A machine and a human, both hidden from view, would compete to convince a human judge of their humanness using only conversation.

How can we determine in practice whether a machine can understand? In 1950, the computing pioneer Alan Turing tried to answer this question with his famous “imitation game,” now called the Turing test. In one study, IBM’s Watson was found to propose “multiple examples of unsafe and incorrect treatment recommendations.” Another study showed that Google’s machine translation system made significant errors when used to translate medical instructions for non-English-speaking patients. Such discussions used to be the purview of philosophers, but in the past decade AI has burst out of its academic bubble into the real world, and its lack of understanding of that world can have real and sometimes devastating consequences. When based on large neural networks, like OpenAI’s GPT-3, such models can generate uncannily humanlike prose (and poetry!) and seemingly perform sophisticated linguistic reasoning.īut has GPT-3 - trained on text from thousands of websites, books and encyclopedias - transcended Watson’s veneer? Does it really understand the language it generates and ostensibly reasons about? This is a topic of stark disagreement in the AI research community. The result is what researchers call a language model. More recently, a new paradigm has been established: Instead of building in explicit knowledge, we let machines learn to understand language on their own, simply by ingesting vast amounts of written text and learning to predict words. This approach, as Watson showed, was futile - it’s impossible to write down all the unwritten facts, rules and assumptions required for understanding text. At first, researchers tried to manually program everything a machine would need to make sense of news stories, fiction or anything else humans might write. Natural language understanding has long been a major goal of AI research. The overall aim of the paper is to understand how smart cities differ in their meanings, intentions and ‘offerings’.Remember IBM’s Watson, the AI Jeopardy! champion? A 2010 promotion proclaimed, “Watson understands natural language with all its ambiguity and complexity.” However, as we saw when Watson subsequently failed spectacularly in its quest to “ revolutionize medicine with artificial intelligence,” a veneer of linguistic facility is not the same as actually comprehending human language. The paper clusters the various views pertaining to the building of smart cities and, thereby, proposes a 3RC framework, consisting of Restrictive, Reflective, Rationalistic and Critical schools, which critically analyses various stages in the development of the field. Such uncertainty at the conceptual level, and the heightened importance given by policy makers and markets to its adoption, highlights the need for proper scrutiny.

The current paper reviews the research in the field to date to find that there are conflicting views in smart city planning, which limit our knowledge about the ‘real’ smart city and its implications for building creative and inclusive urban space. A variety of expectations from various stakeholders make the potential implementation strategies even more complex. The roles of various players, especially the central role of the private sector, is often challenged in the academic literature. Despite efforts to promote its practice, there has been wide criticism about the concept and the way it has been adopted and implemented. While proponents argue that smart cities will bring positive social change by the adoption of ICTs, enriched governance and human capital among the citizenry, opponents point out about the negative effects and gaps exist in their planning and execution. Smart city approaches in city planning have gained significant momentum in the recent past.

I understand