The age of intelligent tools and computation-intensive automation of manufacturing and services is upon us. The rapid development and uptake of intelligent, self-learning systems has begun to automate tasks associated with blue-collar jobs as well as less routine tasks related to knowledge work.
With these technological advancements, many predict a dystopian outcome where jobs are lost and workers are displaced. We strongly believe, however, that the future is ours to create. We can use intelligent tools to generate new employment, augment skills, and create a more just and equal society.
Our view is rooted in economic and technological history, and is distinguished by a sober estimation of both the possibilities and limitations of intelligent tools. It is informed by an understanding that we can directly influence and shape technology, how technology is deployed, and how the benefits of technology are shared.
We are an interdisciplinary and international group of researchers from the fields of engineering and data science, economics, sociology, and political science. Through our connections with international institutions and stakeholders, we support ongoing dialogue and guide research, policy, and practice.
We are exploring how we can go about shaping the world we want in the age of intelligent tools. The challenge, deep and real, is to architect that world, to find ways of working, earning, and learning that support the healthy development of our societies and economies, and the humans who inhabit them.
1. The State of the Debate
2. Shaping the Future
1. The state of the debate
The necessary first task is to understand what the real challenge is. Some recent studies, and much of the resulting press coverage, suggest an impending dystopia. Other studies that analyze the current status and likely future impact of intelligent systems and advanced robotics arrive at very different conclusions. These varying conclusions and implications are so diverse that we must examine carefully the assumptions each study makes, including assumptions about technology, data sources, and analysis.
On one side, we find a set of narratives that express fear. These studies range from the reasonable concern that intelligent systems will restructure the employment market to the sensationalized view of “singularity”. These fear-based stories suggest that some interior logic of technology will sweep in front of us and define our world. Unfortunately, such a narrative can be self-fulfilling.
On the other side, we find narratives that are overly optimistic. These studies suggest there is no problem at all, and try to convince us that the ordinary process of job creation in a rebounding economy will handle the matter of the impact of intelligent systems.
Our working group believes that we can directly influence and shape our own future, including the development and deployment of new technologies. That view sets the difficult task of defining what that future should be and who should be involved in defining it. Competing visions of the future, and the strategies and policies to pursue these visions, will undoubtedly shape the very trajectory of intelligent technologies.
2. shaping the future
The next task is to explore what choices we may have to influence the directions and consequences of intelligent technologies. The issues and debates around these choices are becoming clear.
Intelligent machines as tools:
How can we most effectively engage people in a working world with extensive use of intelligent tools? How can intelligent systems and machines be used to complement human labor, and to substitute for and amplify/augment human intelligence even as work is reorganized and re-conceived? How do we put ourselves on a trajectory to new approaches to accelerated productivity and innovation, to developing and sustaining human capabilities (including skills, training, and education), and to creating an all-inclusive prosperity?
Policies for technology, work, and labor:
What must policies, policy processes, technology development, and work organization practices look like if we are to shape a future in which intelligent machines do not simply replace human labor, but instead are used to augment human intelligence? Critical amongst these questions are how we develop educational and training systems in the context of lifelong learning. In earlier phases of industrial development, extending the number of years of education was sufficient. Reorganizing and repurposing existing educational systems will be much more difficult. We must now reconsider what we do with our years of education: what needs to be learned, and how?
Rethinking business strategies:
How do we develop human-centric business strategies that win in the market by effective and agile use of workforces as indispensable resources, not just as costs to be cut or rationalized? How can our business approaches promote skill development and the effective and innovative use of the new intelligent tools?
The policy objective for governments throughout this technology transformation and economic restructuring will remain classic and enduring: sustain the growth of employment and productivity to assure expanding real incomes of our citizens. Policy will also influence how the economic surplus or bounty resulting from productivity gains related to intelligent systems is shared among workers and citizens, in the form of income as well as required public services. Importantly, the choices governments make, as well as the national and economic context in which firms and governments develop and deploy the intelligent tools, will likely shape the very trajectories of computation-intensive automation in manufacturing and services. (Policy and business strategies in, for example, Northern Europe, China, Japan, and the United States differ significantly — consider the social media context in America, with Google, Apple, Facebook, and Amazon emerging to global status, while in China these technologies emerge as horizontal groupings with Alibaba and Tencent.) The platforms on which intelligent tools are deployed, the character of data networks, and the rules for data will be critical as well. Indeed, a future that supports people, rather than displaces them, will likely be influenced by the inter-tangled competition and cooperation of the multiple national strategies.
Timing is of the essence if we are to steer the shape and influence of intelligent machines and intelligent systems. Technology development timeframes can be underestimated, as well as overestimated, at different points in the maturation cycle. Our initial investments of attention and resources are often driven by irrational exuberance, creating bubbles. Yet we are also prone to underestimating the uptake and impact of new technologies once initial foundations and conditions are set, and we miss the hockey-stick effect. It is essential for us to consider timing, since societal institutions and their policies generally evolve more slowly than technology and the entrepreneurial scale-out of disruptive systems.
These are the core questions underlying our conversations and our work. If you wish to learn more, or to take part, please visit our sites or contact us.