Level up or game over – the role of artificial intelligence in society

5 min read

Garri Kasparov, the chess grandmaster, said: “Doomsday predictions have always been a popular past-time when it comes to technology». We will need to make a choice; how do you react when you get a new robot colleague? Will you fear it, or will you use it to excel to new levels.

In the tech sector, we’re constantly communicating the latest visions from the most advanced research, but at the same time we try to get others to catch up with the visions in real life. The past few years we have heard about, and brought to life, hot tech topics such as IoT, cloud computing, big data, and artificial intelligence. This experience has brought us into a situation where machines are – increasingly often – better than us humans.

We now can connect massive amounts of data to the internet, e.g. using new technologies like Disruptive Technologies’ sensors with an impressive life-span of 15 years and a range of 1km. Cloud computing gives us the ability to utilize storage and processing power at a large scale.  Big data allows us to capture and make sense of the data. However, it is first when we apply machine learning to this that technology truly gets disruptive for human’s role in the workforce.

The world’s most valuable companies measured in market capitalization are tech businesses; Apple, Alphabet, Microsoft, Amazon.com and Facebook. Several of these are also amongst the world’s heaviest investors in R&D activities, and tech-companies like Samsung, Amazon, Alphabet and Intel each invest more than whole nations do in R&D. Samsung invests almost twice as much as Norway does in R&D, counting both public and private investments. This race to invent the most important technology of tomorrow is increasing the general pace of development and driving rapid shifts in the business communities. Mobile first, cloud first, AI first are approaches that have been from time to time announced. It goes without saying that these approaches all require putting internet first. As software’s understanding of language and new language interfaces develop, and the amount of data online (both historic and real-time) grows, businesses can now benefit from practically all acquired knowledge of mankind.

The machines can see, hear, read and write. They can comprehend the data, process it, take action based on it and learn from its actions. For many of us, apart from social interaction and drinking coffee, this is the essence of what we do in our everyday life at work.

Our technology-driven self-disruption is well underway, and has been for millenniums. As the invention of the wheel most likely resulted in fewer people needed to carry loads, the car left coachmen with horse carriages less needed. Today we see lawyers being replaced by AI and financial advisors are challenged by chatbots. In 2016, Deloitte predicted a potential of 114.000 legal jobs to be automated in the next 20 years in UK alone, and at the World Economic Forum in Davos an analysis was published stating a net loss of 5.1 million jobs due to robotics and artificial intelligence. The challenge is real, but the long-term consequences are so far human predictions based on primarily human intelligence. History has demonstrated that we are not able to predict where new jobs will appear when the ones we are used to disappear.

Machines are out-performing humans in tasks that are structured, based on rules and/or requires the processing of large amounts of data or calculations. A calculator is, without any doubt, both faster and more accurate than myself in calculating. When children use calculators in class, instead of pencil and paper, it allows them to focus on the input (translate the problem to math), the reasoning and the utilization of the output rather than the calculation itself.

Elon Musk stated on stage in 2014 when addressing challenges to meet demands: «It is way harder to make the machine that makes the machine than it is to make the machine in the first place». Industry workers do not need to assemble cars, since robots can do this, but the statement illustrates something else about the shift we now are experiencing as result of new advances in technology. We do not necessarily need many industrial employees to assemble a Tesla. We do however need an increasingly high number of highly educated tech heads to create, develop and maintain the machines.

Using recent advances in machine learning, like combining cooperative reinforcement learning and alternative reasoning methods, we are in many ways transferring human experience and problem solving abilities to machines and allowing the machines to build upon it using capabilities far beyond those of a human being. The abilities machines now possess for acquiring vast amounts of data and understanding patterns that humans otherwise cannot see, will require a new look at how we interact with machines.

When onboarding a new employee in a workplace today we can divide the existing employees into two categories: the ones who see the new hire as competition and feel threatened, and the ones that sees the opportunity for personal development and to together grow the business. As the machines we interact with improve and gets smarter, we will need to make a choice. Should we see it as the beginning of the end or as the day we can get to the next level.

About Kjetil Thorvik Brun

Kjetil Thorvik Brun is Head of Digital Industries at Abelia, the Norwegian business association of knowledge and technology based enterprises. He represents the policy interests of tech-industry in Norway. Amongst other experiences, Kjetil has been an advisor for a long line of tech companies as a PR-professional. He has e.g. been manager for Burson-Marsteller Norway's tech and digital communications practices. He has also managed corporate and government affairs in Microsoft internationally.