“I propose to consider the question, ‘Can machines think?'” Because “thinking” is difficult to define […] “Are there imaginable digital computers which would do well in the imitation game?”
—Alan Turing, Computing Machinery and Intelligence.
“Our bodies are hardware, our behaviour software.”
In 2010 the robot Suzette, by Bruce Wilcox, succeeded for the first time in making a human think it was another human. And more recently  Eugene Gootsman became the first chatbot passing the Turing test in a contest where a third of the event’s judges thought that Goostman was human. In the seminal article Computing machinery and intelligence published in 1950 Alan Turing posed the question about the ability of a machine to think. Since then Artificial Intelligence [AI] have evolved and now chatbots seems to be prepared to pass the Turing test as we are used to interact with them in a chat of a big chain store or when receiving phone calls trying to sell us another insurance service. Chatbots are machines designed to emulate the conversational abilities of humans and generally attempting to convince the user into thinking that the machine is human. The people of Cornell Creative Machines Lab moved a step forward and posed two chatbots to talk to each other to see if they could realize the other one was not human. Nowadays it seems that AI have evolved and robots are able to recognize themselves [self-consciousness] and even to deceive to each other… both of them features apparently reserved to human beings.
The imitation game [or Turing test] was made popular by a Blade Runner scene, and nowadays more than 60 years later, in times of Big Data, artificial intelligence, Google, algorithms, and smart cities the question has been turned the other way around, “Is thinking a kind of computation?” If so, where we can find the difference when we talk about architecture, design, and the city?
The connection between cybernetics, computation and architecture has been explored since Alan Turing’s test until now by several philosophers, scientists, or media technologist. In 1961 Hilary Putnam stated that the relationship between mind and brain is similar [if not identical] to the relationship between a running program and a computer. In 1985, the Media Laboratory at MIT opened its doors founded by Nicholas Negroponte with the motto “In a world where radical technology advances are taken for granted, Media Lab researchers design technologies for people to create a better future,” It grew out of the work of MIT’s Architecture Machine Group and remains within MIT’s School of Architecture and Planning. Daniel Innerarity proposed [in 2006] that “rather than neighbourhoods, local networks are developed and public debate is conducted in a virtual space, where streets and squares are no longer the primary venue and staging.” More recently, Molly Wright Steenson wrote that “a world constructed of information and feedback flows, does not stop with machines and people: it seeps into architecture and the design of cities.” Advances in technology, in the decade of the sixties were based in the field of cybernetics and now have become the ever changing world of new information and communication technology [ICT]. From philosophy to cybernetics and game theory, the phenomenon of the network society and digital culture allow a new understanding of the concept of “city”. The inherent relationship between art and science is again part of architectural discourse to understand cities as a sort of organism which self-regulate and adapt to survive. On these ideas, Joseph Rykwert wrote that the city form an autonomous internal growth that is neither based, nor dictated by inevitable economic laws. Rather, he claims, is indeed a curious, imperfectly controlled artifact, based on the combination of random events.
Accordingly, a city is a dynamic system that produces many uncontrollable phenomena. Its conditions are not calibrated against absolute reference values: results are relative and always relational. In this contexts, as Maurice Richter recalls, it is important to understand that human organisations may be used as ‘tools’ just as material objects and technology are. As the number of urban data projects grows, along with the ways in which this information is generated, collected, stored and shared, we tend to feel that greater amounts of data will refine our models and eventually lead to an accurate, controlled representation of the city. Although this is a seductive idea, we need to realise that to have “more information” doesn’t equal to be “more informed” and that the complexity of the system means that all representations will necessarily be partial or biased. Just as the improvements and cybernetics and AI drive us to imagine a Technological Singularity point when machines will exceed human intellectual capacity and control, the same happens with the emergence of Big Data to manage the enormous amount of information that we are now creating and sharing. Nevertheless, having more data without questioning the methods by which we use it will simply magnify the faults in our analysis, while lessening some corporate and academic anxieties.
Then the question arises: How to use technology to manage complexity we’re facing nowadays? Complexity is something inherent to humankind and thus, to our environments; the difference is that in current times we are more aware of it. And instead of being dazzled by the power of technology to manage information and predict our next internet purchase, we should be aware of one of the basic principles in complexity, which states that a small change at one place in a deterministic nonlinear system can result in large differences in a later state. Perhaps the moment to introduce such changes has arrived after the techno-optimism of the past years, and the rise of new technological body should be accompanied by the destruction [or acceleration until evaporation] of old ideas, dogmas and outdated paradigms. A new understanding of our complex systems should be more socially robust and able to evolve, incorporating human conscious dimensions and relations; and the understanding that cities and other manifestations of human relations are not just large collections of people but agglomerations of social links, just like pointed out by Nicholas Negroponte:
“To make inferences about a statement requires a knowledge of the world. To make an inference about the intention of a statement requires some knowledge of the person making it.”