Just over a century since The Manifesto of Futurist Architecture declared the city must be rethought and rebuilt like an "immense and tumultuous shipyard" – "everywhere dynamic", and the house like a "gigantic machine", it may be that author Antonio Sant'Elia had things the wrong way around. Because although his machine-fetishising sketches inspired our common vision of a science-fiction future – as in Fritz Lang's 1927 film Metropolis, with its technological Tower of Babel an imposing centrepiece – it might be the gigantic machines that are making our houses. Architecture and AI visionaries – forming especially around MIT in the 1950s, through to the later work of MIT Media Lab co-founder Nicholas Negroponte – and design pioneers have long thought about automating the creation of our environments. Now the technology is catching up to their ideas, and a radical shift into AI-assisted design is taking hold, with implications that could radically transform the form, feel and function of the places we inhabit. Completely automated design is not quite there yet. This crop of generative, AI-assisted tools is rather new.

We develop reinforcement learning techniques to enable interaction across multiple agents including AIs and humans, with potential applications from AI-assisted design to autonomous driving. Methodological contexts of the research include deep reinforcement learning, inverse reinforcement learning, hierarchical reinforcement learning as well as multi-agent and multi-objective reinforcement learning. FCAI is working on a new paradigm of AI-assisted design that aims to cooperate with designers by supporting and leveraging the creativity and problem-solving of designers. The challenge for such AI is how to infer designers' goals and then help them without being needlessly disruptive. We use generative user models to reason about designers' goals, reasoning, and capabilities. In this call, FCAI is looking for a postdoctoral scholar or research fellow to join our effort to develop AI-assisted design. Suitable backgrounds include deep reinforcement learning, Bayesian inference, cooperative AI, computational cognitive modelling, and user modelling. Computational rationality is an emerging integrative theory of intelligence in humans and machines (1) with applications in human-computer interaction, cooperative AI, and robotics. The theory assumes that observable human behavior is generated by cognitive mechanisms that are adapted to the structure of not only the environment but also the mind and brain itself (2).

As part of its Hype Cycle for emerging technologies in 2020, Gartner has identified social distancing technologies, composable enterprise, AI-assisted design, differential privacy and biodegradable sensors as the five key emerging trends that will drive technology innovation over the next decade. "Emerging technologies are disruptive by nature, but the competitive advantage they provide is not yet well known or proven in the market. Most will take more than five years, and some more than 10 years, to reach the Plateau of Productivity. But some technologies on the Hype Cycle will mature in the near term and technology innovation leaders must understand the opportunities for these technologies, particularly those with transformational or high impact," said Brian Burke, research vice president at Gartner. For example, Gartner points to health passports and social distancing technologies, both related to the COVID-19 pandemic, that are taking the fast track through the Hype Cycle and having a high impact.

Social distancing technologies, composable enterprise, AI-assisted design, differential privacy and biodegradable sensors are among key technologies that will drive technology innovation for the next decade, said leading research and advisory company Gartner in a new report. The 30 must-watch technologies on the Gartner report "Hype Cycle for Emerging Technologies, 2020" include technologies that enable a composable enterprise, aspire to regain society's trust in technology, and alter the state of your brain. "Emerging technologies are disruptive by nature, but the competitive advantage they provide is not yet well known or proven in the market. Most will take more than five years, and some more than 10 years, to reach the Plateau of Productivity. But some technologies on the Hype Cycle will mature in the near term and technology innovation leaders must understand the opportunities for these technologies, particularly those with transformational or high impact," said Brian Burke, research vice president at Gartner.