Architecture artificial intelligence pdf

From the Bauhaus to the Deconstructivists — Style reigned supreme also in the 20th century Fig. These definitions are applied to a series of different disciplines and criteria. For example, in the fabrication industry style is understood as a particular designation, or the title of a machine or machine part.

In literature it pertains to a distinctive manner of expression — just think about realist literature, romantic literature or the flowery style of 18th century poetry. Style can also be applied to human behaviour with all its mannerisms, ticks, physiological and psychological behavioural features. All of which are encapsulated in a distinct style of behaviour — conduct is a main expression of personal style: courteous, discreet, abrasive.

Figure 4: The Card Players, follower in the style of Caravaggio, Harvard Museum The complexity of the term Style consists in the unusual weight and flexibility of the concept itself. In essence the concept defines the main basic rulesets of artistic achievement and excellence. The term Style itself is a latecomer to the considerations of the examination of artistic endeavour and is being discussed in a fierce fashion to this very day.

The etymology of the term in various languages such as the French and English Style, the Italian Stile and the German term Stil, can be traced back to one Latin root: Stilus. The Roman Stilus however is also the ancestor to the writing stylus — the historic tool of the trade of the architecture discipline, wither this be for the penning of timeless architectural rulesets such as Vitruvius books, or the literal scratching of plans on floors Fig.

The German words Grundriss plan and Aufriss elevation still contain parts of the word Riss denoting the origin of scratching plans onto the plaster of medieval building huts and workshops using, you guessed it, pointed sticks. Jahrgang, Heft 2, , P. The term Style in this frame of conversation can be divided into two specific cases. Case one is the idea of Style in Architecture. Case two is the use of the term in Computer Science. In combination, these two instances form the frame of this essay on the emergence of novel considerations of style in the architecture discipline through the application of Neural Networks.

More specifically: through the adoption of Style transfer as a technique. Neural Style Transfer NST as a tool of Architectural Interrogation In order to discuss the nature of style in architecture, I would like to rely on the works of Gottfried Semper as the grand seigneur of the conversation on style. In his opus magnum Style in the Technical and Tectonic Arts, or, Practical Aesthetics, Semper laid out a comprehensive interrogation of style as a driver of architectural innovation, divided into the material driven chapters textiles, ceramics, carpentry and masonry.

Materialism in this frame of conversation is not part of the Materialist philosophy of Marx 11, but rather an attempt of organization and cataloguing of architecture elements, parts and blocks. In his book Style Semper developed an intricate catalogue of transformations, in particular Styles, based on a deep interrogation of distinct objects, resulting in an exceptional understanding of variations in culture.

In this sense, every new style is a result of previous efforts and is within the gravitational field of new materials, new construction methods as well as novel social values see also Charles Jencks, Evolutionary Diagram, Fig. Ultimately Style, to state a possible way to think about it, is simply the application of new materials, techniques and purposes Style as an area of inquiry in architecture theory has a long and painful history, as laid out through the treatises of Gottfried Semper, John Ruskin, Violet le Duc, Garnier and Alois Riegel13, and the opposition to this area of critical interrogation by figures such as Otto Wagner -who started his career profoundly invested in the Historicism of the 19th century, only to reinvent himself as a proponent of modern considerations in architecture- Muthesius, Loos, Augustus Welby Pugin and other critical voices towards a purely historic vantage point of architectural production.

What makes a difference between the analogue methods of inquiry utilized by these critics and the digital methods that can be applied today to plow through massive amounts of historic data about the discipline, is the methods applied to structure and organize Big Data.

However, there is more to discuss than just to drop the issue of Big Data on the table without further closer scrutiny. A first attempt on harnessing Neural Style Transfer techniques for architecture design.

What Mario Carpo missed in his book is the rise of Artificial Intelligence as a tool in contemporary architecture design — there is not a single mention of it in his book The Second Digital Turn.

At least he did not consider it important enough to make it into the index of the book. How could he miss it? Therefore, more complex visual features, like the texture and color of the Casa Batllo roof must be used to differentiate the two. In contrast, the network would need to learn more complex, dense visual feature sets to separate the Casa Batllo image from the Coop Himmelb l au image, e. These example features are highlighted in red in the figure.

This shows once more the slow speed of our discipline and the, at times very slow, ways to adapt to novel technologies. Or to put it this way: Data is the new Oil Because -in an analogy to crude oil- it is almost useless in its unrefined state, but needs to be refined into gas, plastics, chemicals etc. In a similar fashion raw data is pretty much inert, as it is unlegible to the human mind — it needs to be broken down and analyzed in order to reveal the valuable information.

Yes, Data and Information are two distinctly different things. This is also what makes the use of Neural Networks so incredibly powerful. It would go far beyond the boundaries of this article to describe in detail the possible facets in the application of Neural Networks in architecture — reaching from site analysis, to plan analysis to improved methods of Building Information Modeling, to aspects of ecologic, economic and social impact of a project — the opportunities to reveal the profound nature of a project are gigantic.

As this article is primarily concerned with aspects of style by using Style transfer techniques aided by Neural Networks, it is worth inquiring on the use of Big Data in order to interrogate aspects of style. The larger a data set of anything is, the more accurate the results generated by a Neural Network will be.

USA In researching the issue of style in architecture the size of the datasets is crucial. It starts with around images upwards of a particular style or feature to train a network to implement specific stylistic features.

As the technique is based on machine learning through the examination of features within an image, the quality of the images of a database are very important. In an episode of the design of the Robot Garden Fig9 by SPAN, a deep dream neural network was trained to understand what fountains are.

After several iterations of the training the Neural Network made a weird connection — it recognized the spout of water emanating from a spring fountain as a crucial feature and started to recognize this feature all over the place in the target image. Fig10 Resulting in strange color distortions in the vertical dimension of the image. This being said, it made a far better job in recognizing the features of a column.

Architectural formal language can be transformed into computer language when combing the development direction of digital assisted design. Structural knowledge of architectural language provides a good foundation. Symbol- based processing methods can be proposed, modularizing knowledge to simulate human thinking process, and also intending to transform architectural language into computer language to represent knowledge with certain symbols.

This architecture symbol database can use data as parameters to generate artificial intelligent design simulation. When computers can relate subjective concepts to corresponding specific symbols, in fact artificial intelligence will have the ability to create comprehensive schemes that satisfy both objective constraints and subjective concepts related to region culture and aesthetics. By incorporating subjective factors into the parameter system, it is quantified.

In the case of known basic rules, there is no need to input a lot of detailed knowledge, it is easy to modularize and easy to modify; it can be well connected with a symbol database.

This can help in traditional architecture design, for example, where an index of architectonics can be implemented and an artificial intelligent agent can pick and assemble.

When it comes to more complex knowledge; like architecture theory, styles, identity and aesthetics, it takes a collaborative work. It is a long research that needs time and human power to sum up architecture knowledge.

This would need a knowledge center that breaks down architecture into language that can be fed to artificial intelligent agents. Breaking down architecture language cannot be done by a single person. It should be an ongoing process that includes different opinions. And upon discussions and agreements, empirical definitions of architectural styles and methods will be generated to formulate a database that will feed artificial intelligent agents.

This agreed upon knowledge can then be transformed into computer language that becomes an empirical reference for architecture. It first takes human intervention and understanding to classify architecture objects and elements into different categories. These categories can include anything and everything in the architecture discourse. The most important pillar in such a database, and what it makes it differs from any other computer database is that first it is an open source it is a collaborative work where different people can always add information and second, it is an ongoing process.

The difference between computer science and artificial intelligence shows prominently here; AI can accumulate knowledge through Enactivism3. The database accumulates and adjusts automatically and there is always the human intervention to manage and control. Artificial intelligence will have the ability to generate rapid mass production in the future, and designers will only need to set the constraints and filter the AI scheme according to aesthetics, client or market need.

Artificial intelligence can not only comprehensively consider the quantitative processing of various subjective and objective factors, but also have the ability to quickly generate a variety of styles. Even in a sense, the results will completely exceed our imagination After generating a large number of "creative" solutions, the computer needs to judge the screening of the results.

The evaluation criteria for designing good and bad are more 3 Enactivism argues that cognition arises through a dynamic interaction between an acting organism and its environment. Organisms do not passively receive information from their environments, which they then translate into internal representations. Natural cognitive systems participate in the generation of meaning, engaging in transformational and not merely informational interactions: they enact the world.

There are two aspects of evaluation; objective logic based on mechanical laws and subjective experience based on culture and aesthetics, we have strict laws of physics in mechanics — this can easily be judged by a machine, and subjective level of cultural aesthetics.

Subjective judgment needs first human intervention, the findings can be then fed into an AI machine. Then, applying intelligent learning algorithms will be able to create an artificial intelligent judging tool.

The technology used is again Deep Learning. Architecture is not a mathematical equation where the answer is either right or wrong; evaluation of architecture design in not a true or false procedure. Sometimes architecture projects are misunderstood and the evaluation might result unfair. An artificial intelligent architecture database with a judging logic can help in evaluating architecture projects in an objective manner. We all studied the golden ratio in early bachelor years.

It is an example of how beauty can be measured mathematically. Imagine how much this spectrum can be widened with AI. With an artificial intelligent architecture database this process can be faster and more efficient.

And it can be reversed. Client something more contemporary? It is the same process of Google search engine. But, the machine automatically generates several designs proposals based on the clients parameters. It is just an arbitrary example of what could possibly happen. This might deduce that the architect is dead. Bersekas, D. Neuro-Dynamic Programming. Athena: Scientific. Retrieved May 8, , from bimpanzee. Bishop, C. Pattern Recognition and Machine Learning. Bouman, O. The Invisible in Architecture.

London: Academy Group Ltd. Broadbent, M. Design in Architecture and the Human Sciences. London: David Fulton Publishers Ltd. Cai, L. The definition, application and impact of artificial intelligence in the design perspective. Chalmers, D. Facing up to the Problem of Consciousness. Journal of Consciousness Studies. Chambers Biographical Dictionary.

Cambridge: Cambridge University Press. Deep AI. Retrieved May 7, , from deepai. Deep Minds. Retrieved May 15, , from deepminds. Di Paolo, E. Oxford: Oxford University Press. Donati, A. Retrieved May 8, , from forbes. Fodor, J. The Language of Thought. Cambridge: Harvard University Press. Frazer, J. Parametric Computation: History and Future.

London: Architectural Association. Gershenson, C. Requisite Variety, Autopoiesis and Self-organization. Universidad National Autonoma de Mexico. Harnad, S. Why and How We are not Zombies.

The main branches of Artificial Intelligence are: Perception - understanding images, audio, etc. Reasoning - answering questions from data Planning - inferring the required steps to reach a goal Motion - moving a robot in an environment Natural language processing - understanding human language.

Artificial intelligence AI can be used in many sectors such as transportation, finance, healthcare, banking etc. This includes classification, properties, and biological importance of biomolecules. It will introduce the students to the concept of genetic code and concept of heredity. The key emphasis is placed on understanding the basic principles that govern the biological functions of biomolecules.

Table of Contents. Artificial Intelligence Get this Book. Introduction to Artificial Intelligence Get this Book.