Artificial intelligence (AI) has been applied to various methods for digital image creation and editing in Adobe Photoshop 2022. “Inartful” common art and digital photos can be re-imagined using such “neural filters” as style transfer, landscape mixer, color transfer, harmonization, Depth Blur, colorization, and others. The filters can be additive or reductive, or both (of various elements). This session offers walk-throughs of these various Neural Filter features in Adobe Photoshop 2022 (using AI Adobe Sensei). Come explore the “imagination” in a digital image editing software.
Fashioning Text (and Image) Prompts for the CrAIyon Art-Making Generative AIShalin Hai-Jew
CrAIyon (formerly DALL-E after Salvador “Dali”) is a web-facing art-making generative AI tool online (https://www.craiyon.com/) that enables the uses of text (and image) prompts for the creation of watermarked, lightweight visuals. Counterintuitively, the rough visuals are much more usable for recombinations and remixes and recreations into usable digital visuals for various digital learning objects. The textual prompts are not particularly intuitive because of how the generative AI program was trained on mass-scale visuals). There is an art and occasional indirection to working prompts after each try, with the resulting nine-image proof sheets that CrAIyon outputs. The tool can be used iteratively for different outputs.
The tool sometimes turns out serendipitous surprises, including an occasional work so refined that it can be used / shared almost unedited. One challenge in using CrAIyon comes from their request for credit (for all non-subscribers to their service). Another comes from the visual watermarking (orange crayon at the bottom right of the image). However, this tool is quite useful for practical applications if one is willing to engage deep digital image editing (Adobe Photoshop, Adobe Illustrator).
Human-Machine Collaboration: Using art-making AI (CrAIyon) as cited work, o...Shalin Hai-Jew
It is early days for generative art AIs. What are some ways to use these to complement one's work while staying legal (legal-ish)?
Correction: .webp is a raster format
Creating Seeding Visuals to Prompt Art-Making Generative AIsShalin Hai-Jew
Art-making generative AIs have come to the fore. A basic work pipeline typically involves starting with text prompts -> generated images. That image may be used to seed further iterations. Deep Dream Generator (DDG) enables the application of “modifiers” of various types (artist styles, visual adjectives, others) to be applied in addition to the text prompt.
Another approach involves beginning with a “seeding image,” a born-digital or digitized (born-analog) visual on which AI-generated art may be based for a multi-channel and multi-modal prompt. This slideshow provides some observations of how to think about seeding images, particularly in terms of how the DDG handles them, with its “algorithmic pareidolia” (“Deep Dream,” Wikipedia, July 3, 2023).
Human art-making is often about throwing mass-scale conversations. Artists are thought to help bridge humanity into the future. Whether generative AI art enables this or not is still not clear.
Exploring the Deep Dream Generator (an Art-Making Generative AI) Shalin Hai-Jew
The Deep Dream Generator was created by Google engineer Alexander Mordvintsev in 2014. It has a public facing instance at https://deepdreamgenerator.com/, which enables people to use text prompts and image prompts (individually or in combination) to inspire the art-generating generative AI to output images. This work highlights some process-based walk-throughs of the tool, some practical uses, some lightweight art learning, some aspects of the online social community on this platform, and other insights. Some works by the AI prompted by the presenter may be seen here: https://deepdreamgenerator.com/u/sjjalinn.
(This is the first draft of a slideshow that will be used in a conference later in the year.)
Software Engineering Challenges in building AI-based complex systemsIvica Crnkovic
Development of AI-based systems goes far beyond using specific AI-algorithms. The development itself is becoming more complex since data and algorithms become dependent. This presentation lists some of new challenges that AI-developers meet.
Fashioning Text (and Image) Prompts for the CrAIyon Art-Making Generative AIShalin Hai-Jew
CrAIyon (formerly DALL-E after Salvador “Dali”) is a web-facing art-making generative AI tool online (https://www.craiyon.com/) that enables the uses of text (and image) prompts for the creation of watermarked, lightweight visuals. Counterintuitively, the rough visuals are much more usable for recombinations and remixes and recreations into usable digital visuals for various digital learning objects. The textual prompts are not particularly intuitive because of how the generative AI program was trained on mass-scale visuals). There is an art and occasional indirection to working prompts after each try, with the resulting nine-image proof sheets that CrAIyon outputs. The tool can be used iteratively for different outputs.
The tool sometimes turns out serendipitous surprises, including an occasional work so refined that it can be used / shared almost unedited. One challenge in using CrAIyon comes from their request for credit (for all non-subscribers to their service). Another comes from the visual watermarking (orange crayon at the bottom right of the image). However, this tool is quite useful for practical applications if one is willing to engage deep digital image editing (Adobe Photoshop, Adobe Illustrator).
Human-Machine Collaboration: Using art-making AI (CrAIyon) as cited work, o...Shalin Hai-Jew
It is early days for generative art AIs. What are some ways to use these to complement one's work while staying legal (legal-ish)?
Correction: .webp is a raster format
Creating Seeding Visuals to Prompt Art-Making Generative AIsShalin Hai-Jew
Art-making generative AIs have come to the fore. A basic work pipeline typically involves starting with text prompts -> generated images. That image may be used to seed further iterations. Deep Dream Generator (DDG) enables the application of “modifiers” of various types (artist styles, visual adjectives, others) to be applied in addition to the text prompt.
Another approach involves beginning with a “seeding image,” a born-digital or digitized (born-analog) visual on which AI-generated art may be based for a multi-channel and multi-modal prompt. This slideshow provides some observations of how to think about seeding images, particularly in terms of how the DDG handles them, with its “algorithmic pareidolia” (“Deep Dream,” Wikipedia, July 3, 2023).
Human art-making is often about throwing mass-scale conversations. Artists are thought to help bridge humanity into the future. Whether generative AI art enables this or not is still not clear.
Exploring the Deep Dream Generator (an Art-Making Generative AI) Shalin Hai-Jew
The Deep Dream Generator was created by Google engineer Alexander Mordvintsev in 2014. It has a public facing instance at https://deepdreamgenerator.com/, which enables people to use text prompts and image prompts (individually or in combination) to inspire the art-generating generative AI to output images. This work highlights some process-based walk-throughs of the tool, some practical uses, some lightweight art learning, some aspects of the online social community on this platform, and other insights. Some works by the AI prompted by the presenter may be seen here: https://deepdreamgenerator.com/u/sjjalinn.
(This is the first draft of a slideshow that will be used in a conference later in the year.)
Software Engineering Challenges in building AI-based complex systemsIvica Crnkovic
Development of AI-based systems goes far beyond using specific AI-algorithms. The development itself is becoming more complex since data and algorithms become dependent. This presentation lists some of new challenges that AI-developers meet.
UNLEASHING INNOVATION Exploring Generative AI in the Enterprise.pdfHermes Romero
This book presents and exploration of the impact and potential of generative AI in the business landscape. This compelling read takes readers on a journey through the world of generative AI, explaining its fundamental concepts, and showcasing its transformative power when applied in an enterprise setting.
The book delves into the technical aspects of generative AI, explaining its workings in an accessible way. It sheds light on how these models analyze large volumes of data to generate insights, identify trends, conduct sentiment analysis, and extract relevant information from unstructured data.
It also addresses the challenges and considerations when implementing generative AI, including ethical concerns, data privacy, and the need for custom fine-tuning to align with company values and norms. It provides practical guidance on how to overcome these challenges, ensuring a successful AI transformation in the enterprise.
"Unleashing Innovation: Exploring Generative AI in the Enterprise" is a must-read for business leaders, IT professionals, and anyone interested in understanding the revolutionary potential of generative AI in the business world.
Generative AI models, such as ChatGPT and Stable Diffusion, can create new and original content like text, images, video, audio, or other data from simple prompts, as well as handle complex dialogs and reason about problems with or without images. These models are disrupting traditional technologies, from search and content creation to automation and problem solving, and are fundamentally shaping the future user interface to computing devices. Generative AI can apply broadly across industries, providing significant enhancements for utility, productivity, and entertainment. As generative AI adoption grows at record-setting speeds and computing demands increase, on-device and hybrid processing are more important than ever. Just like traditional computing evolved from mainframes to today’s mix of cloud and edge devices, AI processing will be distributed between them for AI to scale and reach its full potential.
In this presentation you’ll learn about:
- Why on-device AI is key
- Full-stack AI optimizations to make on-device AI possible and efficient
- Advanced techniques like quantization, distillation, and speculative decoding
- How generative AI models can be run on device and examples of some running now
- Qualcomm Technologies’ role in scaling on-device generative AI
CUbRIK tutorial at ICWE 2013: part 1 Introduction to Human ComputationCUbRIK Project
2013, July 8
Part 1 of the tutorial illustrated at ICWE 2013, by Alessandro Bozzon (Delft University of Technology)
Crowdsourcing and human computation are novel disciplines that enable the design of computation processes that include humans as actors for task execution. In such a context, Games With a Purpose are an effective mean to channel, in a constructive manner, the human brainpower required to perform tasks that computers are unable to perform, through computer games. This tutorial introduces the core research questions in human computation, with a specific focus on the techniques required to manage structured and unstructured data. The second half of the tutorial delves into the field of game design for serious task, with an emphasis on games for human computation purposes. Our goal is to provide participants with a wide, yet complete overview of the research landscape; we aim at giving practitioners a solid understanding of the best practices in designing and running human computation tasks, while providing academics with solid references and, possibly, promising ideas for their future research activities.
Enterprise integration: The Past, Present and FutureWSO2
If you take the last 10 years, many things have changed. The next 10 years will be no different. However, some key concepts such as integration are very unlikely to change. But, interestingly, this does not mean that you can build a system that lasts for decades, since the integration you had 10 years ago is not the kind of integration you have today and never will be the one you want to have in 10 years time.
While working with customers of varied levels of maturity there is a lot we have learned as a vendor. In this talk we will discuss how some companies managed to keep carrying their legacy baggage for decades while others were able to keep iterating to a level where they never found themselves outdated. But, just like technology, there is also an impact on the business. The more you innovate the more you may end up spending and similarly, the less you innovate the more you’d be spending again. There is a fine line between innovation and stagnation where you spend the least and gain the most. This is the perfect line of iteration that any business wants to be aligned with.
There is a lot to learn from others successes and failures and this talk is mostly focusing on that. We’ll take many case-studies to learn the past, present and future of enterprise integration and how to find the fine line of iterative improvement that best suits the kind of business you are in.
ART AS A VEHICLE TO UNDERSTAND LAND USE PLANNING AND SUSTAINABILITY PRESENTATION AT THE UNIVERSITY OF SOUTHERN CALIFORNIA, SCHOOL OF POLICY, PLANNING , AND DEVELOPMENT, URBAN GROWTH SEMINAR, APRIL 7, 2009
UNLEASHING INNOVATION Exploring Generative AI in the Enterprise.pdfHermes Romero
This book presents and exploration of the impact and potential of generative AI in the business landscape. This compelling read takes readers on a journey through the world of generative AI, explaining its fundamental concepts, and showcasing its transformative power when applied in an enterprise setting.
The book delves into the technical aspects of generative AI, explaining its workings in an accessible way. It sheds light on how these models analyze large volumes of data to generate insights, identify trends, conduct sentiment analysis, and extract relevant information from unstructured data.
It also addresses the challenges and considerations when implementing generative AI, including ethical concerns, data privacy, and the need for custom fine-tuning to align with company values and norms. It provides practical guidance on how to overcome these challenges, ensuring a successful AI transformation in the enterprise.
"Unleashing Innovation: Exploring Generative AI in the Enterprise" is a must-read for business leaders, IT professionals, and anyone interested in understanding the revolutionary potential of generative AI in the business world.
Generative AI models, such as ChatGPT and Stable Diffusion, can create new and original content like text, images, video, audio, or other data from simple prompts, as well as handle complex dialogs and reason about problems with or without images. These models are disrupting traditional technologies, from search and content creation to automation and problem solving, and are fundamentally shaping the future user interface to computing devices. Generative AI can apply broadly across industries, providing significant enhancements for utility, productivity, and entertainment. As generative AI adoption grows at record-setting speeds and computing demands increase, on-device and hybrid processing are more important than ever. Just like traditional computing evolved from mainframes to today’s mix of cloud and edge devices, AI processing will be distributed between them for AI to scale and reach its full potential.
In this presentation you’ll learn about:
- Why on-device AI is key
- Full-stack AI optimizations to make on-device AI possible and efficient
- Advanced techniques like quantization, distillation, and speculative decoding
- How generative AI models can be run on device and examples of some running now
- Qualcomm Technologies’ role in scaling on-device generative AI
CUbRIK tutorial at ICWE 2013: part 1 Introduction to Human ComputationCUbRIK Project
2013, July 8
Part 1 of the tutorial illustrated at ICWE 2013, by Alessandro Bozzon (Delft University of Technology)
Crowdsourcing and human computation are novel disciplines that enable the design of computation processes that include humans as actors for task execution. In such a context, Games With a Purpose are an effective mean to channel, in a constructive manner, the human brainpower required to perform tasks that computers are unable to perform, through computer games. This tutorial introduces the core research questions in human computation, with a specific focus on the techniques required to manage structured and unstructured data. The second half of the tutorial delves into the field of game design for serious task, with an emphasis on games for human computation purposes. Our goal is to provide participants with a wide, yet complete overview of the research landscape; we aim at giving practitioners a solid understanding of the best practices in designing and running human computation tasks, while providing academics with solid references and, possibly, promising ideas for their future research activities.
Enterprise integration: The Past, Present and FutureWSO2
If you take the last 10 years, many things have changed. The next 10 years will be no different. However, some key concepts such as integration are very unlikely to change. But, interestingly, this does not mean that you can build a system that lasts for decades, since the integration you had 10 years ago is not the kind of integration you have today and never will be the one you want to have in 10 years time.
While working with customers of varied levels of maturity there is a lot we have learned as a vendor. In this talk we will discuss how some companies managed to keep carrying their legacy baggage for decades while others were able to keep iterating to a level where they never found themselves outdated. But, just like technology, there is also an impact on the business. The more you innovate the more you may end up spending and similarly, the less you innovate the more you’d be spending again. There is a fine line between innovation and stagnation where you spend the least and gain the most. This is the perfect line of iteration that any business wants to be aligned with.
There is a lot to learn from others successes and failures and this talk is mostly focusing on that. We’ll take many case-studies to learn the past, present and future of enterprise integration and how to find the fine line of iterative improvement that best suits the kind of business you are in.
ART AS A VEHICLE TO UNDERSTAND LAND USE PLANNING AND SUSTAINABILITY PRESENTATION AT THE UNIVERSITY OF SOUTHERN CALIFORNIA, SCHOOL OF POLICY, PLANNING , AND DEVELOPMENT, URBAN GROWTH SEMINAR, APRIL 7, 2009
Presentatie van Elizabeth Currid bij Creative Cities Amsterdam Area (CCAA). Haar boek The Warhol Economy omvat een onderzoek naar de schijnbaar toevallige samenloop van omstandigheden in de creatieve industrie in New York die tot briljante samenwerking leidde (zoals Stephen Sprouse voor Louis Vuitton).
Similar to Co-Creating Common Art with AI Tools in Adobe Photoshop 2022 (8)
Long nonfiction chapters are not in-style and may never have been. Where average chapter lengths of nonfiction book chapters are about 4,000 – 7,000 words in length, some may be several times that max range number. The explanation is that there is some irreducible complexity that that chapter addresses that cannot be addressed in shorter form. This slideshow explores some methods for writing longer chapters while still maintaining coherence, focus, and reader interest…and while using some technological tools to write and edit more efficiently.
Overcoming Reluctance to Pursuing Grant Funds in AcademiaShalin Hai-Jew
Starting as an organization’s new grant writer can be a challenge, especially in a case where there has been a time lapse since the last one left. People get out of the habit of pursuing grant funds. This slideshow addresses some of the reasons for such reluctance and proposes some ways to mitigate these.
Writing grants is one common way that those in institutions of higher education may acquire some funds—small and big, one-off and continuing—to conduct research, hire faculty and researchers and learners and others, update equipment, update or build up new buildings, and achieve other work. This slideshow explores some aspects of the work of grant writing in the present moment in higher education.
Contrasting My Beginner Folk Art vs. Machine Co-Created Folk Art with an Art-...Shalin Hai-Jew
The SARS-CoV-2 pandemic inspired several years of experimentation with common or folk art, involving mixed media, alcohol ink painting, and other explorations. Then, with the emergence of art-making generative AIs, there were further experiments, particularly with one that enables generation of visuals from scanned art and photos, text prompts, style overlays, and text-based visual modifiers. While both types of artmaking are emotionally satisfying and helpful for stress management, there are some contrasting differences. This exploratory slideshow explores some of these differences in order to partially shed light on the informal usage of an art-making generative AI (artificial intelligence).
Common Neophyte Academic Book Manuscript Reviewer MistakesShalin Hai-Jew
The work of academic book reviewing, as a volunteer (most often), is a common academic practice. The presenter has served as a neophyte one for some years before settling into this invited volunteer work for several decades. There have been lessons learned over time about avoidable mistakes…from both experience and observation.
Augmented Reality in Multi-Dimensionality: Design for Space, Motion, Multiple...Shalin Hai-Jew
Augmented reality (AR)—the use of digital overlays over physical space—manifests in a wide range of spaces (indoor, outdoor; virtual) and ways (in real space (with unaided human vision); in head gear; in smart glasses; on mobile devices, and others). There are various authoring technologies that enable the making of AR experiences for various users. This work uses a particular tool (Adobe Aero®) to explore ways to build AR for multiple dimensions, including the fourth dimension (motion, changes over time).
Based on the respective purposes of the AR experience, some basic heuristics are captured for
space design (1),
motion design (2),
multiple perception design (sight, smell, taste, sound, touch) (3),
and virtual- and tangible- interactivity (4).
Some Ways to Conduct SoTL Research in Augmented Reality (AR) for Teaching and...Shalin Hai-Jew
One of the extant questions about augmented reality (AR) is how (in)effective it is for the teaching and learning in various formal, nonformal, and informal contexts. The research literature shows mixed findings, which are often highly context-based (and not generalizable). There are some non-trivial costs to the design/development/deployment of AR for teaching and learning. For the users, there is cognitive load on the working memory [(1) extraneous/poor design, (2) intrinsic/inherent difficulty in topic, and (3) germane/forming schemas]. For teachers, there are additional knowledge, skills, and abilities / attitudes (KSAs) that need to be brought to bear.
Augmented Reality for Learning and AccessibilityShalin Hai-Jew
Recently, the presenter conducted a systematic review of the academic literature and an environmental scan to learn how to set up an augmented reality (AR) shop at an institution of higher education. The ambition was to not only set up AR in an accessible and legal way but also be able to test for potential +/- effects of AR on teaching and learning. The research did not go past the review stage, because of a lack of funding, but some insights about accessibility in AR were acquired.
(The visuals are from Deep Dream Generator and CrAIyon.)
Engaging Pixabay as an open-source contributor to hone digital image editing,...Shalin Hai-Jew
This slideshow describes the author's early experiences with creating two accounts on Pixabay in order to advance digital editing skills in multimedia. The two accounts are located at https://pixabay.com/users/sjjalinn-28605710/ and https://pixabay.com/users/wavegenerics-29440244/ ...
This work explores four main spaces where researchers publish about educational technology: academic-commercial, open-access, open-source, and self-publishing.
Getting Started with Augmented Reality (AR) in Online Teaching and Learning i...Shalin Hai-Jew
University creative shops are exploring whether they can get into the game of producing AR-enhanced experiences: campus tours, interactive gaming, virtual laboratories, exploratory art spaces, simulations, design labs, online / offline / blended teaching and learning modules, and other AR applications.
This work offers a basic environmental scan of the AR space for online teaching and learning, and it includes pedagogical design leads from the current research, technological knowhow, hands-on design / development / deployment of learning objects, and online teaching and learning methods.
Co-Creating Common Art with the CrAIyon AIShalin Hai-Jew
This slideshow contains a variety of images created using the CrAIyon AI...based on seeding terms. This work asks questions about common art in an age of AI.
This is the revised intro to Adobe Animate set of notes used in a training in late June 2022. The Word version is downloadable from www.k-state.edu/ID/AdobeAnimateHandout.docx, with the motion available from the animated .gifs.
"Drift" is the latest in the alcohol ink drip playing series. After reaching the first learning plateau a year and a half in, I am finding second wind. This is all still fun.
100% “Tier 0” in a Year? Supporting Graduate Students’ ETDRs w/ DocumentationShalin Hai-Jew
Video: https://vimeo.com/716175153
What I.T. challenge involves novel research, data, sensitive information, and global reputations? Complex Microsoft Word templates? LaTeX templates? Evolving technologies? Dozens of source citation methods? Local domain-based conventions? Professorial quirks? Multiple web-facing databases? Hard deadlines that can be costly if missed?
Electronic theses, dissertations, and reports, better known as ETDRs!
This presentation describes a real-world context in which a core staff retirement (and the role’s non-replacement) resulted in the need for fast learning of the ETDR space and an effort to enable graduate student work with thorough documentation, updated templates, and web conferences, in the backdrop of the pandemic. The solution here is only partial, and the challenge is still being worked, but some objective progress may be seen.
Mapping Narrative Structures w/ Computational Text Analysis (LIWC-22)Shalin Hai-Jew
A classic narrative (storytelling) structure begins at a start point, builds tension, reaches a point of climax, and then achieves resolution. This structure is found in many texts, written and spoken. LIWC-22 (pronounced “luke”) enables a computational analysis of various texts for various indicators of narrative structure, specifically, staging, plot progression, and cognitive (psychological) tensions. Come see how this tool is applied to various texts and how the resulting information may be used for research and analysis.
This document announces the winners of the 2024 Youth Poster Contest organized by MATFORCE. It lists the grand prize and age category winners for grades K-6, 7-12, and individual age groups from 5 years old to 18 years old.
Hadj Ounis's most notable work is his sculpture titled "Metamorphosis." This piece showcases Ounis's mastery of form and texture, as he seamlessly combines metal and wood to create a dynamic and visually striking composition. The juxtaposition of the two materials creates a sense of tension and harmony, inviting viewers to contemplate the relationship between nature and industry.
Fashionista Chic Couture Maze & Coloring Adventures is a coloring and activity book filled with many maze games and coloring activities designed to delight and engage young fashion enthusiasts. Each page offers a unique blend of fashion-themed mazes and stylish illustrations to color, inspiring creativity and problem-solving skills in children.
Boudoir photography, a genre that captures intimate and sensual images of individuals, has experienced significant transformation over the years, particularly in New York City (NYC). Known for its diversity and vibrant arts scene, NYC has been a hub for the evolution of various art forms, including boudoir photography. This article delves into the historical background, cultural significance, technological advancements, and the contemporary landscape of boudoir photography in NYC.
Co-Creating Common Art with AI Tools in Adobe Photoshop 2022
1. CO-CREATING COMMON ART
WITH AI TOOLS
IN ADOBE PHOTOSHOP 2022:
STYLE TRANSFER, LANDSCAPE MIXER, COLOR TRANSFER, HARMONIZATION, DEPTH BLUR, AND COLORIZATION
SHALIN HAI-JEW
SIDLIT 2022
JULY 27 – 29, 2022
2. PRESENTATION OVERVIEW
• ARTIFICIAL INTELLIGENCE (AI) HAS BEEN APPLIED TO VARIOUS METHODS FOR DIGITAL IMAGE
CREATION AND EDITING IN ADOBE PHOTOSHOP 2022. “INARTFUL” COMMON ART AND
DIGITAL PHOTOS CAN BE RE-IMAGINED USING SUCH “NEURAL FILTERS” AS STYLE TRANSFER,
LANDSCAPE MIXER, COLOR TRANSFER, HARMONIZATION, DEPTH BLUR, COLORIZATION, AND
OTHERS. THE FILTERS CAN BE ADDITIVE OR REDUCTIVE, OR BOTH (OF VARIOUS ELEMENTS).
THIS SESSION OFFERS WALK-THROUGHS OF THESE VARIOUS NEURAL FILTER FEATURES IN
ADOBE PHOTOSHOP 2022 (USING AI ADOBE SENSEI). COME EXPLORE THE “IMAGINATION” IN
A DIGITAL IMAGE EDITING SOFTWARE.
2
4. “COMMON ART”
• “COMMON ART” IS ART CREATED BY COMMON FOLK, A NON-PROFESSIONAL, AN AMATEUR,
OFTEN WITH LITTLE TO NO FORMAL TRAINING IN THE CREATING OF ART. COMMON ART IS A
PRACTICE FROM ANTIQUITY THROUGH THE PRESENT. THE MAKING OF COMMON ART IS FOR
PERSONAL EXPRESSION, CRAFTING, STRESS RELIEF, AND ENJOYMENT, AMONG OTHERS.
• “ART” IS USED IN A VERY GENERAL SENSE HERE.
4
6. ANNS
AN ARTIFICIAL NEURAL NETWORK WITH
HIDDEN LAYERS
CAN HAVE MULTIPLE INPUT AND OUTPUT
NODES (ALBEIT NOT DIRECTLY DEPICTED HERE)
ARTIFICIAL NEURONS THAT PROCESS INPUTS
AND OUTPUTS THROUGH VARIOUS LAYERS TO
PARTICULAR DESIGNED OUTPUTS
CREATED USING AI
SOME MYSTERY IN THE HIDDEN LAYERS IN
TERMS OF EXACT PROCESSING
6
9. NEURAL FILTERS
• INTERACTS WITH THE SEEDING IMAGE (INPUT)
• SEEDING IMAGE GOES THROUGH VARIOUS PROCESSES
• OUTPUT IS A NEW IMAGE, WHICH HAS NEVER EXISTED BEFORE
9
11. EXPLORING THE SOURCE IMAGES
FOR THE STYLE TRANSFERS
• STYLE TRANSFERS ARE SEEDED WITH WORKS FROM THE 19TH, 20TH, AND 21ST CENTURY, FROM
MOSTLY WESTERN BUT SOME EASTERN TRADITIONS.
• SOME OF THE RECOGNIZABLE WORKS SEEM TO BE IN THE PUBLIC DOMAIN.
• ANYWAY, WHAT IS EXTRACTED FROM THEM IS AN ANN. STILL, THE LAW ON WHETHER SUCH
EXTRACTIONS IS SETTLED IS UNCLEAR.
• A NUMBER OF THE OTHER WORKS ARE MORE MODERNIST AND PERHAPS SOMEWHAT
DERIVATIVE OF STOCK IMAGES AND PHOTOS AND WERE NOT EASILY IDENTIFIABLE. THESE DID
HAVE COLOR PALETTES AND TRANSFERRABLE ELEMENTS FOR THE NEURAL FILTERS, THOUGH.
11
12. EXPLORING THE SOURCE IMAGES
FOR THE STYLE TRANSFERS (CONT.)
• THE VISUALS WERE BACKTRACKED FROM IN-TOOL THUMBNAILS THROUGH A REVERSE SEARCH
ENGINE (TINEYE) TO A WORK…AND THE DATA WAS VERIFIED THROUGH ADDITIONAL FOLLOW-ON
SEARCHES. FOR SOME, A SEARCH OF SOME 54 BILLION IMAGES ON TINEYE DID NOT RESULT IN
ANY FINDINGS.
• THOSE OUTSIDE OF COPYRIGHT PROTECTIONS HAVE VERSIONS THAT HAVE BEEN CREATED.
• FINDING NAMES AND TITLES AND DATES WERE NOT ALWAYS STRAIGHTFORWARD, GIVEN DERIVATIONS,
GIVEN DILUTION OF THE ORIGINAL WORKS. SOME RELATED TEXT WERE GENERIC TEXTUAL DESCRIPTORS.
• SOME WHO ARE VERSED IN ART HISTORY WOULD APPRECIATE THE TIE-INS AND ADDITIONAL
ARTFUL EVOCATIONS. IN ANOTHER SENSE, THE CO-CREATED WORKS ARE UNMOORED FROM BOTH
ORIGINATING “PARENTS.”
12
14. ARTIST STYLE: STYLE TRANSFERS
• FOR ARTIST STYLE, SOME OF THE SEEDING WORKS INCLUDE THE FOLLOWING:
• VINCENT VAN GOGH’S “SELF-PORTRAIT (1889); “WHEAT FIELD WITH CYPRESSES” (1889)
• KATSUSHIKA HOKUSAI’S “RAINSTORM BENEATH THE SUMMIT” (1820 – 1823)
• PAUL GAUGUIN’S “HAIL MARY” (1891), FROM THE TAHITIAN WOMEN SERIES; “THE LEMON PICKER” (1891)
• EDVARD MUNCH’S “THE ANXIETY” (1894)
14
15. ARTIST STYLE: STYLE TRANSFERS (CONT.)
• PAUL CEZANNE’S “HOUSES ALONG A ROAD” (1881)
• BERTHE MORISOT’S “WOMAN PICKING FLOWERS” (1879)
• GEORGES SEURAT’S “CADET FROM SAINT CYR” (1884)
• CLAUDE MONET’S “WATER LILIES” (1917)
• MAYNARD DIXON’S “WILD HORSES OF NEVADA” (1927)
15
17. IMAGE STYLE: STYLE TRANSFERS
• FOR IMAGE STYLE, SOME OF THE SEEDING WORKS INCLUDE THE FOLLOWING:
• KATSUSHIKA HOKUSAI’S “UNDER THE WAVE OFF KANAGAWA” / “THE GREAT WAVE” (1831)
• FRANCIS PICABIA’S “EDTAONIST” (OR “ECCLESIASTIC”) (1913); “UDNIE” (1913)
• VINCENT VAN GOGH’S “SELF-PORTRAIT WITH A STRAW HAT” (1887); “THE NIGHT CAFÉ” (1888);
“STARRY NIGHT” (1889); “OLIVE TREES” (1889); “HOUSE AND FIGURE” (1890)
• EDVARD MUNCH’S “THE SCREAM” (1893)
• PAUL GAUGUIN’S “THREE TAHITIAN WOMEN” (1896)
17
18. IMAGE STYLE: STYLE TRANSFERS (CONT.)
• MANY WORKS WERE NOT MATCHED TO ANYTHING ONLINE. SOME BROUGHT UP STOCK IMAGES.
SOME BROUGHT UP PHOTOS. (SOME OF THE WORKS MAY BE RECOLORED OR ARTFUL OVERLAYS
ON EXISTING PHOTOS.)
• THERE WAS A DICHOTOMY BETWEEN THE OLDER FORMALIST ARTWORKS AND THE MORE MODERNIST-
DIGITAL “FILLER” WORKS.
• TWO OF MY ABSOLUTE FAVORITE NEURAL FILTERS WERE FILTERS…THAT I COULD NOT IDENTIFY TO AN
ARTIST NAME OR TITLE OR DATE. I LIKE ONE OF THE ORIGINAL WORKS BUT MAYBE NOT THE OTHER AS
MUCH. IT’S JUST THE ABSTRACTION FROM THAT LATTER WORK THAT WORKS WELL FOR ME. (SO MUCH
OF THIS IS SUBJECTIVE.)
• I DID TRY TO FIND DOCUMENTATION ON THE VARIOUS WORKS, BUT I CAME UP SHORT.
18
19. ABOUT STYLE TRANSFERS FROM
INDIVIDUAL ART WORKS
• IT HELPS TO THINK OF EACH ARTWORK USED IN THE ARTIFICIAL NEURAL NETWORK (ANN) FOR
STYLE AS A FEATURE SET OF LINES, COLORS, TEXTURES, AND SO ON. THIS FEATURE SET MAY BE
PERCEIVED AS A KIND OF VISUAL GIST.
• IT HELPS TO THINK OF THE WORKS AS BEING ABSTRACTED OUT INTO SEPARATE APPLIED STYLES.
• YOU’RE NOT LOOKING AT THE ARTWORK IN FRONT OF YOU, BUT YOU’RE LOOKING AT ITS FEATURES.
• IT HELPS TO THINK OF MYSTERY, WITH THE ANN’S HIDDEN LAYERS SELECTING VISUAL FEATURES
AND CREATING RULES FOR WHEN TO APPLY THOSE FEATURES TO THE SEEDING WORK (AND BASED
ON THE PARAMETER SETTINGS).
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20. FORMALIST SCHOOLS OF ART STYLES
• MANY WORKS SEEM TO BE OF THE IMPRESSIONIST SCHOOL.
• MORE MODERN WORKS SEEM TO BE OF THE REALIST SCHOOL, GENERALLY SPEAKING. THERE
IS ALSO SOME DADA.
20
21. STYLE TRANSFER - CUSTOM
• CAN “STYLE TRANSFER” FROM AN ORIGINAL OR
“CUSTOM” IMAGE
• THE ELEMENTS OF STYLE INCLUDE ASPECTS OF THE
ORIGINAL SOURCE IMAGE (THAT INFORMS THE STYLE):
COLOR, LINE, TEXTURE (LESS COMPOSITION)
• WORKS LIKE A MELD WHERE THE SEEDING IMAGE ALSO
INFORMS THE NEW VISUAL
21
22. LANDSCAPE MIXER
THIS NEURAL FILTER ENABLES THE DRAWING OF
VARIOUS LANDSCAPE SCENES (LANDFORMS),
OFTEN WITH DIFFERENT SEASONS AND TIMES
OF DAY (IN TERMS OF LIGHTING).
THIS FILTER DEMONSTRATES THE POWERFUL
NUANCES AVAILABLE WITH ANN PROCESSING.
THE VARIOUS NEURAL FILTERS IN THE
LANDSCAPE MIXER SET VARY IN TERMS OF THE
ACTUAL “CREATION” OF THE ILLUSION OF
VARIOUS LANDFORMS.
22
23. COLOR TRANSFER
THE COLOR TRANSFER NEURAL FILTER RECOLORS THE EXISTING
COLORS IN A SEEDING IMAGE.
THE MORE VARIANCE IN ORIGINAL COLORS, THE MORE MULTI-
COLOR COLOR TRANSFER NEURAL FILTERS CAN APPLY.
THERE ARE SOME MONOCHROMATIC OR BI- OR TRI-CHROMATIC
COLOR TRANSFER NEURAL FILTERS. (SOME “COLOR” TRANSFERS
CAN INVOLVE THE REMOVAL OF COLORS AND GOING TO DARKS
AND LIGHTS, VARYING VALUES.)
23
25. DEPTH BLUR
ENABLES THE DEFINING OF THE FOCAL
DISTANCE
ENABLES CLICKING ON A PART OF AN
IMAGE FOR OBJECT FOCUS (AND
EVERYTHING “BEHIND” CAN BE BLURRED
AND / OR HAZED VISUALLY)
25
26. COLORIZATION
THE COLORIZATION NEURAL FILTER INVOLVES
THE AI ADDING OF COLOR TO A GRAYSCALE
(MIDTONES) VISUAL.
THE COLORS ADDED ARE NOT MERELY
COMMUNICATING POTENTIAL COLORS IN A
REAL-WORLD CONTEXT…BUT APPARENTLY ADD
SOMETHING OF DRAMA TO THE COLORING
TO CATCH THE EYES. [A COLOR IMAGE THAT IS
GRAYSCALED AND THEN HAS “COLORIZATION”
APPLIED DOES NOT RETURN TO ITS ORIGINAL
COLOR STATE…WHICH IS TECHNICALLY
UNKNOWABLE FROM THE GRAY. RATHER, THE
NEW COLORIZED IMAGE IS A NEW VERSION.]
26
27. COLORIZATION PROFILES
• RETRO HIGH CONTRAST
• RETRO BLUE BROWN
• RETRO LIGHT YELLOW
• RETRO PURPLE YELLOW
• RETRO BRIGHT
• RETRO RED
• RETRO GREEN
• RETRO FADED
• RETRO DENIM
• RETRO DARK
• RETRO BROWN
27
28. ELUSIVE VISUAL NUANCES
• THERE ARE ELUSIVE VISUAL NUANCES TO VARIOUS STYLES
AND NEURAL FILTERS.
• PERHAPS THERE IS SOMETHING EVOKED OF MOOD.
• PERHAPS THERE ARE ASPECTS OF A WORK THAT CONVEY
“RETRO.”
• PERHAPS VARIOUS ASPECTS OF THE STYLE ARE
HIGHLIGHTED OR EMPHASIZED OR DISAPPEARED BASED
ON THE PARAMETER SETTINGS…AND BASED ON THE
UNDERLYING SEEDING IMAGE.
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29. NEURAL FILTERS FOR CO-CREATED ART
HUMAN
• THE HUMAN DECIDES WHAT IMAGES TO USE AS SEEDING
VISUALS. THESE CAN BE BORN-ANALOG OR BORN-
DIGITAL…OR SOME COMBINATION.
• THE PROCESS IS SEQUENCE-SENSITIVE.
• THERE CAN BE PRE-PROCESSING; THERE CAN BE POST-
PROCESSING.
• HEIGHTEN THE SPATIAL RESOLUTION BEFORE THE NEURAL
FILTERING.
• INTEGRATE VARIOUS LAYERS FOR DIFFERENT VISUAL EFFECTS.
• ENGAGE LIGHT COMPOSITING.
COMPUTER
• THE NEURAL FILTERS ARE SOMEWHAT
PREDICTABLE AFTER SOME EXPERIENCES
WITH EACH ONE AND AFTER SOME
SAMPLING.
29
31. NEURAL FILTERS FOR CO-CREATED ART (CONT.)
HUMAN
• THE HUMAN DECIDES (A LITTLE INDIRECTLY)
WHAT FEATURES TO SHOWCASE IN THE
NEURAL FILTERED IMAGE.
COMPUTER
• THE COMPUTER OFFERS PREVIEWS OF THE
VARIOUS APPLIED FILTERS.
• THE SYSTEM DOES NOT FORCE A “COMMIT.” IT
OFFERS THE OVERLAY TENTATIVELY AND FAIRLY
ACCURATELY.
• IT HELPS IF THE PERSON CAN ENGAGE VISUAL
THINKING AND ANTICIPATING LOOKS-AND-
FEELS. MUCH OF THIS IS MECHANISTIC.
31
32. NEURAL FILTERS FOR CO-CREATED ART (CONT.)
HUMAN
• THERE IS THE SELECTION OF THE NEURAL FILTER
(OR FILTERS IN SEQUENCE).
• THERE ARE THE SLIDER / ADJUSTMENTS TO THE
NEURAL FILTER(S).
• THERE IS THE SELECTION OF DIGITAL IMAGE
EDITS OUTSIDE OF THE NEURAL FILTERS.
• THE FILTER GALLERY ENABLES A WIDE RANGE
OF OTHER VISUAL EFFECTS.
COMPUTER
• THE SOFTWARE PROGRAM ENABLES A WIDE
RANGE OF OTHER EDITS AND EFFECTS.
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33. NEURAL FILTERS FOR CO-CREATED ART (CONT.)
HUMAN
• THERE IS THE USE OF LAYERS AND MASKING
AND TRANSPARENCIES FOR DIFFERENT VISUAL
EFFECTS.
• THE HUMAN DECIDES WHAT IS AESTHETICALLY
PLEASING.
• IT IS OKAY TO HAVE “UGLY” LIMINAL STAGES
AS LONG AS THE FINAL VISUAL WORKS.
COMPUTER
• THE SOFTWARE CREATES A SENSE OF
VISUAL COHESION WITH THE NEURAL
FILTERS.
33
34. NEURAL FILTERS FOR CO-CREATED ART (CONT.)
HUMAN
• THE HUMAN CAN NARRATE A STORY
AROUND A CO-CREATED VISUAL ART.
• THE STORY MAY BE EXPERIENTIAL.
• ALL VISUAL ARTWORKS HAVE ASPECTS THAT
CANNOT BE VERBALLY OR TEXTUALLY
DESCRIBED. THERE IS A LEVEL OF
“INCHOATE” IN THE ARTWORKS.
COMPUTER
• THE COMPUTER CAN CREATE A NUMBER OF
VARIATIONS FROM ONE SEED.
• IT IS WHOLLY POSSIBLE TO FORGET THE
SEQUENCED STEPS TO CREATE A VISUAL.
• IT HELPS TO SAVE AN EDITABLE VERSION OF
THE FILE INSTEAD OF JUST THE FIXED DIGITAL
IMAGE AT THE END, IF THERE ARE OTHER
NEURAL FILTERS AND EDITS THAT ARE TO BE
TRIED.
34
35. WHAT THE SEEDING IMAGE BRINGS?
• THE SEEDING IMAGE, DEPENDING ON THE NEURAL FILTER, CAN BRING ANY OR ALL OF THE
FOLLOWING TO THE COLLABORATION:
• COMPOSITION
• LINES
• COLORS
• TEXTURES
• BRIGHTNESS / DARKNESS
• AND OTHER COMBINATIONS
35
36. WHAT THE SEEDING IMAGE BRINGS? (CONT.)
• WHATEVER THE SEEDING IMAGE BRINGS, THE NEURAL FILTERS WILL COVER OVER OR MUTE
SOME ASPECTS; THEY WILL HIGHLIGHT OR EMPHASIZE OTHER ASPECTS.
• THE NEURAL FILTERS DO NOT COLOR OVER ALPHA CHANNELS. THEY COLOR OVER
TRANSPARENCY GRADIENTS POORLY OR VERY LIGHTLY.
• NEURAL FILTERS TEND TO DO BETTER WITH COLORS.
• COLOR HALFTONES MAINTAIN THAT SPOTTED PATTERN.
36
38. DIGITAL IMAGE PREPARATION AND POST-NEURAL
FILTER PREPARATION
PRE-PROCESSING
• SAVE RAW PHOTOS AS COMMON DIGITAL
IMAGE FORMAT TYPES.
• JUMP THE RESOLUTION ON THE VISUALS.
• CONDUCT DIGITAL IMAGE EDITS (CONTRAST,
LINES, OTHERS) FOR PARTICULAR ANTICIPATED
NEURAL NETWORK EFFECTS.
• ENGAGE LIGHT COMPOSITING.
POST-PROCESSING
• SAVE OUT THE NEURAL-FILTERED IMAGE.
• MAKE ANY ADDITIONAL DIGITAL IMAGE
EDITS.
• SLICE. FLIP. CRUMPLE. FOLD. COLOR.
DRAW OVER. DRAW UNDER.
38
39. WHAT APPEALS TO ME VISUALLY?
• FOR THE LONGEST TIME, I HAVE LIKE “STRANGE” VISUAL EFFECTS. I LIKE STRANGE SURPRISES.
• OVER TIME, I HAVE LEARNED TO LIKE USING SLIDERS AND MORE NUANCED APPROACHES.
• IT HELPS TO VERSION ALONG THE WAY, SO THAT THERE ARE INTERMEDIATE VISUALS THAT MAY
BE USED ALONG DIFFERENT BRANCHES AND PATHS.
• IT HELPS TO CONSUME OTHERS’ WORKS, SO THAT ONE CAN LEARN FROM HOW OTHERS
PROCEED.
• YOU DON’T WANT TO GO SO FAST THAT YOU LOSE THE LEARNING ALONG THE WAY.
39
40. GALLERY
UNEDITED
SEEDING ALCOHOL INK PAINTINGS
SEEDING DIGITAL PHOTOGRAPHS
MORE POWERFUL
OVERLAYS
STYLE TRANSFER
LANDSCAPE MIXER
COLOR TRANSFER
MORE SUBTLE OVERLAYS
HARMONIZATION
DEPTH BLUR
COLORIZATION
40
111. IN THE CO-CREATION, IS THIS MORE HUMAN
OR MORE MACHINE?
WHAT DO YOU THINK? WHY?
WHAT DO YOU THINK THE LEGAL ESTABLISHMENT HAS SAID SO FAR ABOUT ART CREATED BY AI
ALONE?
111
112. CO-CREATED ART IS CYBORGIAN
• THIS CO-CREATED ART IS
• PART-HUMAN, PART-MACHINE;
• HUMAN INTELLECT-BASED, MACHINE INTELLECT-BASED;
• AND BORN-ANALOG IN SOME CASES.
• AND BORN-DIGITAL IN OTHERS.
• OR THE CO-CREATED ART IS ALL HUMAN WITH THE SUPPORT OF AI-BASED TOOLS.
• IS THE ASSERTION OF “CO-CREATION” WITH AN AI ROMANTICIZING THE ACTS OF CREATION? THE
HARNESSING OF A TECHNICAL TOOL?
• OR IS IT GIVING THE HUMAN TOO MUCH CREDIT, GIVEN THE HEAVY (BUT FAST) COMPUTATION IN AI
APPLICATIONS?
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113. GENERAL ADVICE
• EXPERIMENT BROADLY. TRY EVERYTHING. PAY ATTENTION AS YOU’RE EXPERIMENTING.
HARNESS SERENDIPITY.
• BE SURE TO PRESERVE A COPY OF EVERY LOOK THAT YOU LIKE. SOMETIMES, IT’S VERY HARD TO
RECAPTURE A PARTICULAR LOOK.
• DON’T FORGET THE OTHER DIGITAL IMAGE EDITING TOOLS (AND EQUIPMENT: DIGITAL
CAMERAS, SCANNERS, PEN-AND-TABLET SETUPS) (AND ANALOG MATERIALS) AND SUCH, JUST
BECAUSE NEURAL FILTERS ARE SO DAZZLING THAT THEY CAN OVERSHADOW THE OTHER
ELEMENTS AND TOOLS.
113
114. CONTACT
• DR. SHALIN HAI-JEW
• ITS, KANSAS STATE UNIVERSITY
• SHALIN@KSU.EDU
• 785-532-5262
• A CLARIFICATION: A FEW OF THE WORKS HAD MULTIPLE DIGITAL EDITS APPLIED INSTEAD OF JUST ONE
NEURAL FILTER. HOWEVER, THE MAIN NEURAL FILTER APPLIED WAS USED TO DECIDE WHICH CATEGORY THE
VISUAL WAS PLACED IN. I ALSO USED A FEW SEEDING LINE ART WORKS FOR DIFFERENT VISUAL EFFECTS.
• MISTAKES: I DID DUE DILIGENCE TO TRY TO PRESENT THIS AS ACCURATELY AS POSSIBLE GIVEN THE AVAILABLE
DOCUMENTATION AND FIRST-HAND EXPERIENCES. ALL MISTAKES ARE MY OWN. PLEASE FEEL FREE TO EMAIL IF
YOU HAVE FOUND A MISTAKE.
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