Technologists in Sync: Generative Design

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Artificial Intelligence - Generative Design.

Any iterative rule-based technique used to develop several choices that fulfill a stated set of objectives and constraints is referred to as generative design.

The end result of such a process may be anything from complicated architectural models to works of art, and it could be used in a number of industries, including architecture, art, engineering, and product design, to mention a few.

A more conventional design technique involves evaluating a very small number of possibilities before selecting one to develop into a finished product.

The justification for utilizing a generative design framework is that the end aim of a project is not always known at the start.

As a result, the goal should not be to come up with a single proper solution to an issue, but rather to come up with a variety of feasible choices that all meet the requirements.

Using a computer's processing capacity, multiple variations of a solution may be quickly created and analyzed, much more quickly than a person could.

As the designer/aims user's and overall vision become clearer, the input parameters are fine-tuned to refine the solution space.

This avoids the problem of being locked into a single solution too early in the design phase, allowing for creative exploration of a broad variety of possibilities.

The expectation is that by doing so, the odds of achieving a result that best meets the defined design requirements will increase.

It's worth noting that generative design doesn't have to be a digital process; an iterative approach might be created in a physical environment.

However, since a computer's processing capacity (i.e., the quantity and speed of calculations) greatly exceeds that of a person, generative design approaches are often equated with digital techniques.

The creative process is being aided by digital technologies, particularly artificial intelligence-based solutions.

Generative art and computational design in architecture are two examples of artificial intelligence applications.

The term "generative art," often known as "computer art," refers to artwork created in part with the help of a self-contained digital system.

Decisions that would normally be made by a human artist are delegated to an automated procedure in whole or in part.

Instead, by describing the inputs and rule sets to be followed, the artist generally maintains some influence over the process.

Georg Nees, Frieder Nake, and A. Michael Noll are usually acknowledged as the inventors of visual computer art.

The "3N" group of computer pioneers is sometimes referred to as a unit.

Georg Nees is widely credited with the founding of the first generative art exhibition, Computer Graphic, in Stuttgart in 1965.

In the same year, exhibitions by Nake (in cooperation with Nees) and Noll were held in Stuttgart and New York City, respectively (Boden and Edmonds 2009).

In their use of computers to generate works of art, these early examples of generative art in the visual media are groundbreaking.

They were also constrained by the existing research methodologies at the time.

In today's world, the availability of AI-based technology, along with exponential advances in processing power, has resulted in the emergence of new forms of generative art.

Computational creativity, described as "a discipline of artificial intelligence focused on designing agents that make creative goods autonomously," is an intriguing subset of these new efforts (Davis et al. 2016).

When it comes to generative art, the purpose of computational creativity is to use machine learning methods to tap into a computer's creative potential.

In this approach, the creativity process shifts away from giving a computer step-by-step instructions (as was the case in the early days) and toward more abstract procedures with unpredictable outputs.

The DeepDream computer vision software, invented by Google developer Alexander Mordvintsev in 2015, is a modern example of computational innovation.

A convolutional neural network is used in this project to purposefully over-process a picture.

This brings forward patterns that correspond to how a certain layer in the network interprets an input picture based on the image types it has been taught to recognize.

The end effect is psychedelic reinterpretations of the original picture, comparable to what one may see in a restless night's sleep.

Mordvintsev demonstrates how a neural network trained on a set of animals can take images of clouds and convert them into rough animal representations that match the detected features.

Using a different training set, the network would transform elements like horizon lines and towering vertical structures into squiggly representations of skyscrapers and buildings.

As a result, these new pictures might be regarded unexpected unique pieces of art made entirely by the computer's own creative process based on a neural network.

Another contemporary example of computational creativity is My Artificial Muse.

Unlike DeepDream, which depends entirely on a neural network to create art, Artificial Muse investigates how an AI-based method might cooperate with a human to inspire new paintings (Barqué-Duran et al. 2018).

The neural network is trained using a massive collection of human postures culled from existing photos and rendered as stick figures.

The data is then used to build an entirely new position, which is then given back into the algorithm, which reconstructs what it believes a painting based on this stance should look like.

As a result, the new stance might be seen as a muse for the algorithm, inspiring it to produce an entirely unique picture, which is subsequently executed by the artist.

Two-dimensional computer-aided drafting (CAD) systems were the first to integrate computers into the field of architecture, and they were used to directly imitate the job of hand sketching.

Although using a computer to create drawings was still a manual process, it was seen to be an advance over the analogue method since it allowed for more accuracy and reproducibility.

More complicated parametric design software, which takes a more programmed approach to the construction of an architectural model, soon exceeded these rudimentary CAD applications (i.e., geometry is created through user-specified variables).

Today, the most popular platform for this sort of work is Grasshopper (a plugin for the three-dimensional computer-aided design software Rhino), which was created by David Rutten in 2007 while working at Robert McNeel & Associates.

Take, for example, defining a rectangle, which is a pretty straightforward geometric problem.

The length and breadth values would be created as user-controlled parameters in a parametric modeling technique.

The program would automatically change the final design (i.e., the rectangle drawing) based on the parameter values provided.

Imagine this on a bigger scale, where a set of parameters connects a complicated collection of geometric representations (e.g., curves, surfaces, planes, etc.).

As a consequence, basic user-specified parameters may be used to determine the output of a complicated geometric design.

An further advantage is that parameters interact in unexpected ways, resulting in results that a creator would not have imagined.

Despite the fact that parametric design uses a computer to produce and display complicated results, the process is still manual.

A set of parameters must be specified and controlled by a person.

The computer or program that performs the design computations is given more agency in generative design methodologies.

Neural networks may be trained on examples of designs that meet a project's general aims, and then used to create multiple design proposals using fresh input data.

A recent example of generative design in an architectural environment is the layout of the new Autodesk headquarters in Toronto's MaRS Innovation District (Autodesk 2016).

Existing workers were polled as part of this initiative, and data was collected on six quantifiable goals: work style preference, adjacency preference, degree of distraction, interconnection, daylight, and views to the outside.

All of these requirements were taken into account by the generative design algorithm, which generated numerous office arrangements that met or exceeded the stated standards.

These findings were analyzed, and the highest-scoring ones were utilized to design the new workplace arrangement.

In this approach, a huge quantity of data was utilized to build a final optimal design, including prior projects and user-specified data.

The data linkages would have been too complicated for a person to comprehend, and could only be fully explored through a generative design technique.

In a broad variety of applications where a designer wants to explore a big solution area, generative design techniques have shown to be beneficial.

It avoids the issue of concentrating on a single solution too early in the design phase by allowing for creative explorations of a variety of possibilities.

As AI-based computational approaches develop, generative design will find new uses.

~ Jai Krishna Ponnappan

You may also want to read more about Artificial Intelligence here.

Artificial Intelligence - What Is Computational Creativity?

Computational Creativity is a term used to describe a kind of creativity that is based on Computer-generated art is connected to computational creativity, although it is not reducible to it.

According to Margaret Boden, "CG-art" is an artwork that "results from some computer program being allowed to operate on its own, with zero input from the human artist" (Boden 2010, 141).

This definition is both severe and limiting, since it is confined to the creation of "art works" as defined by human observers.

Computational creativity, on the other hand, is a broader phrase that encompasses a broader range of actions, equipment, and outputs.

"Computational creativity is an area of Artificial Intelligence (AI) study... where we construct and engage with computational systems that produce products and ideas," said Simon Colton and Geraint A. Wiggins.

Those "artefacts and ideas" might be works of art, as well as other things, discoveries, and/or performances (Colton and Wiggins 2012, 21).

Games, narrative, music composition and performance, and visual arts are examples of computational creativity applications and implementations.

Games and other cognitive skill competitions are often used to evaluate and assess machine skills.

The fundamental criterion of machine intelligence, in fact, was established via a game, which Alan Turing dubbed "The Game of Imitation" (1950).

Since then, AI progress and accomplishment have been monitored and evaluated via games and other human-machine contests.

Chess has had a special status and privileged position among all the games in which computers have been involved, to the point where critics such as Douglas Hofstadter (1979, 674) and Hubert Dreyfus (1992) confidently asserted that championship-level AI chess would forever remain out of reach and unattainable.

After beating Garry Kasparov in 1997, IBM's Deep Blue modified the game's rules.

But chess was just the start.

In 2015, AlphaGo, a Go-playing algorithm built by Google DeepMind, defeated Lee Sedol, one of the most famous human players of this notoriously tough board game, in four out of five games.

Human observers, including as Fan Hui (2016), have praised AlphaGo's nimble play as "beautiful," "intuitive," and "innovative." 'Automated Insights' is a service provided by Automated Insights Natural Language Generation (NLG) techniques such as Wordsmith and Narrative Science's Quill are used to create human-readable tales from machine-readable data.

Unlike basic news aggregators or template NLG systems, these computers "write" (or "produce," as the case may be) unique tales that are almost indistinguishable from human-created material in many cases.

Christer Clerwall, for example, performed a small-scale research in 2014 in which human test subjects were asked to assess news pieces written by Wordsmith and a professional writer from the Los Angeles Times.

The study's findings reveal that, although software-generated information is often seen as descriptive and dull, it is also regarded as more impartial and trustworthy (Clerwall 2014, 519).

"Within 10 years, a digital computer would produce music regarded by critics as holding great artistic merit," Herbert Simon and Allen Newell predicted in their famous article "Heuristic Problem Solving" (1958). (Simon and Newell 1958, 7).

This prediction has come true.

Experiments in Musical Intelligence (EMI, or "Emmy") by David Cope is one of the most well-known works in the subject of "algorithmic composition."

Emmy is a computer-based algorithmic composer capable of analyzing existing musical compositions, rearranging their fundamental components, and then creating new, unique scores that sound like and, in some circumstances, are indistinguishable from Mozart, Bach, and Chopin's iconic masterpieces (Cope 2001).

There are robotic systems in music performance, such as Shimon, a marimba-playing jazz-bot from Georgia Tech University, that can not only improvise with human musicians in real time, but also "is designed to create meaningful and inspiring musical interactions with humans, leading to novel musical experiences and outcomes" (Hoffman and Weinberg 2011).

Cope's method, which he refers to as "recombinacy," is not restricted to music.

It may be used and applied to any creative technique in which new works are created by reorganizing or recombining a set of finite parts, such as the alphabet's twenty-six letters, the musical scale's twelve tones, the human eye's sixteen million colors, and so on.

As a result, other creative undertakings, like as painting, have adopted similar computational creativity method.

The Painting Fool is an automated painter created by Simon Colton that seeks to be "considered seriously as a creative artist in its own right" (Colton 2012, 16).

To far, the algorithm has generated thousands of "original" artworks, which have been shown in both online and physical art exhibitions.

Obvious, a Paris-based collaboration comprised of the artists Hugo Caselles-Dupré, Pierre Fautrel, and Gauthier Vernie, uses a generative adversarial network (GAN) to create portraits of a fictitious family (the Belamys) in the manner of the European masters.

Christies auctioned one of these pictures, "Portrait of Edmond Belamy," for $432,500 in October 2018.

Designing ostensibly creative systems instantly runs into semantic and conceptual issues.

Creativity is an enigmatic phenomena that is difficult to pinpoint or quantify.

Are these programs, algorithms, and systems really "creative," or are they merely a sort of "imitation," as some detractors have labeled them? This issue is similar to John Searle's (1984, 32–38) Chinese Room thought experiment, which aimed to highlight the distinction between genuine cognitive activity, such as creative expression, and simple simulation or imitation.

Researchers in the field of computational creativity have introduced and operationalized a rather specific formulation to characterize their efforts: "The philosophy, science, and engineering of computational systems that, by taking on specific responsibilities, exhibit behaviors that unbiased observers would deem creative" (Colton and Wig gins 2012, 21).

The key word in this description is "responsibility."

"The term responsibilities highlights the difference between the systems we build and creativity support tools studied in the HCI [human-computer interaction] community and embedded in tools like Adobe's Photoshop, to which most observers would probably not attribute creative intent or behavior," Colton and Wiggins explain (Colton and Wiggins 2012, 21).

"The program is only a tool to improve human creativity" (Colton 2012, 3–4) using a software application like Photoshop; it is an instrument utilized by a human artist who is and remains responsible for the creative choices and output created by the instrument.

Computational creativity research, on the other hand, "seeks to develop software that is creative in and of itself" (Colton 2012, 4).

On the one hand, one might react as we have in the past, dismissing contemporary technological advancements as simply another instrument or tool of human action—or what technology philosophers such as Martin Heidegger (1977) and Andrew Feenberg (1991) refer to as "the instrumental theory of technology."

This is, in fact, the explanation supplied by David Cope in his own appraisal of his work's influence and relevance.

Emmy and other algorithmic composition systems, according to Cope, do not compete with or threaten to replace human composition.

They are just instruments used in and for musical creation.

"Computers represent just instruments with which we stretch our ideas and bodies," writes Cope.

Computers, programs, and the data utilized to generate their output were all developed by humanity.

Our algorithms make music that is just as much ours as music made by our greatest human inspirations" (Cope 2001, 139).

According to Cope, no matter how much algorithmic mediation is invented and used, the musical composition generated by these advanced digital tools is ultimately the responsibility of the human person.

The similar argument may be made for other supposedly creative programs, such as AlphaGo, a Go-playing algorithm, or The Painting Fool, a painting software.

When AlphaGo wins a big tournament or The Painting Fool creates a spectacular piece of visual art that is presented in a gallery, there is still a human person (or individuals) who is (or can reply or answer for) what has been created, according to the argument.

The attribution lines may get more intricate and drawn out, but there is always someone in a position of power behind the scenes, it might be claimed.

In circumstances where efforts have been made to transfer responsibility to the computer, evidence of this already exists.

Consider AlphaGo's game-winning move 37 versus Lee Sedol in game two.

If someone wants to learn more about the move and its significance, AlphaGo is the one to ask.

The algorithm, on the other hand, will remain silent.

In actuality, it was up to the human programmers and spectators to answer on AlphaGo's behalf and explain the importance and effect of the move.

As a result, as Colton (2012) and Colton et al. (2015) point out, if the mission of computational creativity is to succeed, the software will have to do more than create objects and behaviors that humans interpret as creative output.

It must also take ownership of the task by accounting for what it accomplished and how it did it.

"The software," Colton and Wiggins argue, "should be available for questioning about its motivations, processes, and products," eventually capable of not only generating titles for and explanations and narratives about the work but also responding to questions by engaging in critical dialogue with its audience (Colton and Wiggins 2012, 25). (Colton et al. 2015, 15).

At the same time, these algorithmic incursions into what had previously been a protected and solely human realm have created possibilities.

It's not only a question of whether computers, machine learning algorithms, or other applications can or cannot be held accountable for what they do or don't do; it's also a question of how we define, explain, and define creative responsibility in the first place.

This suggests that there is a strong and weak component to this endeavor, which Mohammad Majid al-Rifaie and Mark Bishop refer to as strong and weak forms of computational creativity, reflecting Searle's initial difference on AI initiatives (Majid al-Rifaie and Bishop 2015, 37).

The types of application development and demonstrations presented by people and companies such as DeepMind, David Cope, and Simon Colton are examples of the "strong" sort.

However, these efforts have a "weak AI" component in that they simulate, operationalize, and stress test various conceptualizations of artistic responsibility and creative expression, resulting in critical and potentially insightful reevaluations of how we have defined these concepts in our own thinking.

Nothing has made Douglas Hofstadter reexamine his own thinking about thinking more than the endeavor to cope with and make sense of David Cope's Emmy nomination (Hofstadter 2001, 38).

To put it another way, developing and experimenting with new algorithmic capabilities does not necessarily detract from human beings and what (hopefully) makes us unique, but it does provide new opportunities to be more precise and scientific about these distinguishing characteristics and their limits.

~ Jai Krishna Ponnappan

You may also want to read more about Artificial Intelligence here.

Artificial Intelligence - What Is The AARON Computer Program?

Harold Cohen built AARON, a computer program that allows him to produce paintings.

The initial version was created "about 1972," according to Cohen.

Because AARON is not open source, its development came to a halt when Cohen died in 2016.

In 2014, AARON was still creating fresh photos, and its functioning was still visible in 2016.

AARON is not an abbreviation.

The name was chosen since it is the first letter of the alphabet, and Cohen anticipated that he would eventually build further programs, which he never did.

AARON has various versions during the course of its four decades of development, each with its own set of capabilities.

Earlier versions could only generate black-and-white line drawings, while later versions could also paint in color.

Some AARON versions were set up to make abstract paintings, while others were set up to create scenes with objects and people.

AARON's main goal was to generate not just computer pictures, but also physical, large-scale images or paintings.

The lines made by AARON, a program written in C at the time, were traced directly on the wall in Cohen's show at the San Francisco Museum of Modern Art.

The software was then paired with a machine that had a robotic arm and could apply paint on canvas in later creative episodes of AARON.

For example, the version of AARON on display at Boston's Computer Museum in 1995, which was written in LISP at the time and ran on a Silicon Graphics computer, generated a file containing a set of instructions.

After then, the file was transmitted to a PC that was running a C++ program.

This computer was equipped with a robotic arm.

The C++ code processed the commands and controlled the arm's movement, as well as the dye mixing and application to the canvas.

Cohen's drawing and painting devices were also significant advancements.

Industrial inkjet printers were employed in subsequent generations as well.

Because of the colors these new printers could create, Cohen considered this configuration of AARON to be the most advanced; he thought that the inkjet was the most important innovation since the industrial revolution when it came to colors.

While Cohen primarily concentrated on tactile pictures, Ray Kurzweil built a screensaver version of AARON around the year 2000.

By 2016, Cohen had developed his own version of AARON, which produced black-and-white pictures that the user could color using a big touch screen.

"Fingerpainting," he called it.

AARON, according to Cohen, is neither a "totally independent artist" nor completely creative.

He did feel, however, that AARON demonstrates one requirement of autonomy: emergence, which Cohen defines as "paintings that are really shocking and unique." Cohen never got too far into AARON's philosophical ramifications.

It's easy to infer that AARON's work as a colorist was his greatest accomplishment, based on the amount of time he devotes to it in practically all of the interviews conducted with him.

Computational Creativity and Generative Design are two more terms for the same thing.

~ Jai Krishna Ponnappan

You may also want to read more about Artificial Intelligence here.

See also: Computational Creativity; Generative Design.

Artificial Intelligence - Generative Design.

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Artificial Intelligence - What Is Computational Creativity?

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Artificial Intelligence - What Is The AARON Computer Program?

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