It was never about replacing the artist: AI and post-creativity

Pau Waelder

The following text is an excerpt from my contribution to the book The Meaning of Creativity in the Age of AI, edited by Raivo Kelomees, Varvara Guljajeva, and Oliver Laas (Tallinn: EKA, 2022). The volume is focuses on critical observations of the possibilities of Artificial Intelligence in the field of the arts and includes contributions by artists, art professionals, and scholars Varvara Guljajeva, Chris Hales, Mar Canet Solà, Jon Karvinen, Luba Elliot, Oliver Laas, Raivo Kelomees, Mauri Kaipainen, Pia Tikka, and Sabine Himmelsbach.

The book, which addresses key questions currently being debated around AI systems such as DALL-E 2 and Chat GPT, has been recently made available as a free PDF.

Cover of the book The Meaning of Creativity in the Age of AI (EKA, 2022)

Can you teach your machine to draw?

On 5th February 1965, during the opening of Georg Nees’ exhibition of algorithmic art at the Technische Hochschule in Stuttgart, there was an exchange between the engineer and an artist who asked him provocatively if he could teach the computer to draw the same way he did. Nees replied that, given a precise description, he could effectively write a program that would produce drawings in the artist’s style (Nake, 2010, p.40). His response echoes the conjecture that had given birth to the field of artificial intelligence ten years earlier: that “every aspect of learning or any other feature of intelligence can in principle be so precisely described that a machine can be made to simulate it” (Moor, 2006, p.87). It should be noted that, at least at this point, the machine is not meant to think or create, but simulate. In his seminal paper from 1950, Alan Turing already suggested that computers could perform an “imitation game” (later known as the Turing Test) in which the aim was to mimic human intelligence to the point of seeming human to an external observer (Turing, 1950).

Therefore, what Nees asserted is that the computer could create a successful imitation of the artist’s work. The exchange between Nees and the artist did not go well, as the engineer’s vision of a computable art seemed to threaten the superiority of artistic creativity. Upset and resentful, the artist and his colleagues left the room, with philosopher Max Bense trying to appease them by calling the art made with computers “artificial” (Nake, 2010, p.40) – as opposed, one might think, to a “natural” art made by human artists. The need for this distinction denotes the uneasy relationship between artists and their tools, the latter supposedly having no agency at all, being mere instruments in the skilled hands of the artist.

The computer introduced an unprecedented level of autonomy: the artist only needed to write a set of instructions, the program did the rest.

Certainly, there had been some room for randomness and uncontrolled processes to emerge in the different artistic practices that had succeeded each other during the 20th century, but until that point creativity was unquestionably anthropocentric, with the artist (or their assistants), at the centre of the creation of every artwork. The computer introduced an unprecedented level of autonomy: the artist only needed to write a set of instructions, the program did the rest. This was challenging for artists at a time when few had seen a computer and even fewer knew how to write a program or understood what it could do.

Vera Molnar. Untitled. Plotter drawing. Ink on paper, 1968. Courtesy DAM Museum

Despite the profound differences from our current perception of computers, over fifty years later, AI still holds the same fascination and is subject to the same misunderstandings as early computer art. The initial rejection of computer-generated art has turned to uncritical enthusiasm, and the prospect of an art that does not need human artists has been celebrated with a spectacular sale at Christie’s. But the artist was never out of the picture. 

Pioneering computer artist Vera Molnar created her first artworks in the 1960s with a “machine imaginaire”, a program for an imaginary computer that helped her develop a series of combinatorial compositions of geometric forms and colours. In 1968, she started working with a real computer (which back then was only available at a research lab), but she has always stressed that the machine is, to her, nothing but a tool: “The computer helps, but it does not ′do′, does not ′design′ or ′invent′ anything” (Molnar, 1990, p.16).

“The computer helps, but it does not ′do′, does not ′design′ or ′invent′ anything”

Vera Molnar

Another pioneer, Frieder Nake, recalls the experience of creating his first algorithmic drawing in 1965, underscoring his role as the creator of the artwork:

“Clearly: I was the artist! A laughable artist, to be sure. […] But an artist insofar as he – like all other artists – decided when an image was finished or whether it was finished at all and not rather to be thrown away. I developed the general software, wrote the specific program, set the parameters for running the program. […] I influenced the process of materialization by choosing the paper, the pens, and the inks; and I finally selected the pieces that were to be destroyed or to leave the studio to be presented to the public.”

Nake, 2020

Manfred Mohr, one of the first artists to work with computers who, like Molnar, had a background in fine arts instead of mathematics, has frequently stated that his artworks transcend the computational process they are based on: “My artistic goal is reached” he states, “when a finished work can visually dissociate itself from its logical content and convincingly stand as an independent abstract entity” (Mohr, 2002). 

Manfred Mohr. P032.Plotter drawing on paper, 38 x 38 cm., 1970. Courtesy DAM Museum

Algorithmic artists have played with the balance between control and randomness, always keeping a direct involvement in every part of the process of creation, from the code to the final output. The software, however, can be allowed a greater portion of the decision making. This is what Harold Cohen did in 1973 when he developed AARON, a computer program designed to generate drawings on its own, with no visual input, based on a complex series of instructions written by the artist.

Influenced by the ideas that were being discussed at Stanford University’s Artificial Intelligence Laboratory at the time, Cohen sought to understand how images were made. AARON aimed to answer that question by creating drawings that simulated those of a human artist, without human intervention. Cohen stressed AARON was “not an artists’ tool” but “a complete and functionally independent entity, capable of generating autonomously an endless succession of different drawings” (Cohen, 1979). This autonomy led to thinking about AARON in cognitive terms, with Cohen himself stating that the program “has a very clear idea of what it is doing” (Cohen and Cohen, 1995, p.3). For over four decades, the artist kept developing the program, establishing a relationship that he described as the kind of collaboration one would have with another human being:

“AARON is teaching me things all the way down the line. From the beginning, it has always been very much a two-way interaction. I have learned things about what I want from AARON that I could never have learned without AARON”

Cohen and Cohen, 1995, p.12

Cohen’s work prefigured the current applications of AI systems in art making, not only in the way the program worked but also in its role as a collaborator rather than a mere tool. 

Harold Cohen. Arnolfini series. Plotter drawing, ink on paper, 1983. Courtesy DAM Museum

Artists working with artificial neural networks nowadays describe their experience in similar terms to those expressed by AARON’s creator. When Anna Ridler created her own dataset of 200 drawings to train a GAN for her animated film Fall of the House of Usher I (2017), she sought to push the boundaries of creativity by producing an artwork that is a machine generated interpretation of her drawings, which in turn represent scenes from a silent film based on a short story by Edgar Allan Poe. The outcome has led her to wonder where is the “real” artwork, and to doubt the role that the program plays in its making: “I do not see a GAN as a tool like I would think of say a photoshop filter but neither would I see it is as true creative partner. I’m not really quite sure what is is” (Ridler, 2018).

For Patrick Tresset, working with robots that can draw in their own style enables him to distance himself from his work: “I found it very difficult to show my work, as a painter, as an emotional thing, and the distance that we have with the action when you use computers, that you are not directly involved… makes it far easier for me to exhibit” (Upton, 2018).

Memo Akten explores the structure and functioning of artificial neural networks and uses Machine Learning as a form of exploring human thinking: “My main interest,” he states, “is in using machines that learn as a reflection on ourselves, and how we navigate our world, how we learn and ‘understand’, and ultimately how we make decisions and take actions” (Akten, 2018).

Gregory Chatonsky criticizes the perception of the artist as purely autonomous and the machine as a simple tool, while describing his creative process as an interaction with the software that not only generates images but also spurs his imagination: “Working with a neural network to produce images or texts,” he states, “I perceive how my imagination develops, becomes disproportionate and germinates in all directions. I try to adapt to this rhythm, to this breath. It’s almost alive” (Chatonsky, 2020).

Artists have carried out a dialogical relationship with the software they have used, considering it not just an instrument, but a collaborator.

These statements show that artists have carried out a dialogical relationship with the software they have used, considering it not just an instrument, but a collaborator. However, the deeply entrenched perception of the artist as the sole creator of the artwork, in full control of every aspect of the outcome, looms over this partnership insisting that either the machine is to remain a mere tool or it is destined to take over the artist’s role.

Anna Ridler. Mosaic Virus. 3-screen GAN video installation. 2018-2019. Courtesy DAM Museum

Towards post-anthropocentric creativity

The question whether a machine can be creative is recurrently asked as AI systems increase their capabilities and become more sophisticated. Recently developed systems such as CAN (Creative Adversarial Network), which is taught to deviate from the examples it has learnt in order to produce new types of images (Elgammal et. al., 2017), or DALL-E, which can generate images from text descriptions (Ramesh et. al., 2021), illustrate how far computers can go in creating visual content.

CAN has even been used in an attempt to pass the Turing Test, that is, to produce machine-generated art that appears indistinguishable from that created by an artist. The results have been disputed in a study that shows a preference for art made by humans and suggests that what should be asked is not if AI can create art, but whether the art created by AI is worthy (Hong and Ming, 2019).

What should be asked is not if AI can create art, but whether the art created by AI is worthy.

Seen from this perspective, the debate pivots to more practical considerations: what can AI do, and how can it be used? GANs are widely employed by artists nowadays, but they tend to generate the same type of images because of the limitations of the programs and the processors. In this sense, the artificial neural networks are not particularly creative because they do not produce anything that breaks out from a set of established parameters and similar outputs. The creativity stems from how artists use these images and assign them a certain narrative. Therefore, to expect machines to become creative by following problem-solving approaches seems limiting and even counterproductive (Esling and Devis, 2020), given that we don’t even understand how creativity works and cannot translate it into computable formulas.  

Instead of asking whether an AI system can replace an artist, it would be more interesting to consider how artists can expand their creativity using AI. This proposition does not imply considering the artist as the sole creator of the artwork, but moves past this preconception to embrace a notion of creativity that includes all the actors involved, human and non-human.

Guido Segni. Demand Full Laziness. Lot 2018/000022. AI-generated image, 2018.

Jan Løhmann Stephensen suggests the terms “postcreativity” or “postanthropocentric creativity” to challenge the idea of creativity as something that is exclusive to humans and a marker of human “greatness” (Løhmann, 2019). Through the lens of postcreativity, we can consider artworks as the outcome of an interaction between a variety of actors, including humans, objects, systems, and environments. In AI-generated art, this means taking into account all the people, animals, natural environments, institutions, communities, software, networks, etc. that take part, more or less directly, more or less willingly, in the artwork’s making.

This opens up deeper reflection on how the piece is created, as do Anna Ridler and Memo Akten in their examination of the artificial neural networks they use. It also allows artists to distance themselves from the specific output while retaining authorship of the process, as do Patrick Tresset and Guido Segni – the latter currently engaged in a five year project titled Demand Full Laziness (2018-2023), in which he outsources his artistic production to a deep learning algorithm trained with images from his moments of rest. Overall, it emphasises the potential of co-creation between humans and machines, in which computers do not mimic, but expand human creativity. 

Through the lens of postcreativity, we can consider artworks as the outcome of an interaction between a variety of actors, including humans, objects, systems, and environments.

Artificial Intelligence has developed at a growing pace over the past seven decades, and it will continue to do so, bringing new challenges and possibilities for computer-generated art. As several authors point out, AI is currently at a stage equivalent to the daguerrotype in photography (Aguera, 2016; Hertzman, 2018), and it is difficult to predict what novel forms of creativity it will unfold. It might well be, if AI were to reach a stage of consciousness or self-volition, that a program may not be interested in producing a drawing or a photograph and would rather express itself through elegant programming code or a beautiful mathematical equation. Or, maybe it would even create art that is not intended for humans to understand, but is addressed to fellow AIs. 

This text was written in March, 2021


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Cetinic, E., and She, J., 2021. Understanding And Creating Art With Ai: Review And Outlook. Cornell University [online] Available at: [Accessed 14 March 2021].

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Elgammal, A., Liu, B., Elhoseiny, M., Mazzone, M., 2017. CAN: Creative Adversarial Networks Generating “Art” by Learning About Styles and Deviating from Style Norms. Cornell University [online] Available at:  [Accessed 14 March 2021].

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Hong, J. and Ming Curran, N., 2019. Artificial Intelligence, Artists, and Art: Attitudes Toward Artwork Produced By Humans vs. Artificial Intelligence. ACM Trans. Multimedia Comput. Commun. Appl., 15(2). Available at: [Accessed 14 March 2021].

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Nake, F., 2010. Roots and randomness –a perspective on the beginnings of digital art. In: W. Lieser, ed., The World of Digital Art. Postdam: h.f. Ullmann, pp.39-41.

–– 2020. Three Drawings and one Story. DAM Museum, [online]. Available at: [Accessed 14 March 2021].

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Fabio Catapano: the beauty of simplicity

Pau Waelder

Fabio Catapano is an Italian digital artist and designer who works with code, CGI, and motion. Encouraged by the possibilities that the NFT market has opened to digital artists, he is developing a growing body of work inspired by Japanese aesthetics and creating generative art that moves away from strict geometry and explores the poetic side of creative coding. On the occasion of his solo artcast A Theory of Color, we had a conversation about his creative process and his views on the future of digital art.

Fabio Catapano. Colorem 221201, 2022

What took you to create your artworks using generative algorithms and how would you describe your creative process?

It was the result of a series of choices. When I was younger, I worked for a long time as a VJ making visuals for clubs and musicians. In that process, you need to create a lot of video content, and I used a software called Quartz Composer, which is pretty much one of the first node-based generative system software programs. Besides my work as a VJ, I have always been passionate about programming languages and I learned some Visual Basic as a hobby. So I had both the interest and the motivation to use this software and explore the creative possibilities of generative algorithms. Since what I did is write the code and then the system would generate the outcome, I found it fascinating to ask myself who is the creator, me or the machine? I feel that we are co-creators, and the software is not just a tool, it is something else.

“I take cues from the way software developers think and collaborate, how they create iterations and updates of the same program.”

The initial idea for an artwork can originate in a shape, the feeling of motion, or a texture, colors, or the combination of two or more elements together. The process in itself is very, very experimental, a form of research in which every outcome is a good outcome. How the project develops is very spontaneous: for instance, I started two years ago with the series Colorem and I wasn’t expecting to create so many pieces. But I ended up creating day after day a different iteration of the same system in a way that felt as a journal of the whole process. I take cues from the way software developers think and collaborate, how they create iterations and updates of the same program. This is why the artwork titles include a reference number that indicates the date of creation and are therefore similar to the versions in a computer program. 

Working in iterations. Diagram by Fabio Catapano.

Every day there is a different outcome and a different exploration, that may be driven by a series of colors, or shapes, or something that I did before. Sometimes I want something that is a bit more grainy, or a bit more clean. But none of those, in my opinion, are the correct answers. They are just moments in time, part of an exploration. That’s pretty much how I started to work with generative art. 

Ideas lead to other ideas. Diagram by Fabio Catapano.

Color plays an important role in your latest series of works. This is an element that is crucial both to designers and visual artists. How do you work with color in the different facets of your professional work? What led you to make it a central part of your artistic research?

It’s funny, because many years ago –I was 17 back then– when I started to create digital art with Photoshop and other programs, it was very colorful. After that, I discovered generative art, and I shifted to black and white. I did so because I was more focused on learning the system and how to create genuine art. So I was more interested in how to create shapes and decided to remove the colors from the equation, and everything became black and white. But then I realized that there was nothing really creative about it. Many other generative artists at that time were creating very geometrical, black and white art that, to me, looks only like a lazy version of a work by Bridget Riley. So I was learning but it felt like I was bringing nothing new to the conversation. 

That’s when I started to shift to colors. I also did so because I wanted to do the opposite of what you expect from computer art, very geometric and strict, with shapes but not colors. I wanted to show that a computer can dream. So I created these shapes that are fluid and can move from one color to another. Also at that time I became interested in the Japanese concept of wabi-sabi, which deals with appreciating the simplicity, imperfection, and mutability of things. I took inspiration from the book WA: The Essence of Japanese Design by Rossella Menegazzo and Stefania Piotti, which shows how Japanese artists such as Takeshi Hara or Koichi Ogawa, among many others, manage to bring such quality in the designs they create. I was also inspired by the Polish artist Wojciech Fangor. I love the way these artists deal with simplicity, structure, and color. 

Japanese inspiration. Images collected by Fabio Catapano.

I also want to show that generative art can be something else, not just the geometrical art that is usually represented by the cyberpunk community. Generative art does not need to be futuristic, it can be something else: it can be white, it can be slow, it can be dreamy… Slowness is also important to my work because nowadays everything goes very fast in our digital lives, social media promotes content that grabs attention in the first three seconds, and I intentionally try to go in the opposite direction, towards a calm and slow contemplation.

“I wanted to do the opposite of what you expect from computer art, very geometric and strict, with shapes but not colors. I wanted to show that a computer can dream.”

While you work with generative algorithms, the outputs of your work are usually still images, videos, and prints. How do you work with these different formats? What makes you choose which will be the final shape of a particular piece?

I have released only one project as a software, Origami, that generated a new output every time it was minted, in a limited edition. This was on (fx)hash, last June. I have never released an artwork as a software that someone can run on the computer, mostly because I find it complicated to explain and distribute. However, I think that, for instance, Colorem as work shouldn’t be a video, it should be software. Because the idea is that it can run there and just constantly change and never be the same. But that’s pretty much true for any generative artwork. So if one day I find a way to distribute those ideas through software, I will be happy to explore further and introduce a new layer of variability and new layer of randomness that is informed by an external factor. I would like the artwork to be detached from me at some point. 

Creating with a computer. Diagram by Fabio Catapano.

In my work I try to think in a more fluid way where I don’t care much about, for instance, the ratio, because ideally with a few clicks I can change the format. And if I work in a print on paper, then I choose a particular moment in the process which to me is interesting, and that can stand on itself as a static artwork. There is also an important process taking place when I create a print, which involves choosing the paper and seeing how the pigments react to the paper, and how the texture of the paper gives a new dimension to the colors. Actually, working with paper inspired me to introduce grainy textures in my digital artworks and try out gray backgrounds, which is something I am still experimenting with.

In this sense, something that is interesting is that artists today can work in a way that artists before couldn’t: today we can use social media as a lab, by posting tests and experiments and getting a response from your audience. To be honest, it is important for me what my followers say, to have that feedback, because I don’t create the artworks to just put them in a drawer, I want them to be seen.   

Another format that I want to work with is projection. As a VJ, I worked a really long time with a projector. And I’m missing right now that in the equation: I have a screen that emits light. I have a paper that receives light. But the projector does something else, it throws light on a surface. That is way more interesting because that again becomes not just an image, it becomes a lighting solution. And the reason why I haven’t tried that yet is because you need the right projector, the right space with the right amount of light, the right attention from the audience, and stuff like that. It’s nothing new, of course, but I would really like to explore that other avenue.

Fabio Catapano. Colorem 221025, 2022

You have been nominated as one of the ten most influential NFT artists in Italy. What has the NFT market brought to your practice, what do you find most interesting in distributing your work in this format?

There is this well-known saying: “beauty is in the eye of the beholder.” I’d say that also value is in the eye of the beholder. What this means is that, after NFTs, even JPEGs have gained value, a value that is supported by a collective agreement and a collective trust. So we decided that the JPEG from now on is not just a JPEG that one can find on the internet, but is a JPEG that can have a $1 value and tomorrow can increase that value to $2 and so on. So, what the NFT market brought me as an artist is a community and a collective trust that turned digital art into something valuable. We know that digital art has existed for many years, and that it has had its value, but suddenly, we have more attention. And it’s a good thing, because there are many projects, many museum shows, and many new things happening. To me it has also meant being able to proudly say: “I’m a digital artist,” and that people can understand what that means.

Value is in the eye of the beholder. Diagram by Fabio Catapano.

On the other hand, the NFT market brought me some revenue and the opportunity to focus on the practice itself. I launched my Genesis with SuperRare. The series was called Data Collector, and it referred to the fact that nowadays collectors are actually collecting data, a bunch of information that moves from one wallet to another. And suddenly this data has value, because we all agreed that it has. So I took these classic statues and made them into particles that move like data moves from one wallet to another. Beyond art, I think that NFTs and blockchain technology will be very important in many more aspects of our lives.

“What the NFT market brought me as an artist is a community and a collective trust that turned digital art into something valuable.”

Having participated in exhibitions in museums, galleries, and also metaverses, what would you highlight in these spaces as the most interesting for the presentation of your work?

I would say that the one space I don’t like is the metaverse as it is designed right now. I see no reason why I need to have a puppet moving in a digital world, watching very low resolution JPEGs. Why do you need a room at all? Additionally, what is being offered now looks like a cheap version of a video game. In fact, I’d say that Fornite and Minecraft are better “metaverses” than most projects I’ve seen.

Then when it comes to galleries, I have to say that most of the people running these spaces don’t know how to display digital art, because they don’t understand the medium. They don’t understand its physicality and the technology behind it. Now everyone wants to jump on this trend, but there are so many things that you need to consider: choosing the screens, the right environment, the lighting, and so forth. Still, I believe this will change and it will get better.

Fabio Catapano. Colorem 221207, 2022

How would you compare your creative process when working with a brand as a designer and when you are creating as part of your own artistic research?

An artist today has to be many things at once: a designer, a photographer, a marketer… There are a lot of things that probably have been there before, but today even more so because the market is more competitive. In my commercial projects, I didn’t actually create the work for them. Rather, the brand bought an artwork I had made and licensed it to use it in their communications and design. It is more and more common that art and design are combined or fused in some contexts. Design is great, but it can be very dry from a storytelling point of view, while art can push those boundaries and can explore new visions.

Fabio Catapano. Colorem Fragments v1, 2022

You have expressed interest in the possibility of displaying digital art on any screen, in a way that can be compared with street art taking over public space. From the perspective of sociology and anthropology, how do you see this presence of digital art evolving in the future?
It is clear to me that we are increasingly surrounded by screens and digital devices. We have quickly switched from having one television set per home to having multiple TVs, smartphones, tablets, and computers. These screens are also closer to us than the television set ever was, and they are not in one room anymore, they move with us and invade every space we inhabit, also the public space. Looking at films like Blade Runner, I see a future with screens everywhere, in which the content will be customized to every user. This can also happen from an artistic point of view, so for instance the content is actually related to the person that is looking at it. Similarly to what is happening now with NFTs, every person is identified by their wallet and carries their art collection with them, wherever they go. With connected screens, we will be able to take our art with us and enjoy it wherever we are.

What We’re Reading Now: Art (x) Design (x) Technology

At Niio, we are passionate about the intersection of Art, Design & Technology. From code-based and algorithmic artworks, to AR & VR installations, to blockchain for authentication, crypto art as well as the .ART domain, talk of digital art was everywhere in ’17.  Check out some of the great stories that we’re reading now and look out for lots more throughout the year.

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Photo via Art Production Fund


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Mat Collishaw: Thresholds at Somerset House Photo: Graham Carlow


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Richard Prince’s “Ripple” paintings share a name with a high-rising cryptocurrency. Credit David Regen/Gladstone Gallery


ARTSY  // 
When Steve Jobs Gave Andy Warhol a Computer Lesson

It was October 9th, 1984, and Steve Jobs was going to a nine-year-old’s birthday party.  He’d been invited just a few hours earlier by journalist David Scheff, who was wrapping up a profile of the Apple Computer wunderkind for Playboy. Jobs was far from the highest-profile guest, however. Walter Cronkite, Andy Warhol, Keith Haring, Louise Nevelson, John Cage, and singer-songwriter Harry Nilsson were also in attendance. And Yoko Ono, of course—it was her son’s birthday, after all.  Read more.
A 1984 Macintosh. Photo via Dave Winer on Flickr.


Is It Big Brother? Is It Art? What If It’s Both? 

The watchers watch us, we watch ourselves, and maybe someone is preparing to feed it all back to us as art.

The creator of Colorimeter is Rafael Lozano-Hemmer, a Mexican-born artist who lives in Montreal.


Rhizome Gets $1M. From Mellon Foundation For Webrecorder, Its Web Preservation Tool 

The New York–based digital arts organization Rhizome has been awarded a two-year $1 million grant from the Andrew W. Mellon Foundation to continue the development of its web preservation tool Webrecorder. The grant, the largest in the institution’s history, follows a previous two-year grant of $600,000 from the Mellon Foundation that it received in December 2015 to put the tool’s development into full gear.  Read more.