Technology will continue to displace humans in job roles that can be automated. For certain, business and government leaders will have a choice -- accept progress and prosper, or wait for the inevitable outcome of regression. Historically, while most of the Luddites eventually concede, it's a complicated scenario.
By 2020, smart machines will be a top five investment priority for more than 30 percent of CIOs, according to the latest market study by Gartner. That being said, with some smart machines moving towards fully autonomous operation for the first time, balancing the need to exercise control versus the drive to realize benefits is crucial.
As an example, Google’s self-driving car project team may discover that pursuing full autonomy is 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," said Brian Prentice, research vice president at Gartner.
Smart Machine Market Development
Gartner analysts believe that putting a steering wheel in an autonomous vehicle means a fully licensed driver must always be in the car and prepared to take control if and when necessary. Not only does this destroy many of the stated benefits of autonomous vehicles, but it changes the role of the driver from actively controlling the car to passively monitoring it for potential failure.
According to the Gartner assessment, Google's dilemma is representative of a challenge all smart machine initiatives must face and address.
"Smart machines respond to their environment. 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," said Mr. Prentice. "The trick then is to figure out what is actually controllable and limit smart machines to that which can be accurately modeled and managed."
Gartner says that major unresolved problems in machine learning solutions, such as how to ensure learning data is fully representative and how to avoid "reward hacking," need to be addressed before any autonomous machine that continues to learn from its environment can be deployed as a mass-market solution to a real-world problem.
"The vision of the fully autonomous vehicle will not become reality, for any car manufacturer, in a time frame that doesn't fall into the realm of science fiction," said Mr. Prentice. "The failure of this vision will be set against the backdrop of advances in smaller, more pragmatic applications of machine learning in automobiles that will improve safety and driver experience."
According to Gartner, savvy leaders must:
In summary, while smart machines may be designed and built by smart people, automation exists in a world where cognitive computing technology may greatly exceed the capabilities of the average human. Therefore, autonomous vehicles are merely one of many examples of where the human-machine interface will be the key challenge for technology vendors to overcome.
By 2020, smart machines will be a top five investment priority for more than 30 percent of CIOs, according to the latest market study by Gartner. That being said, with some smart machines moving towards fully autonomous operation for the first time, balancing the need to exercise control versus the drive to realize benefits is crucial.
As an example, Google’s self-driving car project team may discover that pursuing full autonomy is 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," said Brian Prentice, research vice president at Gartner.
Smart Machine Market Development
Gartner analysts believe that putting a steering wheel in an autonomous vehicle means a fully licensed driver must always be in the car and prepared to take control if and when necessary. Not only does this destroy many of the stated benefits of autonomous vehicles, but it changes the role of the driver from actively controlling the car to passively monitoring it for potential failure.
According to the Gartner assessment, Google's dilemma is representative of a challenge all smart machine initiatives must face and address.
"Smart machines respond to their environment. 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," said Mr. Prentice. "The trick then is to figure out what is actually controllable and limit smart machines to that which can be accurately modeled and managed."
Gartner says that major unresolved problems in machine learning solutions, such as how to ensure learning data is fully representative and how to avoid "reward hacking," need to be addressed before any autonomous machine that continues to learn from its environment can be deployed as a mass-market solution to a real-world problem.
"The vision of the fully autonomous vehicle will not become reality, for any car manufacturer, in a time frame that doesn't fall into the realm of science fiction," said Mr. Prentice. "The failure of this vision will be set against the backdrop of advances in smaller, more pragmatic applications of machine learning in automobiles that will improve safety and driver experience."
According to Gartner, savvy leaders 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 aiming to make use of smart machine technologies, identify and analyze the constraints within the environment — in law and in public attitudes — that the eventual solution will face.
- Design any smart machine solution outward from constraints identified in the key areas of user experience, information asymmetry and the business model to hit the sweet spot for smart machine-enabled solutions, and maximize the benefit the technology will provide.
In summary, while smart machines may be designed and built by smart people, automation exists in a world where cognitive computing technology may greatly exceed the capabilities of the average human. Therefore, autonomous vehicles are merely one of many examples of where the human-machine interface will be the key challenge for technology vendors to overcome.