Benjamin Dillenburger @ AI Breakfast at WEF

Prof. Dr. Benjamin Dillenburger RETHINK Director will be a guest at the AI Breakfast at WEF.

The ETH AI Center and Merantix hold an AI-focused breakfast event as part of ETH Zurich’s presence at the upcoming World Economic Forum (WEF).

This exclusive event provides an opportunity for AI thought leaders from research, politics, and the economy to enhance interdisciplinary knowledge sharing, have discussions covering topics from technology development to regulation and ethics to policy issues that arise out of AI, and overall strengthen the ties between different stakeholders involved in shaping the future of AI with a focus on Europe.

Date24 May 2022
Time09.15 – 10.45
LocationETH Zurich Pavillion
Talstrasse 41
7270 Davos Platz
Switzerland
OrganisationETH AI Center
LanguageEnglish
Further InformationMore details

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Future of Construction symposium

The Future of Construction features two symposiums with complementary topics, i.e. one on Computational Design for Sustainable Construction organized by members from Design++, and one on Construction Robotics organized by members from NCCR dfab. The shared goal of the joint event is to bring together experts from various fields, such as architecture, sensing, robotics, engineering, computer science, circular construction, computer vision and more. We will address the challenges for the future of construction, focus on innovative methods, share knowledge, and establish a dialogue between international participants from academia and industry. This exchange will help identify needs and opportunities of the various involved research fields for successful future collaborations.

Visit the website for Registration and up-​to-date information: futureofconstruction.ch

Official opening of Design++

RETHINK is proud to announce the opening of our ETH partner initiative DESIGN++.  Join us at the official opening of Design++, the new Center for Augmented Computational Design in Architecture, Engineering and Construction at ETH Zurich on May 27th 2021, 16:00 – 18:00.


During the event, you will hear about research perspectives from the diverse and interconnected domains of Civil Engineering, Architecture and Computer Science. The event will also feature the opening of the Immersive Design Lab (IDL) which was initiated and implemented by Gramazio Kohler Research, ETH Zurich.


The event will be held online via Zoom, and you can register on Eventbrite, which is also available by scanning the QR code on the PDF invitation at the bottom of the page.

The agenda for the event includes the following:
16.00 – 16.02: Welcome – Prof. Daniel Hall
16.02 – 16.10: Opening statement from ETH Zurich VP Research – Prof. Detlef Günther
16.10 – 16.15: Overview of the center – Dr. Danielle Griego
16.15 – 16.30: Keynote presentation – Prof. Marc Pollefeys
16.30 – 16.45: Design++ as a strategic initiative, input from the Advisory Board – Prof. Philippe Block, Prof. Stelian Coros, Prof. Robert Flatt
16.45 – 16.55: IDL virtual tour – Prof. Matthias Kohler and Dr. Romana Rust
16.55 – 17.10: Perspectives from Architecture – Prof. Benjamin Dillenburger, Prof. Matthias Kohler, Dr. Romana Rust
17.10 – 17.25: Perspectives from Civil Engineering – Prof. Walter Kaufman, Dr. Michael Kraus
17.25 – 17.40: Perspectives from Computer Science – Prof. Siyu Tang, Dr. Roi Poranne
17.40 – 17.50: Q&A Panel discussion with speakers – moderated by Dr. Iro Armeni
17.50 – 17.55: A statement from our Strategic Partners – Basler & Hofmann
17.55 – 18.00: Closing remarks – Prof. Daniel Hall


More information about Design++: designplusplus.ai
For press inquiries please contact gabaglio@arch.ethz.ch

Very best regards, Design++ Steering Committee

AI for Healthcare Equity Conference

RETHINK is happy to announce the “AI for Healthcare Equity Conference” on the 12th of April, 2021 from out partners at Jamel Clinic:
The potential of AI to bring equity in healthcare has spurred significant research efforts across academia, industry and government. Racial, gender and socio-economic disparities have traditionally afflicted healthcare systems in ways that are difficult to detect and quantify. New AI technologies, however, provide a platform for change. By bringing together thought leaders in these fields, we will assess the current state-of-the-art work in this space, identify key areas of impact, and present machine learning techniques that support fairness, personalization and inclusiveness. We will also discuss the regulatory and policy implications of such innovations.

For further information check out this link: https://www.jclinic.mit.edu/equity-conference

Opportunities in machine automation for construction, agriculture, and forestry

Robotic Systems Lab Workshop

Robotics and AI have a huge impact on the future of construction, forestry, and agriculture. In this workshop, the Robotic Systems Lab discussed different opportunities in automating large scale vehicles such as hydraulic excavators or tree harvester to prevent humans from working in dangerous areas, to increase productivity and precision, and to enable new construction methods.

Vitisit the Robitcs System Lab for more information: https://rsl.ethz.ch/

AIA Symposium Recording and behind the scenes

The AiA Symposium was a huge success, with over 150 participants following our event we were overwhelmed by the positive response and participation. A big thanks goes also to our speakers for their excellent talks and the high quality discussion in the panel.
For those of you who could not follow the life stream please follow the link below to check out the recording:


AiA Symposium


Here is a list of time stamps allowing you to fast forward to the respective speakers (the small white dots in the time line allow you to jump to a speaker directly):


Session 1
10:24    Stanislas Chaillou                               (Latent Architecture)
34:27    Mathias Bernhard                               (Future Vectors, Encoding of Architecture in Various Domains)
58:02    Ania Apolinarska & Luis Salamanca  (Conditional autoencoders for generative architectural design)
1:20:21 Tyson Hosmer & Panagiotis Tigas     (Towards An Autonomous Architecture)


Session 2
1:49:26     Pierre Cutellic  (Neurodesign   Modelling with neural potentials)
2:17:31     Iro Armeni        (Automatically generating structured information on the as-is status of facilities from visual data)
2:39:50     Alec Jacobson (How do we get to -ubiquitous- 3D?)
3:03:57     Caitlin Mueller  (ML for Human Design: Creativity and Performance)


Panel Discussion
3:30:16      Moderation Norman Sieroka


If you have any trouble accessing the link please contact Jan Hiss under jan.hiss@rethink.ethz.ch.

Behind the scenes

Since such a big event always includes a lot of organizational work we wanted to show you a bit how it looked from our site 🙂 We build our headquarter in the RETHINK OpenLab at ETH Zürich were we were able to create a small interview corner and overlooked the webinar technique and Q&A. Big thanks goes to the whole team!

RETHINK WHITE PAPER ON EXPLAINABLE AI

Artificial intelligence approaches are routinely used in many computer-assisted drug discovery tasks, such as property prediction, de novo molecular design, and retrosynthesis planning. Despite their growing ubiquity, these models are notorious for behaving obscurely, which has generated demand for methods that are more readily accessible to the human mind. In a recent RETHINK White Paper, published in the journal Nature Machine Intelligence, we aim to address this point by summarizing the most promising directions in Explainable Artificial Intelligence (XAI) research, as well as daring a forecast towards future opportunities and potential applications in the context of drug discovery. Jiménez-​Luna, J., Grisoni, F. & Schneider, G. (2020) Drug discovery with explainable artificial intelligence. Nature Mach. Intell. 2, 573-​584.

Design++ @RETHINK

Aligning our visions for Design++, the new Center for Augmented Computational Design in Architecture, Engineering and Construction. The objectives of this initiative are to develop digitally augmented design tools with and computational processes that will enable experts to simultaneously increase construction productivity, improve the quality of the built environment and substantially reduce the ecological impact of AEC sectors. We focus extended reality (XR) tools enabled via the new Immersive Design Lab and utilize relevant AI and machine learning methods to explore our research questions.

AIA Symposium on Artificial Intelligence in Architecture, Engineering, and Construction

AIA Symposium on Artificial Intelligence in Architecture, Engineering, and Construction


Tuesday, 20.10.2020
13:15 to 18:00

ETH Zurich Webinar Chaired by Benjamin Dillenburger & Matthias Kohler

The AIA Symposium on Artificial Intelligence in Architecture, Engineering, and Construction will bring together experts and enthusiasts from various fields of academia and industry to explore the potential of artificial intelligence for the design and fabrication of our built environment. As part of the Rethink Design Program and the upcoming Design++ Initiative at ETH Zurich, AIA provides a platform for participants to discuss cutting-edge research, exchange ideas, and strengthen interdisciplinary connections and collaborations.

Opening
13:15 Welcome
Benjamin Dillenburger & Matthias Kohler
(20min talks followed by 5min discussion)


Session 1
13:30 Stanislas Chaillou
13:55 Mathias Bernhard
14:20 Ania Apolinarska & Luis Salamanca 14:45 Tyson Hosmer & Panagiotis Tigas 


Break

15:10-15:30


Session 2

15:30 Pierre Cutellic
15:55 Iro Armeni
16:20 Alec Jacobson
16:45 Caitlin Mueller


Panel Discussion
17:15 – 18:00 Moderation Norman Sieroka

Speakers

Caitlin Mueller

Caitlin Mueller is a researcher, designer, and educator working at the interface of architecture and structural engineering. She is currently an Associate Professor in the Building Technology Program, where she leads the Digital Structures research group. As a researcher, Mueller focuses on developing new computational methods and tools for synthesizing architectural and structural intentions in early-stage design. She also works in the field of digital fabrication, with a focus on linking high structural performance with new methods of architectural making. In addition to her digital work, she conducts research on the nature of collaboration between architects and engineers from a historical perspective. Mueller also aims for interdisciplinary learning and integration in her teaching efforts, which include subjects in structural design and computational methods.


Alec Jacobson

Alec Jacobson is an Assistant Professor and Canada Research Chair in the Departments of Computer Science and Mathematics at University of Toronto. He was a post-doc at Columbia University. He received a PhD in Computer Science from ETH Zurich and MA and BA degrees in Computer Science and Mathematics from the Courant Institute of Mathematical Sciences at New York University. He has published several papers in the proceedings of ACM SIGGRAPH. He leads development of the widely used geometry processing library, libigl, winner of the 2015 SGP software award. In 2020, he received the ACM SIGGRAPH Significant New Researcher Award


Tyson Hosmer

Tyson Hosmer is a Senior Teaching Fellow at UCL Bartlett BPro in London where he directs the Living Architecture Lab (Research Cluster 3) focused on developing autonomous reconfigurable architecture with artificial intelligence, recently winning the Autodesk Emerging Research Award at Acadia 2019 for his co-authored paper Towards an Autonomous Architecture. He is an Associate with Zaha Hadid Architects where he leads grant-funded research development of cognitive agent-based technologies and machine learning for design. His thirteen years of experience in practice includes working in Asymptote Architecture, Kokkugia, AXI:OME, and serving as Research Director with Cecil Balmond Studio along with teaching for over six years with the AADRL.

Panagiotis Tigas
Panos is a DPhil student and researcher at the University of Oxford (OATML group) and Machine Learning tutor at Bartlett School of Architecture (Living Architecture Lab). His research areas include applied and theoretical cybernetics, autonomous and intelligent natural and artificial systems, generative and algorithmic design. He has worked for Microsoft, Autodesk Research (AI/Generative Design), Brave Research (Privacy-Preserving ML), and his own startup Filisia Interfaces (Special Education Tech).


Stanislas Chaillou
A Paris native, Stanislas received his undergraduate degree in Architecture at the Swiss Federal Institute of Technology of Lausanne, and his Master in Architecture from the Harvard Graduate School of Design. Focusing his practice around Architecture and Artificial Intelligence, he believes in the necessary integration of both disciplines to change our built environment. Stanislas works today as an Architect & Data Scientist at Spacemaker’s R&D department. He is in charge of developing AI research projects, to assist and enhance architectural conception. He recently curated the exhibit “AI & Architecture” at the Arsenal Pavilion in Paris. Stanislas also published in a variety of reviews and platforms, among which are ARCH+, the Harvard Real Estate Review, the Veolia FACTS Reports, Towards Data Science, Archdaily, Archinect, and others. Stanislas was awarded two American Architecture Prizes (2017) and the Architecture Masterprize (2018). He also received the 2018 ZGF scholarship for his past architectural work. He is a Fulbright scholar, an Arthur Sachs Fellow and Jean Gaillard Fellow at Harvard University.


Ania Apolinarska

Aleksandra Anna Apolinarska is a postdoctoral researcher at Gramazio Kohler Research at ETH Zurich. Originally educated as an architect, she specializes in computational methods for complex design problems. In her PhD, she studied interrelations between design, performance and fabrication of novel complex timber structures enabled by digital workflows. Prior to her academic career, she worked at architectural practices and consultancies such as Foster+Partners and DesignToProduction on multiple geometrically challenging projects. Her current research at ETH Zurich seeks opportunities to augment the design process with data and machine learning.


Luis Salamanca

Luis Salamanca is senior data scientist at the Swiss Data Science Center, ETH Zürich. After his studies on electrical engineering, he carried out a Ph.D. on coding theory, applying Bayesian inference and message passing algorithms to communication theory’s problems. Thereafter, he started working as a postdoc at the Luxembourg Center for systems biomedicine on more applied medical imaging and neuroscience problems. There he got immersed in more interdisciplinary research, collaborating with MDs, biologists, etc. At the SDSC he is expanding his interdisciplinarity by working on projects revolving around natural language processing, interactive generative design, and other various topics.


Pierre Cutellic
Pierre Cutellic is a trained architect and designer. He is currently conducting research as a PhD Fellow at the Chair of Digital Architectonics, Institute of Technology for Architecture, ETH Zürich since 2016. His research focuses on the Brain-Computer Interface integrations of peculiar neuro-signals together with machine learning, for decision-making and learning automation of architectural modelling and design processes. Pierre graduated summa cum laude in Architecture from E.N.S.A. Paris-Malaquais in 2007, and joined the AEC consulting firm Gehry Technologies (nka. Trimble Consulting) from 2008 to 2010 as a consultant for large culture & arts projects in the EU and UAE. From 2010 to 2015, he was teaching as Adjunct-Assistant Professor in digital knowledge and practices, integrative design and production, human computation and algorithmics at the Digital Knowledge Dept. of Paris-Malaquais. His past professional experience and academic research have been frequently published in AEC related journals and conferences since 2010. Prior to joining ETH, Pierre was a lecturer at the CNPA Laboratory of EPF Lausanne since 2014.


Mathias Bernhard
Mathias Bernhard is a postdoctoral researcher at the Weitzman School of Design, University of Pennsylvania. Before that, he was postdoc at Digital Building Technologies, ETH Zurich, investigating computational design tools for additive manufacturing. He holds a Doctor of Sciences (Dr. sc.) in Architecture from ETH Zurich. His thesis “Domain Transforms in Architecture – Encoding and Decoding of Cultural Artefacts” draws a map of the variegated field of CAD, and investigates the implications of alternative models of abstraction and their creative potential. He is an architect with profound specialization in computational design, digital fabrication, and information technology. In particular, he is interested in how artefacts can be encoded, made machine-readable, and digitally operational. His research focuses on how the increasingly ubiquitous availability of data and computational power influences the design process and how different methods of artificial intelligence, machine learning or evolutionary strategies can be employed in the development of our built environment.

Iro Armeni
Iro Armeni completed her PhD at Stanford University this August, Civil and Environmental Engineering (CEE) Department, Sustainable Design and Construction (SDC) Program, with a PhD minor at the Computer Science Department. She conducted research under the supervision of Martin Fischer (CEE, Center for Integrated Facility Engineering – CIFE) and Silvio Savarese (Computer Science Department, Stanford Vision and Learning Lab – SVL). Iro Armeni is interested in interdisciplinary research between Civil Engineering and Machine Perception. Her area of focus is on automated semantic and operational understanding of buildings throughout their life cycle using visual data. Prior to her PhD, Iro Armeni received a MEng in Architecture and Digital Design (University of Tokyo-2011), a MSc in Computer Science (Ionian University-2013),vand a Diploma in Architectural Engineering (National Technical University of Athens-2009).


Norman Sieroka
Norman Sieroka has been full Professor for Theoretical Philosophy at the University of Bremen since 2019. He studied philosophy, physics and mathematics at Heidelberg University and the University of Cambridge. At Cambridge he received his MPhil degree in history and philosophy of science in 1999; in Heidelberg he received his diploma in physics in 2002 and in 2004 he earned his doctorate in physics at the Institute for Theoretical Physics there. Subsequently, at the ETH Zurich, he earned a further doctorate as well as his habilitation, both in philosophy. After working as a researcher and lecturer in physics, neurophysiology, and philosophy at Heidelberg and Bamberg Universities, Norman Sieroka was employed at the ETH Zurich from 2004 to 2019. He held positions as a postdoc and a senior research fellow at the ETH Chair of Philosophy. From 2016 to 2019 he was the managing director of the Turing Centre Zurich and educational developer for the “Critical Thinking” Initiative of ETH (CTETH). He was a visiting faculty member in the Departments of Philosophy and of History and Philosophy of Science at the University of Notre Dame in the U.S. in 2015; and an associated member of the Center “History of Knowledge” of the ETH and the University of Zurich from 2015 to 2017. Since 2018 Norman Sieroka is also a member of the Governance Board of the ETH think tank “RETHINK”.


Philosophy of AI and the Role of Digital Design

Philosophy is often about “big concepts”; concepts such as knowledge, understanding, autonomy, transparency, intelligence, and creativity. And all these concepts are at stake in the context of current research in data science and artificial intelligence. It seems inescapable that we lose some of our own autonomy once our cars start driving autonomously and our houses become smarter and smarter. Computers outsmart us in number crunching since decades, but will they also outsmart us in creativity? Will they become the “better scientists” or will there always remain a difference between “pure prediction” and “real understanding”? Is predictive success acceptable even if it comes with a loss in transparency? After all, transparency is something we are very much worried about not only in science but in all kinds of political and societal contexts. At the same time, privacy and data protection laws are a major theme in public discourse as well. Consider tracking apps, for instance—do we really want to become transparent citizens and consumers, X-rayed as it were by a machine learning algorithm no one might actually understand?


Coordinating concepts and experiences


 These are only a few of the numerous well-known questions that people are currently concerned about in relation to AI and data science. What might be the role of philosophy in this context? According to A.N. Whitehead, the purpose of doing philosophy is “to coordinate the current expressions of human experience” (Whitehead 1933: 286). Of course, the use of such concepts as knowledge, transparency, creativity, and autonomy stretches across various contexts of experience—from everyday business to global politics, science and so on. Thus, philosophy is about taking these different contexts seriously and gaining a better understanding of “big concepts” by putting their different uses and connotations into a meaningful structure. Such a structure, however, cannot be fixed once and for all. This is what Whitehead’s phrasing “current expressions of human experience” emphasizes. We do not experience things the same way (and maybe not even the same things) as people did a hundred or a thousand years ago. In order to distinguish more pervasive issues from what might rather be temporary particularities, it is therefore helpful to have a look at history and at the way the current situation came about. What, for instance, is really new about contemporary data science as compared to data-based (observational) research as conducted in the past? Might “understanding” or “creativity” be good terms to characterize and distinguish a humanly pre-trained software like AlphaZero from a fully self-trained software like AlphaGo? Or is the use of these terms to be restricted to actions carried out solely by human beings—and why?


Tackling these questions is likely to reveal shifts in the meanings and implications of those “big concepts”. And it will lead to a critical and historical awareness which, in turn, will reduce the danger of becoming obsessed, panicked, or paralyzed by contingent current developments. In fact, such an awareness may even lead to a kind of toolbox increasing one’s ability to cope with present and future obstacles (cf. Sieroka et al. 2018; also for the relation to the notion of responsibility).


Some examples
Let us assume transparency is indeed of fundamental value. But so is the saving of human life. So what about, for instance, using an “intransparent” emergency care app on my smartphone? If my concern is about whether I am having a heart attack and what to do next, I am not so much worried about the transparency of some underlying biomedical theorizing. Moreover, there might not be that much “scientific transparency” in my physician’s claim about whether I had a heart attack or not, either. Why should I then mistrust “Doctor App” on my smartphone whose diagnosis is based on the data of fifty million heart attacks, say, whereas my physician has experienced maybe fifty cases altogether? However, one might argue that this should not stop us from aiming at “transparency” in the long run. Even if “Doctor App” is currently more successful in its predictions, its effectiveness may not be sustainable or resilient if there is no understanding of the underlying physiological processes involved. That is, there is a danger of “Doctor App” relying too closely on a particular set of data, which would then hinder adaptability to new data and to a wider range of applications.


This may suffice to illustrate that many further questions arise about “transparency” in the context of knowledge acquisition, especially as it can be based on very different research agendas (theory formation vs. exploration vs. problem solving). Moreover, transparency issues are also crucial in moral contexts: presumably I do not want “Doctor App”—after it stumbled across the abnormal values in my last liver function test—to secretly order a new liver on the darknet.


This example also leads to further questions about autonomy, both regarding increased autonomy on the side of AI systems and possible losses of autonomy on the side of human beings. Consider, for instance, the kind of autonomy that I lose if, at some later stage, I live in an elderly home and am looked after by some robot? The main worry here is related to a loss in dignity and self-esteem. There might surely be contexts in which I will feel a treatment by “only a robot” as being impersonal and unworthy. But then again there might also be contexts in which I feel less embarrassed with “only a robot” witnessing my bodily deterioration.


The philosopher T.W. Adorno already scented the decline of the West and the rise of fascism when encountering automatic doors. That is, long before concerns about care robots, the simple loss of a standard door handle meant for him the loss of a “core of experience”, wresting away part of one’s individual agency in passing (or not passing) through a door (Adorno [1951] 1997: 40). Of course, it is not me who opens the door then. But to what extent is it really a relevant loss in autonomy? If autonomy is about making decisions which are not (causally or otherwise) fixed, then I am still autonomous in either passing through the door or not. Hence, it is true that a part of my action gets automatized, but one may question whether this part is really the most relevant one in terms of individual autonomy. Or, to provide a different but not unrelated example, consider autonomous driving: if my car drives autonomously, admittedly there are many decisions which I no longer make myself (when to signal, how fast to accelerate …). However, there are many other decisions I may now make instead. I might decide about changing the radio station or about whether to look out of the left or the right window. Thus, there might be no overall loss in my autonomy, even though my car might have gained some. Maybe the space of possibilities has widened.


Here, again, the attempt to coordinate experiences and concepts is revealing: if autonomy is about previously unfixed decisions, then my car may indeed be considered “autonomous” because there are outputs (its particular acceleration, say, when entering the motorway under current traffic conditions) which may be based on implemented algorithms but which are not fully fixed beforehand. So “being autonomous” might be synonymous with “self-programming” which, in turn, may raise further questions (a) about crucial shifts that happened in computer science and the computer industry over the past few decades (cf. Gugerli 2019) and (b) about relations to experiential contexts other than road traffic and AI. For instance, the question of whether or how a notion of “autonomy” applies not only to persons but also to cars might have severe legal consequences, too.


Digital design and material cultures


So far my discussion focused on concepts (knowledge, autonomy, etc.) and thus on cognitive or mental content rather than on material objects. However, the advancement of data science and AI over the last decades also went alongside crucial changes in our material culture. Not only can we find computer hardware, smartphones, etc. nearly everywhere. There is also a huge change in what can be built—on large as well as small scales—based on machine learning algorithms, based on data-intensive 3D-printing, etc.


The keyword here is “digital design” and the reason for treating it in the present context is that material objects, too, are part and parcel of our encounter with the world around us. Thus, if philosophy is about coordinating experiences, material cultures are to be considered as well. Let me briefly mention two examples from contemporary research as an illustration.


The first example is drug discovery. The advance of AI and data science continues to have a huge impact on the invention of new medicines (Schneider et al. 2019). Chemical analyses and adjustments in the compilation of active components can be automatized, and thus timeliness, which is often needed in health care, can be increased immensely. Next, there are questions of personalized medicine—something which, of course, is a very data intensive enterprise. Moreover, many of the philosophical questions already mentioned above reappear in this “materialized” context in an interesting vein. For instance, questions about creativity: what if a drug was designed (composed) by a machine learning process? How to describe the role played by the scientist who set it up initially? What about the role of the algorithm and what about the material setup which eventually did the mixture?


Whereas drug discovery provides an important example for digital design in the realm of small scales (indeed down to molecular structures), an important example for digital design involving large scales is architecture (Frazer 1995). Despite this difference in physical scaling, however, crucial questions in relation to data science and AI are strikingly similar. In particular, they once more concern the role and dynamics of personalization, automation, and materialization. For instance, there are again questions about automatized quality assurance (is this new material or new construction method really reliable?) and about personal autonomy (to what extent am I allowed to manually override the settings of my smart house?). Once more, there are efforts to reduce execution times, to use less material resources, to facilitate fabrication processes, to integrate different stakeholders on an equal footing and so on; and, once more, this raises questions about creativity and the like.


Of course, the situations in architecture and drug discovery are not fully analogous. However, it might be revealing to compare some of their underlying aims and values in the context of digital design. Design processes, it seems, are always about efficacy; that is, they aim at optimizing something. In the present case this might be drug efficacy on the one hand and maybe energy efficiency on the other. But how exactly can these types of efficacy be evaluated and set off against drawbacks such as side effects (drug discovery) and functional unsuitability (architecture)? And what exactly is the role of “the digital” in this context? Again, is it only about improvements in number crunching or is there something new and creative to it? What about even such a sweeping value as beauty? One might argue that beauty is important in architecture but not in drug discovery. But what if beauty can, at least to some extent, be broken down (and maybe “number crunched”) to structural features such as symmetries: is the preselection of molecules in drug design not based on structural features?


Given all these thought-provoking questions I am very happy that, after tackling drug discovery as RETHINK’s first challenge, we will now take architecture as our second challenge. The shift from small molecules to huge buildings will surely help to learn more about both specific and pervasive issues of modern data science and AI—and philosophy will contribute its share in “coordinating our experiences”.


Norman Sieroka (University of Bremen // ETH D-CHAB)


References:


Theodor W. Adorno: Minima Moralia: Reflections on a Damaged Life, trans. Edmund Jephcott. London: Verso 1997.


John Frazer: An Evolutionary Architecture. London: Architectural Association 1995. David Gugerli: Digitalkolumne. Das Autonomieproblem digitaler Gesellschaften. In: Merkur. Deutsche Zeitschrift für europäisches Denken 73 (837), 2019, S. 63-71.


Petra Schneider et al.: Rethinking Drug Design in the Artificial Intelligence Era. Nature Reviews Drug Discovery 19 (5), 2020, S. 353-364 [DOI: 10.1038/s41573-019-0050-3].


Norman Sieroka, Vivianne I. Otto, and Gerd Folkers: Critical Thinking in Education and Research—Why and How? Guest Editorial, Angewandte Chemie (International Edition) 57 (51), 2018, S. 16574-16575. [DOI: 10.1002/anie.201810397].


Alfred North Whitehead: Adventures of Ideas. Cambridge: CUP 1933.