‘Sketches of Thought’ is a human-machine collaborative design system based on Artificial Intelligence (AI). The first aim of ‘Sketches of Thought’ is illustrating an approach to AI-integration within the designer’s creative workflow. The system translates a hand-drawn architecture sketch into a photorealistic image that suggests a possible evolution of the design idea. The dialogue with the system happens through a visual interface whereby the designer communicates by sketching directly on a drawing monitor, while the system responds by showing the results of the image translation process on a second monitor. Interaction with the system does not end after a first iteration. Instead, the designer is encouraged to adjust the initial sketch – or even make new sketches – for several times to explore, with the aid of the machine’s feedback, different elaborations of an idea. This system does not require particular drawing skills, and therefore anyone can experience a proficient ‘exchange of ideas’ with the AI model. The second aim of ‘Sketches of Thought’ is helping the designer familiarise with AI technology. This is achieved by unveiling the black box of the AI model functioning, that is, through a representation of its internal processes. Moreover, as the AI model simulates some aspects of human cognition, a look inside the black box of AI also means visualising a simplified version of the human mental processes. Therefore, learning about AI is an opportunity for humans to learn more about themselves. The relevance of this virtual prototype is twofold: first, it promotes the view of AI as a means to augment rather than replace the human cognitive capabilities. Second, it challenges the current beliefs and prejudices on AI-technology by making the AI internal processes explicit through a visual representation.

In book: La bellezza per il rospo (Beauty according to the toad) (pp.221-222), Publisher: CLEAN Edizioni, Editors: Roberta Amirante, Carmine Piscopo, Paola Scala.

Born in the world of mathematics to define all those “equations that express a set of quantities as explicit functions of a number of independent variables, known as ‘parameters’." Its architectural transposition, which was introduced by Luigi Moretti during the Forties, indicates a consistent system of elements and relationships among them, that is founded on the construction of topological rather than metrical spaces. In other words, it identifies the only invariant of each and every design process with the potential of standardization in digital design and allows software developers to release specific programs which are able to support conceptual design in architecture.

In book: "AAD Algorithms-Aided Design. Parametric strategies using grasshopper", (pp.352-359). Publisher: Le Penseur. Editor: Arturo Tedeschi.

In architecture and structural engineering, ‘form-finding’ identifies the process of designing optimal structural shapes by using experimental tools and strategies, i.e. physical models, to simulate a specific mechanical behaviour.

The reverse hanging method is the oldest and probably most diffused form-finding technique for arches, vaults and shells – a physical model, made with elastic cables or membranes with no rotational stiffness, is first subject to gravitational forces to obtain a structural state of pure tension; such a form, which is called “funicular”, is then inverted to identify the mechanical compression-only situation.

This principle was first mentioned in a publication by Robert Hooke in 1675 . With a Latin anagram, only solved in 1701, he proposed to reverse the curve which was generated by a hanging chain, under self-weight and supported only at its ends, in order to define the optimal structural form of an arch. Such a curve is called “catenary” when the chain presents a constant distribution of weight . Initially confused with a parabola by Galileo Galilei, the catenary was then mathematically described by David Gregory in 1697.

In book: "Shell Structures for Architecture - Form finding and optimization", (pp.290-294). Chapter: Appendix C. Publisher: Routledge. Editors: Sigrid Adriaenssens, Philippe Block, Diederik Veenendaal, Chris Williams.

Genetic algorithms are a subclass of evolutionary algorithms. A genetic algorithm (GA) is defined by Goldberg as a “search algorithm based on the mechanics of natural selection and natural genetics”, but another definition, more focused on its way of functioning, is provided by John R. Koza in his 1992 book, “Genetic programming: On the Programming of Computers by Means of Natural Selection”: “The GA is a highly parallel mathematical algorithm that transforms a set (population) of individual mathematical objects [...], each with an associated fitness value, into a new population (i.e. the next generation) using operations patterned after the Darwinian principle of reproduction and survival of the fittest and after naturally occurring genetic operations (notably sexual recombination)”. In other words, in a random population of potential solutions, the best individuals are favoured and combined in order to create better individuals at the nextgeneration.

In book: "Shell Structures for Architecture - Form finding and optimization", (pp.224-236). Chapter: 18. Publisher: Routledge. Editors: Sigrid Adriaenssens, Philippe Block, Diederik Veenendaal, Chris Williams.

Since the introduction of digital technologies, computational design and optimization has been gradually replacing practice based on physical models. Even if born as a set of mere solution strategies, architectural and structural design optimization seems also to be effective in supporting conceptual design. This aspect is highlighted through two design exercises of computational morphogenesis using genetic algorithm (GA). Two key aspects emerge from such processes: the possibility of freely formulating objective functions apart from structural performances, and the opportunity for designers to actively interact throughout the computational process.