to exercise control versus the drive to realise benefits is crucial, says Brian Prentice, research vice president at Gartner.

Speaking at the Gartner Symposium/ITxpo in the Gold Coast, Prentice says Google’s self-driving car project is a perfect example of why pursuing full autonomy may be neither possible nor desirable in smart machines.

“Human beings are still required as the final point of redundancy in an autonomous vehicle, so a fully autonomous car requires a steering wheel should a driver be required to take control,” says Prentice.

“But putting a steering wheel in an autonomous car means a fully licensed, sober driver must always be in the car and prepared to take control if necessary.

“Not only does this destroy many of the stated benefits of autonomous vehicle, but it changes the role of the driver from actively controlling the car to passively monitoring it for potential failure.”

Gartner says the ‘Google Steering Wheel Dilemma’ represents a challenge all smart machine initiatives must face.

“Google's Car project shows full autonomy is not yet possible, so CIOs need radical ideas to maximise benefits,” Prentice points out in a Gartner Maverick Research paper he co-authored with David Groombridge.

As the two analysts note, the rise of smart machines raises more worrying scenarios.

One is the potential for these machines to become autonomous agents that should be monitored like spies. There is also the threat of these machines displacing traditional jobs and the failure to replace these with new jobs.

At the same time, smart machines are assumed to sooner or later begin to act autonomously, and this autonomy of action is being actively sought.

“The predictions around smart machines assume that such machines will constantly learn and respond to complex decisions in a humanlike way, and so replace humans in many areas.

Google's Car project shows full autonomy is not yet possible, so CIOs need radical ideas to maximise benefits.

For this to happen, CIOs have to address the ‘Google Steering Wheel Dilemma'. "Failure to plan for this will fundamentally constrain the benets that smart machines can deliver,” the two analyst point out.

“Smart machines respond to their environment,” adds Prentice. “But what is the environment that the smart machine is responding to?

“Environments that are largely uncontrollable are not amenable to smart machine projects because it is difficult, if not impossible, to model accurately,” he adds.

“The trick then is to figure out what is actually controllable and limit smart machines to that which can be accurately modeled and managed.”

He says CIOs seeking to maximise the benefits of smart machine solutions must plan to deliver smart machine-enabled services that assist and are overseen by humans to achieve maximum benefit in the next three to five years, rather than those that are fully autonomous.

At the beginning of any project, Prentice likewise advises using smart machine technologies to identify and analyse the constraints within the environment — in law and in public attitudes — that the eventual solution will face.