{"id":24336,"date":"2020-04-29T07:00:00","date_gmt":"2020-04-29T14:00:00","guid":{"rendered":"https:\/\/insidebigdata.com\/?p=24336"},"modified":"2023-05-30T11:35:13","modified_gmt":"2023-05-30T18:35:13","slug":"ai-under-the-hood-playform","status":"publish","type":"post","link":"https:\/\/insidebigdata.com\/2020\/04\/29\/ai-under-the-hood-playform\/","title":{"rendered":"AI Under the Hood: Playform"},"content":{"rendered":"<div class=\"wp-block-image\">\n<figure class=\"alignright size-large\"><img decoding=\"async\" loading=\"lazy\" width=\"300\" height=\"131\" src=\"https:\/\/insidebigdata.com\/wp-content\/uploads\/2020\/04\/Playform_logo.png\" alt=\"\" class=\"wp-image-24337\" srcset=\"https:\/\/insidebigdata.com\/wp-content\/uploads\/2020\/04\/Playform_logo.png 300w, https:\/\/insidebigdata.com\/wp-content\/uploads\/2020\/04\/Playform_logo-150x66.png 150w\" sizes=\"(max-width: 300px) 100vw, 300px\" \/><\/figure><\/div>\n\n\n<p>In this regular insideBIGDATA feature we highlight our industry\u2019s \nmovers  and shakers, companies that are pushing technology forward, and \nsetting  trends for innovation. We look at companies with a focus on big\n data,  data science, machine learning, AI and deep learning \u2013 some new,\n some  old, always leading, always dynamic. We also take deep dives into\n new  technology promoted (or hyped) as \u201cAI\u201d or my favorite \u201cAI-powered\u201d\n to  provide transparency for what\u2019s really going on under the hood. \nWatch  this column for intimate coverage of some pretty cool firms doing\n some  pretty exciting things. Enjoy the ride! <\/p>\n\n\n\n<p>In this installment of \u201cAI Under the Hood\u201d I introduce recently launched <a rel=\"noreferrer noopener\" aria-label=\"Playform (opens in a new tab)\" href=\"https:\/\/www.playform.io\/\" target=\"_blank\">Playform<\/a> (Artrendex Inc.), a generative AI collaborative tool for artists. The company\u2019s tech publicist reached out to me around when the global pandemic became serious, so it&#8217;s taken a while for me to write this review. But I was impressed with all the materials the company provided me for making a technology assessment. As a previous researcher myself, I&#8217;m always excited when a company sends me a link to an arXiv.org paper written by a founder. Nice touch!<\/p>\n\n\n\n<figure class=\"wp-block-embed is-type-video is-provider-vimeo wp-block-embed-vimeo wp-embed-aspect-16-9 wp-has-aspect-ratio\"><div class=\"wp-block-embed__wrapper\">\n<div class=\"embed-vimeo\" style=\"text-align: center;\"><iframe loading=\"lazy\" src=\"https:\/\/player.vimeo.com\/video\/394682544\" width=\"600\" height=\"338\" frameborder=\"0\" webkitallowfullscreen mozallowfullscreen allowfullscreen><\/iframe><\/div>\n<\/div><\/figure>\n\n\n\n<p>Dr. Ahmed Elgammal and his team&#8217;s latest paper &#8220;<a rel=\"noreferrer noopener\" aria-label=\"Sketch-to-Art: Synthesizing Stylized Art Images From Sketches (opens in a new tab)\" href=\"https:\/\/arxiv.org\/pdf\/2002.12888.pdf\" target=\"_blank\">Sketch-to-Art: Synthesizing Stylized Art Images From Sketches<\/a>&#8221; explaining the GAN framework behind Playform\u2019s &#8220;Sketch to Image&#8221; tool.  Dr. Elgammal is also the founder and director of Rutgers University&#8217;s Art and Artificial Intelligence Lab.  The tool itself synthesizes fully detailed, stylized images from simple sketches using three modules:  a dual-masked mechanism, a feature-map transformation technique, and an inverse procedure of instance-normalization.   <\/p>\n\n\n\n<p>Playform utilizes GANs to enrich creativity rather than replace it. Playform is user-friendly software that allows artists to custom train the AI with their own images and gives artists a variety of ways to experiment with generative AI. Using one such tool, for example, Playform can take a simple sketch and transform it into a full-fledged image with color, texture, and stunning detail. It allows a designer to create a series of prototypes of a piece of clothing, say, then layer in other textures, colors, and images for a truly innovative result. The platform functions like an interactive mirror, reflecting back novel iterations that help creatives evolve their ideas.&nbsp; <\/p>\n\n\n\n<p>You can find a deep dive into the Playform technology and how it compares  to\/evolves past other applications that synthesize images (SketchyGAN,  GauGAN), <a rel=\"noreferrer noopener\" aria-label=\"HERE (opens in a new tab)\" href=\"https:\/\/becominghuman.ai\/sketch-to-art-synthesizing-stylized-art-images-from-hand-drawn-sketches-f6511ab5c84f\" target=\"_blank\">HERE<\/a>. For instance, unlike GauGAN, Playform&#8217;s AI model guesses the semantics of the composition&nbsp;rather than requiring the user to manually label each region. This automated semantic understanding is achieved through novel components in the Generator, Discriminator, and feature extractor.&nbsp; <\/p>\n\n\n\n<blockquote class=\"wp-block-quote\">\n<p>\u201cWe think of Playform as accompanying and reflecting artists as they generate and perfect their ideas,\u201d says Ahmed Elgammal, founder of Playform. \u201cPlayform can be integrated into the creative process because we built it in collaboration with artists.\u201d <\/p>\n<\/blockquote>\n\n\n\n<p>Generative AI, when algorithms create new images, texts, or sounds based on training from massive data sets, enriches human creativity, without replacing it. Playform was built on ongoing artist input, to ensure it served visual creatives in a way they could easily incorporate into their practice. \u201cPlayform is not a tool. It\u2019s a creative soulmate to enhance artistic expression,\u201d says Elgammal.&nbsp;<\/p>\n\n\n\n<p>Behind Playform\u2019s innovative new features like sketch-to-image lies a powerful AI engine trained on centuries of artworks, representing a range of styles, cultures, and techniques. Crafted with an eye for art history and style, this data set allows the AI to identify, mimic, and completely transform a range of images and inputs. From a bare bones sketch, for example, Playform can generate a novel landscape, portrait, or other type of image in a specific user defined style or historical style, taking its cues from Monet, Turner, Roerich, or one of many other artists or movements.<\/p>\n\n\n\n<blockquote class=\"wp-block-quote\">\n<p>\u201cOur goal is to find out ways for generative AI to fit the creative process of artists and other creative professionals. We work closely with artists to understand what they want to do and how AI can help that. We look for questions like \u2018Is it possible to do X?\u2019 and try to make it possible,\u201d Elgammal reflects. \u201cTo discover what artists truly want, we have developed an artist residency program where we worked with artists, for one month each on specific projects based on their concepts and adapting the AI technology to achieve what they want to do. We then used the experience to make this particular creative process part of Playform.\u201d&nbsp;<\/p>\n<\/blockquote>\n\n\n\n<p>Elgammal and the Playform team worked with artist and instigator Devin Gharakhanian, who created abstract images from old photographs of Charlie Chaplin and who helped inspire Playform\u2019s style morph feature. The portraits were displayed at SCOPE Art Fair in conjunction with Art Basel Miami, causing a buzz at the high-profile event and making history as the first human-AI generated artwork displayed there.<\/p>\n\n\n\n<p>Qinza Najm, a Playform artist in residence, worked with Playform to create a process inspired by her own artworks that explored abstract images based on the human body. The series that emerged from the collaboration with Playform was chosen for an exhibit about art and science at the National Museum of China in Beijing in November 2019, with 1 million visitors to the exhibition during its one-month run.<\/p>\n\n\n\n<p>Along with groundbreaking works based on artists\u2019 existing approaches, Playform has empowered conceptual explorations of what it means to be a mediated human and how we collide and merge with our digitally generated selves. NYU professor and artist Carla Gannis used Playform to create a series of works based on childhood memories for an avatar named C.A.R.L.A. G.A.N. She then monitored people\u2019s responses to the works versus her own \u201chuman\u201d works. In another experiment, Gannis used Playform to create visuals she incorporated into a larger VR-based project, which will be exhibited at Telematic in San Francisco in March 2020. Italian artist Domenico Barra developed a project called Affiliation, which explored storytelling via Instagram stories using works made with Playform.&nbsp;<\/p>\n\n\n\n<p>Playform\u2019s images can also be used as a foundation for works in other media. Artist Anne Spalter generated images using Playform, then executed them in pastels on canvas, drawing on AI\u2019s peculiar ability to surface and blend unexpected elements. Spalter recently exhibited her Playform-based art at the Spring Break Art Fairs in LA and New York City.&nbsp;<\/p>\n\n\n\n<p>From time-tested methods to bleeding edge technology, Playform is designed to nurture and provoke creative impulses. Then artists take the next steps to make art. \u201cWe always listen to artists and creative professionals to build AI that can be part of their daily process,\u201d notes Elgammal. \u201cWith them as our guides, we want to spark new ways of seeing.\u201d<\/p>\n\n\n<div class=\"wp-block-image\">\n<figure class=\"alignleft size-large is-resized\"><img decoding=\"async\" loading=\"lazy\" src=\"https:\/\/insidebigdata.com\/wp-content\/uploads\/2018\/12\/Daniel_2018_pic.png\" alt=\"\" class=\"wp-image-21778\" width=\"127\" height=\"145\" srcset=\"https:\/\/insidebigdata.com\/wp-content\/uploads\/2018\/12\/Daniel_2018_pic.png 200w, https:\/\/insidebigdata.com\/wp-content\/uploads\/2018\/12\/Daniel_2018_pic-131x150.png 131w\" sizes=\"(max-width: 127px) 100vw, 127px\" \/><\/figure><\/div>\n\n\n<p>C<em>ontributed by Daniel D. Gutierrez, Managing Editor and Resident \n Data Scientist for insideBIGDATA. In addition to being a tech  \njournalist, Daniel also is a consultant in data scientist, author,  \neducator and sits on a number of advisory boards for various start-up  \ncompanies.&nbsp;<\/em><\/p>\n\n\n\n<p><em>Sign up for the free insideBIGDATA&nbsp;<a href=\"http:\/\/insidebigdata.com\/newsletter\/\" target=\"_blank\" rel=\"noreferrer noopener\">newsletter<\/a>.<\/em><\/p>\n","protected":false},"excerpt":{"rendered":"<p>In this installment of \u201cAI Under the Hood\u201d I introduce recently launched Playform (Artrendex Inc.), a generative AI collaborative tool for artists. The company\u2019s tech publicist reached out to me around when the global pandemic became serious, so it&#8217;s taken a while for me to write this review. But I was impressed with all the materials the company provided me for making a technology assessment. As a previous researcher myself, I&#8217;m always excited when a company sends me a link to an arXiv.org paper written by a founder. Nice touch!<\/p>\n","protected":false},"author":37,"featured_media":24337,"comment_status":"open","ping_status":"closed","sticky":false,"template":"","format":"standard","meta":{"jetpack_post_was_ever_published":false,"footnotes":""},"categories":[526,1297,115,87,180,67,56,84,1],"tags":[437,324,264,797,95],"acf":[],"yoast_head":"<!-- This site is optimized with the Yoast SEO plugin v20.6 - https:\/\/yoast.com\/wordpress\/plugins\/seo\/ -->\n<title>AI Under the Hood: Playform - insideBIGDATA<\/title>\n<meta name=\"robots\" content=\"index, follow, max-snippet:-1, max-image-preview:large, max-video-preview:-1\" \/>\n<link rel=\"canonical\" href=\"https:\/\/insidebigdata.com\/2020\/04\/29\/ai-under-the-hood-playform\/\" \/>\n<meta property=\"og:locale\" content=\"en_US\" \/>\n<meta property=\"og:type\" content=\"article\" \/>\n<meta property=\"og:title\" content=\"AI Under the Hood: Playform - insideBIGDATA\" \/>\n<meta property=\"og:description\" content=\"In this installment of \u201cAI Under the Hood\u201d I introduce recently launched Playform (Artrendex Inc.), a generative AI collaborative tool for artists. 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Gutierrez is a Data Scientist with Los Angeles-based AMULET Analytics, a service division of AMULET Development Corp. He's been involved with data science and Big Data long before it came in vogue, so imagine his delight when the Harvard Business Review recently deemed \\\"data scientist\\\" as the sexiest profession for the 21st century. Previously, he taught computer science and database classes at UCLA Extension for over 15 years, and authored three computer industry books on database technology. He also served as technical editor, columnist and writer at a major computer industry monthly publication for 7 years. Follow his data science musings at @AMULETAnalytics.\",\"sameAs\":[\"http:\/\/www.insidebigdata.com\",\"https:\/\/twitter.com\/@AMULETAnalytics\"],\"url\":\"https:\/\/insidebigdata.com\/author\/dangutierrez\/\"}]}<\/script>\n<!-- \/ Yoast SEO plugin. -->","yoast_head_json":{"title":"AI Under the Hood: Playform - insideBIGDATA","robots":{"index":"index","follow":"follow","max-snippet":"max-snippet:-1","max-image-preview":"max-image-preview:large","max-video-preview":"max-video-preview:-1"},"canonical":"https:\/\/insidebigdata.com\/2020\/04\/29\/ai-under-the-hood-playform\/","og_locale":"en_US","og_type":"article","og_title":"AI Under the Hood: Playform - insideBIGDATA","og_description":"In this installment of \u201cAI Under the Hood\u201d I introduce recently launched Playform (Artrendex Inc.), a generative AI collaborative tool for artists. 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Nice touch!","og_url":"https:\/\/insidebigdata.com\/2020\/04\/29\/ai-under-the-hood-playform\/","og_site_name":"insideBIGDATA","article_publisher":"http:\/\/www.facebook.com\/insidebigdata","article_published_time":"2020-04-29T14:00:00+00:00","article_modified_time":"2023-05-30T18:35:13+00:00","og_image":[{"width":300,"height":131,"url":"https:\/\/insidebigdata.com\/wp-content\/uploads\/2020\/04\/Playform_logo.png","type":"image\/png"}],"author":"Daniel Gutierrez","twitter_card":"summary_large_image","twitter_creator":"@AMULETAnalytics","twitter_site":"@insideBigData","twitter_misc":{"Written by":"Daniel Gutierrez","Est. reading time":"6 minutes"},"schema":{"@context":"https:\/\/schema.org","@graph":[{"@type":"WebPage","@id":"https:\/\/insidebigdata.com\/2020\/04\/29\/ai-under-the-hood-playform\/","url":"https:\/\/insidebigdata.com\/2020\/04\/29\/ai-under-the-hood-playform\/","name":"AI Under the Hood: Playform - insideBIGDATA","isPartOf":{"@id":"https:\/\/insidebigdata.com\/#website"},"datePublished":"2020-04-29T14:00:00+00:00","dateModified":"2023-05-30T18:35:13+00:00","author":{"@id":"https:\/\/insidebigdata.com\/#\/schema\/person\/2540da209c83a68f4f5922848f7376ed"},"breadcrumb":{"@id":"https:\/\/insidebigdata.com\/2020\/04\/29\/ai-under-the-hood-playform\/#breadcrumb"},"inLanguage":"en-US","potentialAction":[{"@type":"ReadAction","target":["https:\/\/insidebigdata.com\/2020\/04\/29\/ai-under-the-hood-playform\/"]}]},{"@type":"BreadcrumbList","@id":"https:\/\/insidebigdata.com\/2020\/04\/29\/ai-under-the-hood-playform\/#breadcrumb","itemListElement":[{"@type":"ListItem","position":1,"name":"Home","item":"https:\/\/insidebigdata.com\/"},{"@type":"ListItem","position":2,"name":"AI Under the Hood: Playform"}]},{"@type":"WebSite","@id":"https:\/\/insidebigdata.com\/#website","url":"https:\/\/insidebigdata.com\/","name":"insideBIGDATA","description":"Your Source for AI, Data Science, Deep Learning &amp; Machine Learning Strategies","potentialAction":[{"@type":"SearchAction","target":{"@type":"EntryPoint","urlTemplate":"https:\/\/insidebigdata.com\/?s={search_term_string}"},"query-input":"required name=search_term_string"}],"inLanguage":"en-US"},{"@type":"Person","@id":"https:\/\/insidebigdata.com\/#\/schema\/person\/2540da209c83a68f4f5922848f7376ed","name":"Daniel Gutierrez","image":{"@type":"ImageObject","inLanguage":"en-US","@id":"https:\/\/insidebigdata.com\/#\/schema\/person\/image\/","url":"https:\/\/secure.gravatar.com\/avatar\/5780282e7e567e2a502233e948464542?s=96&d=mm&r=g","contentUrl":"https:\/\/secure.gravatar.com\/avatar\/5780282e7e567e2a502233e948464542?s=96&d=mm&r=g","caption":"Daniel Gutierrez"},"description":"Daniel D. Gutierrez is a Data Scientist with Los Angeles-based AMULET Analytics, a service division of AMULET Development Corp. He's been involved with data science and Big Data long before it came in vogue, so imagine his delight when the Harvard Business Review recently deemed \"data scientist\" as the sexiest profession for the 21st century. Previously, he taught computer science and database classes at UCLA Extension for over 15 years, and authored three computer industry books on database technology. He also served as technical editor, columnist and writer at a major computer industry monthly publication for 7 years. Follow his data science musings at @AMULETAnalytics.","sameAs":["http:\/\/www.insidebigdata.com","https:\/\/twitter.com\/@AMULETAnalytics"],"url":"https:\/\/insidebigdata.com\/author\/dangutierrez\/"}]}},"jetpack_featured_media_url":"https:\/\/insidebigdata.com\/wp-content\/uploads\/2020\/04\/Playform_logo.png","jetpack_shortlink":"https:\/\/wp.me\/p9eA3j-6kw","jetpack-related-posts":[{"id":24153,"url":"https:\/\/insidebigdata.com\/2020\/03\/24\/ai-under-the-hood-causalens\/","url_meta":{"origin":24336,"position":0},"title":"AI Under the Hood: causaLens","date":"March 24, 2020","format":false,"excerpt":"In this installment of \"AI Under the Hood\" I introduce causaLens, a London-based deep tech company building a machine that predicts the global economy in real-time. The company's Growth Analyst reached out to me on LinkedIn, and I liked what I learned from the materials I received.","rel":"","context":"In &quot;AI Deep Learning&quot;","img":{"alt_text":"","src":"https:\/\/i0.wp.com\/insidebigdata.com\/wp-content\/uploads\/2020\/03\/causaLens_logo.jpg?resize=350%2C200&ssl=1","width":350,"height":200},"classes":[]},{"id":24376,"url":"https:\/\/insidebigdata.com\/2020\/05\/11\/ai-under-the-hood-decormatters\/","url_meta":{"origin":24336,"position":1},"title":"AI Under the Hood: DecorMatters","date":"May 11, 2020","format":false,"excerpt":"In this installment of \u201cAI Under the Hood\u201d I introduce Silicon Valley-based DecorMatters, a compelling creativity-sharing ecosystem that brings together interior designers and furniture shoppers to make any home renovation project easier and more affordable. Founded in 2016, DecorMatters is powered by augmented reality and AI technology, and is redefining\u2026","rel":"","context":"In &quot;AI Deep Learning&quot;","img":{"alt_text":"","src":"https:\/\/i0.wp.com\/img.youtube.com\/vi\/yOIedPez1ms\/0.jpg?resize=350%2C200","width":350,"height":200},"classes":[]},{"id":24854,"url":"https:\/\/insidebigdata.com\/2020\/08\/10\/ai-under-the-hood-flippy-the-robot\/","url_meta":{"origin":24336,"position":2},"title":"AI Under the Hood: Flippy the Robot","date":"August 10, 2020","format":false,"excerpt":"In this installment of \u201cAI Under the Hood\u201d I introduce \"Flippy\" by Miso Robotics. Flippy works in fast-food kitchens, operating a frying station for example. The product was decades in the making in terms of research in robotics and machine learning. Flippy is an amalgamation of motors, sensor, chips and\u2026","rel":"","context":"In &quot;AI Deep Learning&quot;","img":{"alt_text":"","src":"https:\/\/i0.wp.com\/insidebigdata.com\/wp-content\/uploads\/2020\/08\/Miso_paper.png?resize=350%2C200&ssl=1","width":350,"height":200},"classes":[]},{"id":24062,"url":"https:\/\/insidebigdata.com\/2020\/03\/19\/ai-under-the-hood-kaskada-inc\/","url_meta":{"origin":24336,"position":3},"title":"AI Under the Hood: Kaskada, Inc.","date":"March 19, 2020","format":false,"excerpt":"In this installment of \"AI Under the Hood\" I introduce Kasakda, Inc., a Seattle-based early stage company founded in January 2018. Kaskada is a machine learning platform for feature engineering using event-based data. Kaskada\u2019s platform allows data scientists to unify the feature engineering process across their organizations with a single\u2026","rel":"","context":"In &quot;AI Deep Learning&quot;","img":{"alt_text":"","src":"","width":0,"height":0},"classes":[]},{"id":31465,"url":"https:\/\/insidebigdata.com\/2023\/01\/26\/ai-under-the-hood-interactions\/","url_meta":{"origin":24336,"position":4},"title":"AI Under the Hood: Interactions","date":"January 26, 2023","format":false,"excerpt":"We asked our friends over at Interactions to do a deep dive into their technology. Mahnoosh Mehrabani, Ph.D., Interactions' Sr. Principal Scientist shared some fascinating information about how Interactions' Intelligent Virtual Assistants (IVAs) leverage advanced natural language understanding (NLU) models for \"speech recognition\" and \"advanced machine learning.\" The company uses\u2026","rel":"","context":"In &quot;AI Deep Learning&quot;","img":{"alt_text":"","src":"https:\/\/i0.wp.com\/img.youtube.com\/vi\/QbxAXMiwsaQ\/0.jpg?resize=350%2C200","width":350,"height":200},"classes":[]},{"id":26483,"url":"https:\/\/insidebigdata.com\/2021\/06\/17\/ai-under-the-hood-object-detection-model-capable-of-identifying-floating-plastic-beneath-the-surface-of-the-ocean\/","url_meta":{"origin":24336,"position":5},"title":"AI Under the Hood: Object Detection Model Capable of Identifying Floating Plastic Beneath the Surface of the Ocean","date":"June 17, 2021","format":false,"excerpt":"A group of researchers, Gautam Tata, Sarah-Jeanne Royer, Olivier Poirion, and Jay Lowe, have written a new paper \"DeepPlastic: A Novel Approach to Detecting Epipelagic Bound Plastic Using Deep Visual Models.\" The workflow described in the paper includes creating and preprocessing a domain-specific data set, building an object detection model\u2026","rel":"","context":"In &quot;AI Deep Learning&quot;","img":{"alt_text":"deep learning","src":"https:\/\/i0.wp.com\/insidebigdata.com\/wp-content\/uploads\/2018\/10\/shutterstock_1096541144.jpg?resize=350%2C200&ssl=1","width":350,"height":200},"classes":[]}],"_links":{"self":[{"href":"https:\/\/insidebigdata.com\/wp-json\/wp\/v2\/posts\/24336"}],"collection":[{"href":"https:\/\/insidebigdata.com\/wp-json\/wp\/v2\/posts"}],"about":[{"href":"https:\/\/insidebigdata.com\/wp-json\/wp\/v2\/types\/post"}],"author":[{"embeddable":true,"href":"https:\/\/insidebigdata.com\/wp-json\/wp\/v2\/users\/37"}],"replies":[{"embeddable":true,"href":"https:\/\/insidebigdata.com\/wp-json\/wp\/v2\/comments?post=24336"}],"version-history":[{"count":0,"href":"https:\/\/insidebigdata.com\/wp-json\/wp\/v2\/posts\/24336\/revisions"}],"wp:featuredmedia":[{"embeddable":true,"href":"https:\/\/insidebigdata.com\/wp-json\/wp\/v2\/media\/24337"}],"wp:attachment":[{"href":"https:\/\/insidebigdata.com\/wp-json\/wp\/v2\/media?parent=24336"}],"wp:term":[{"taxonomy":"category","embeddable":true,"href":"https:\/\/insidebigdata.com\/wp-json\/wp\/v2\/categories?post=24336"},{"taxonomy":"post_tag","embeddable":true,"href":"https:\/\/insidebigdata.com\/wp-json\/wp\/v2\/tags?post=24336"}],"curies":[{"name":"wp","href":"https:\/\/api.w.org\/{rel}","templated":true}]}}