Augmented Intelligence shows cause-and effect-relationships, enabling decision makers to explain their rationale
Recently, the city of Toronto selected Alphabet’s Sidewalk Labs to bring its computing power and big data experience to bear in an effort to build a better urban center. It’s no surprise that the same company that offers market-leading solutions to everything from email to mapping and advertising to self-driving cars might be called upon to help make a city smarter.
As one of the firms that has led the digital transformation of our business and personal lives, Alphabet is now leading the way when it comes to transforming the ways our cities work, too.
Alphabet’s Toronto project is one of the largest of this type and is notable for the control being ceded to the company by the city. Alphabet’s big data and AI approaches will help solve some of the problems that traditional cities have encountered in their planned city project; however, even at this early stage, it is clear that the Google-parent company will undoubtedly run into problems.
Like every city, Toronto is a complex web of systems and sub-systems where a change to one part of the city will have impact on other parts. With constraints like budgets and staff always close to mind, investments in mass transit systems can mean investments in social or cultural capital is reduced. Spending more on repairing and maintaining city infrastructure can free up budgets for spending elsewhere today, but infrastructure replacement costs can only be pushed so far down the road.
Political systems are interweaved with financial systems, infrastructure systems, social systems, and more. The interactions between all of these systems, the cascading effects and impacts of decisions made in one system on the other systems, and the inability of an one human – or even group of humans – to understand how all of these systems interact and impact each other makes the modern city demonstrably complex in nature.
The complexity of the Toronto project (indeed, the underlying complexity of any city) will see Alphabet turn to its expertise in big data and artificial intelligence (AI). Big data and data science approaches have helped make Alphabet the company it is today. Google, the company’s cash cow, has employed both to offer the world’s best search engine and a scarily accurate targeted advertising programme.
By building incredibly detailed profiles of its users, Google has been able to deliver relevant advertisements from companies seeking to reach increasingly niche markets, and spin an incredible profit doing so. The AI engines that help drive this data science approach to understanding and serving human users are among the best in the world and have helped position the company as a leader in the field.
Yet all the data in the world, coupled with cutting edge AI, won’t help Alphabet overcome the challenges it will face in delivering a smarter city. These approaches can only help Alphabet respond to challenges it predicts will emerge and plan for events that its AI systems can comprehend.
Where big data fails (predicting events that have never happened before) and where AI reaches its limits (problems that the human brain, that AI tries to mimic, cannot comprehend), Alphabet will need something else, something that is capable of predicting events that have never happened before, and optimising choices about systems that human intelligence cannot comprehend.
In short, Alphabet and Toronto won’t need artificial intelligence – they’ll need Augmented Intelligence.
Augmented Intelligence is a complementary technology that can help negotiate, evaluate, predict, and optimise the Toronto city system project and ensure it succeeds. Augmented Intelligence utilises expert knowledge, not necessarily raw data, to explore a complex system like a city and reveal the emergent phenomena and cascading effects of all of those interacting systems and decisions.
At the same time, Augmented Intelligence doesn’t predict the future. Instead, it reveals possible futures based on multiple factors and possible decisions – using more sophisticated analytics than has ever before been possible. Still, someone must decide how to use the analysis and choose the best path to follow. Whether that someone ends up being Alphabet or the civic leaders of Toronto, a particular benefit of Augmented Intelligence is that, in showing the cause-and effect-relationships, it enables decision makers to explain their rationale – something that Google’s black-box AI typically cannot do.
Augmented Intelligence is already being deployed to help solve city planning problems. Take parking, for example. Augmented Intelligence is helping planners consider the impact of autonomous vehicles (AVs) on urban parking requirements. Future parking requirements in cities will diminish substantially with the introduction of AVs and the parking problems of the present will be a thing of the past in an AV world.
However, adopting a holistic, systems-of-systems approach to the introduction of AVs means not only assessing the obvious impacts on parking and traffic congestion, but also other systems impacted by the adoption of AVs such as healthcare (reduced auto accidents), environmental (more efficient use of cars leading to lower emissions), energy (electric vehicles demanding different feeling/charging), and economic systems (impacts on mechanics, gas stations, parking garage owners, taxis and metered vehicles etc). All of these changes are coming at the same time, and only Augmented Intelligence and complex systems modeling will allow planners to understand the impacts of their choices, and identify the optimal planning strategies to implement.
The company that brought the world the best search engine, the best email, and the most effective online advertising is also the same company that delivered epic failures like Google Wave, Google Answers, and Orkut.
Whether Alphabet will succeed in delivering Toronto a truly smart city or another entry in its corporate collection of imperfect productions will depend, in large part, on how it tackles the complexity of the city. Artificial intelligence and big data might have helped make Alphabet one of North America’s most successful companies, but they’ll need Augmented Intelligence to deliver Toronto one of North America’s smartest cities.
Michel Morvan is Co-founder, Executive Chairman and CEO of Cosmo Tech USA. Before co-founding Cosmo Tech Michel was Chief Scientist and Vice President for Strategic Intelligence and Innovation at Veolia Environment. He is a former Full Professor of Computer Science at École Normale Supérieure in Lyon, former Chair of Complex Systems Modelling and Senior Scientist at the École des Hautes Etudes en Sciences Sociales in Paris, and former External Professor at the Santa Fe Institute in New Mexico. He is an Eisenhower Fellow.