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000-M71 IBM Information Management Content Management OnDemand Technical Mastery Test v1

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000-M71 exam Dumps Source : IBM Information Management Content Management OnDemand Technical Mastery Test v1

Test Code : 000-M71
Test Name : IBM Information Management Content Management OnDemand Technical Mastery Test v1
Vendor Name : IBM
: 38 Real Questions

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At suppose convention, IBM launches new products and features for managing distinctive clouds | killexams.com Real Questions and Pass4sure dumps

IBM Corp. is stepping up its hybrid-cloud push because it bids to become the go-to service company for businesses that use diverse public and personal cloud structures.

the use of “multiclouds” is becoming rather regular, with the IBM Institute for enterprise cost estimating that ninety eight % of all groups will adopt hybrid guidance know-how architectures by way of 2021. corporations are doing so in an effort to take knowledge of every cloud platform’s exciting capabilities, but they face difficulties in doing so for lack of consistent equipment to control and combine distinct clouds.

That explains why IBM is including to its hybrid cloud tools and services choices. at the IBM believe conference in San Francisco today, the enterprise announced a brand new Cloud Integration Platform that’s supposed to make it less complicated to roll out utility functions across numerous clouds. It also introduced new features to support manipulate supplies across cloud environments and at ease the data and functions that reside in them.

The IBM Cloud Integration Platform serves because the leading groundwork of the company’s new hybrid cloud play, connecting functions, application and functions throughout public and private clouds and on-premises systems. The platform gives integration tools for these apps which are obtainable from a single development environment, that means that developers should write, look at various and at ease their code most effective as soon as before rolling it out to essentially the most relevant cloud.

the brand new platform is being provided alongside new IBM functions for cloud approach and design. IBM is providing to help agencies control IT elements across their hybrid cloud infrastructures. in addition, IBM is launching a new Cloud Advisory consulting provider that goes even extra through assisting valued clientele architect their total cloud suggestions from beginning to conclusion. IBM observed groups will use open and secure multicloud ideas and its Cloud Innovate formulation and equipment to help shoppers with software development, migration, modernization and administration.

Naturally, protection is yet another massive challenge for any commercial enterprise adopting a multicloud strategy, and for that purpose IBM also announced new services to support shield cloud workloads. The IBM Cloud Hyper give protection to Crypto service provides encryption key management via a dedicated cloud hardware protection module in response to FIPS a hundred and forty-2 level four-based expertise.

“IBM is executing in its pivot in against hybrid cloud offerings, in combination with the brand new capabilities it receives from crimson Hat,” which it observed closing fall it might acquire in a $34 billion deal, mentioned Holger Mueller, predominant analyst and vp of Constellation research Inc. “As such, IBM needs to create new layers that summary different public clouds and on-premises capabilities, and IBM Cloud Integration systems is doing exactly that. however to be a hit, corporations additionally desire functions, so IBM is adding these for the administration and operation of a multicloud environments.”

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Perficient Named IBM 2019 Watson Commerce business companion of the 12 months | killexams.com Real Questions and Pass4sure dumps

Perficient, Inc. PRFT, +0.94% (“Perficient”), a leading digital transformation consulting company serving world 2000® and other gigantic business purchasers all the way through North the usa, announced it has been named IBM’s 2019 Watson Commerce company associate of the 12 months. The IBM Excellence Award, announced right through IBM’s PartnerWorld at feel 2019, acknowledges Perficient’s ongoing increase and relationships with key shoppers, and concept leadership across the IBM Watson consumer Engagement Commerce platform as an necessary component for digital transformation.

“Our strategy to commerce is concentrated on crafting a event, connecting with customers, and delivering a seamless customer experience across channels and all through the business, imperatives in nowadays’s client-driven world,” said Steve Gatto, country wide income director, Commerce solutions, Perficient Digital. “collectively, with their valued clientele, we’re transforming businesses in a method that not most effective drives growth but strengthens their ordinary manufacturer, and they normally evolve their offerings to hold consumers at the properly of their online game. We’re honored to be identified by IBM, and we’re looking forward to sharing their creative solutions all the way through IBM suppose 2019.”

Perficient Digital Takes Commerce solutions beyond Transactions to seriously change the consumer Lifecycle for a global assorted company

With branded producers and distributors under drive from the dramatic shift to online procuring, a worldwide different manufacturer sought to digitally transform its commerce business. In partnership with Perficient Digital, both corporations delivered optimized consumer sales, up to date product guidance (PIM), and streamlined the ordering process through building of a B2B portal. With the implementation of IBM’s Sterling Order management device (OMS), and Perficient’s knowledge, the various manufacturer is future-proofing its business to align with industry developments and market alternatives.

in addition, the enterprise’s OMS will supply them superior flexibility in managing complicated order management scenarios, improved reliability in order processing and fulfilment, and a value reduction in enforcing throughout its business. it's going to extra permit the corporation to carry provider enhancements to its shoppers, optimize its pricing, promotion and usual provide chain, increase earnings because of more suitable stock visibility, and cut back charges through better efficiencies in order visibility.

Perficient Digital Enhances the online client experience for a leading cloth Retailer

In a market that has historically relied on brick-and-mortar experiences, a leading fabric and craft retailer become challenged with extending the customer journey on-line. Perficient partnered with the business to enforce an IBM Watson Commerce solution that supplied up-to-date visibility of its inventory and better monitoring of its product amount, location, and availability. using IBM Order management, Perficient additional stronger the answer via cloud migration that offers a single view of supply and demand, orchestrates order fulfillment techniques across purchase online Pickup In save (BOPIS) and Ship-from-shop (SFS), and empowers enterprise representatives to enhanced serve shoppers each in name centers and in-store engagements.

“Perficient has been deploying IBM Commerce solutions for essentially 20 years, providing conclusion-to-end digital commerce solutions that embody distinct channels, and carry seamless and efficient experiences across their whole commercial enterprise,” spoke of Sameer Peera, regularly occurring manager, Perficient’s commerce apply. “With the recent information that HCL took over construction of IBM WebSphere Portal, IBM net content management and net experience manufacturing facility, their valued clientele proceed to interact us for assist with their digital commerce thoughts. We’re glad to be their go-to accomplice as they navigate the altering market panorama and carry for his or her customers.”

Perficient competencies in motion at IBM suppose 2019

moreover its award-profitable commerce solution skills, Perficient specialists are accessible during the IBM believe 2019 convention in sales space #320 to talk about its event and advantage throughout the IBM portfolio , in particular cloud, cognitive, information, analytics, DevOps, IoT, content management, BPM, connectivity, commerce, cell, and customer engagement.

whereas IBM has announced its plans to promote its commerce portfolio, the news of its acquisition of crimson Hat additionally signaled the criticality cloud development and start play in successful conclusion-to-end digital transformations. As an IBM international Elite partner, one among simplest seven partners with that reputation globally, and a crimson Hat Premier accomplice, Perficient is smartly placed to work with each organizations via this transition. And, their specialists should be reachable all through IBM consider to discuss the way to navigate the cloud market, share key customer success experiences, and provide strategic competencies on the alternatives forward for valued clientele.

“technology is altering so abruptly, and enterprises should retain pace or face disruption,” mentioned Hari Madamalla, vp, rising solutions, Perficient. “With knowledge and journey in all aspects of the commerce journey, to main cloud, hosting, managed capabilities and support options, organisations turn to Perficient as a go-to associate for his or her digital transformations.”

be a part of a few Perficient field depend experts and their shoppers as they existing right through six IBM suppose classes, including:

As a Platinum IBM enterprise accomplice, Perficient holds greater than 30 awards across its 20-year partnership historical past. The business is an award-winning, licensed software value Plus answer company and one of the most few companions to acquire dozens of IBM professional degree application competency achievements.

For updates all through the adventure and after, join with Perficient experts online through viewingPerficient and Perficient Digital’s blogs, or observe us on Twitter@Perficient and @PRFTDigital.

About Perficient

Perficient is the leading digital transformation consulting enterprise serving global 2000® and commercial enterprise clients all over North the united states. With unparalleled tips expertise, administration consulting, and inventive capabilities, Perficient and its Perficient Digital agency bring vision, execution, and cost with wonderful digital journey, enterprise optimization, and industry solutions. Their work allows consumers to enrich productiveness and competitiveness; develop and strengthen relationships with consumers, suppliers, and companions; and in the reduction of charges. Perficient's specialists serve consumers from a network of places of work throughout North the united states and offshore locations in India and China. Traded on the Nasdaq global opt for Market, Perficient is a member of the Russell 2000 index and the S&P SmallCap 600 index. Perficient is an award-successful Adobe Premier companion, Platinum degree IBM business accomplice, a Microsoft country wide carrier company and Gold CertifiedPartner, an Oracle Platinum companion, an superior Pivotal able associate, a Gold Salesforce Consulting partner, and a Sitecore Platinum associate. For greater tips, visitwww.perficient.com.

safe Harbor commentary

one of the vital statements contained during this news release that don't seem to be basically old statements discuss future expectations or state other ahead-looking counsel regarding economic effects and business outlook for 2018. these statements are discipline to popular and unknown hazards, uncertainties, and other elements that could cause the specific results to differ materially from those meditated by using the statements. The ahead-looking tips is in response to management’s latest intent, belief, expectations, estimates, and projections involving their enterprise and their trade. you should definitely be mindful that these statements simplest replicate their predictions. actual hobbies or outcomes may also fluctuate considerably. critical elements that may trigger their precise results to be materially different from the forward-searching statements include (however don't seem to be confined to) these disclosed beneath the heading “risk factors” in their annual record on kind 10-k for the year ended December 31, 2017.

View source version on businesswire.com: https://www.businesswire.com/news/home/20190212005973/en/

source: Perficient, Inc.

Ann Higby, PR supervisor, Perficient, 314-219-8836ann.higby@perficient.com

Copyright enterprise Wire 2019


Twistlock Integrates with IBM Cloud security consultant to bring protection and probability Intelligence Alerting | killexams.com Real Questions and Pass4sure dumps

available these days, the integration allows for Centralized security handle for the insurance policy of Kubernetes and Cloud Native applications on IBM Cloud

SAN FRANCISCO, Feb. 13, 2019 /PRNewswire/ -- Twistlock, the most comprehensive, automated and scalable cloud native cybersecurity platform, introduced today that it has integrated with IBM Cloud safety consultant. IBM Cloud clients are actually able to entry Twistlock's cloud native security intelligence via IBM Cloud safety advisor, a unified safety interface that makes it handy to manipulate threats and vulnerabilities in a 'single pane of glass.'

IBM Cloud protection advisor is designed to summarize critical protection tips from the IBM Cloud Kubernetes service throughout handy-to-navigate tiles, similar to Twistlock, and to alert clients whenever a security difficulty is detected. It gives granular details for a consumer for investigating and prioritizing security and compliance concerns, and provides historic aspect and intelligence behind the alert. it might support additional offer protection to environments with the aid of proposing remediation steps and insights for the coverage of Kubernetes purposes in production.

"The swift rise of cloud native purposes is pushing corporations to take a tough analyze how security is integrated into utility building and start at every degree," noted Dr. Nataraj Nagaratnam, special engineer and chief know-how officer for cloud security, IBM. "the mixing of Twistlock's functions into IBM Cloud protection consultant gives IBM Cloud shoppers extra protection and compliance capabilities on the way to innovate with self belief."

"by means of securing Kubernetes functions on IBM Cloud with Twistlock, customers will make sure that they are building and deploying compliant, reputable application that end clients can have faith," referred to John Leon, Director of BD and Partnerships at, Twistlock. "We're proud to be a security collaborator for IBM Cloud and its shoppers."

For greater suggestions about the way to implement Twistlock into IBM Cloud protection advisor, please seek advice from https://www.ibm.com/blogs/?p=165188.

About TwistlockTwistlock is the realm's most finished cloud safety platform for safeguarding modern enterprise workloads and cloud native applications towards threats. depended on with the aid of 30% of the Fortune one hundred and the realm's most discerning CISOs, it provides a unified solution for guaranteeing that containers, serverless, and microservices-primarily based purposes are compliant and comfortable all over the building lifecycle. via delivering the latest danger intelligence, potent vulnerability management, computerized runtime insurance policy and firewalls, microsegmentation for granular manage, and seamless integration with CI/CD equipment, Twistlock powers the continuous deployment of professional, comfy purposes at scale. it is backed via YL Ventures, TenEleven, Rally Ventures, Polaris partners, ICONIQ, and Dell technologies Capital. Twistlock was centered in 2015 is based in Portland, OR. For more information, please discuss with www.twistlock.com.

 

View fashioned content material:http://www.prnewswire.com/news-releases/twistlock-integrates-with-ibm-cloud-safety-guide-to-convey-protection-and-risk-intelligence-alerting-300794728.html


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Ask HN: Who wants to be hired? (May 2016) | killexams.com real questions and Pass4sure dumps

Location: New York City, NY | Austin, TX

Remote: Yes

Willing to relocate: Yes

Technologies: JavaSript, React, Angular, Node.js, TDD, Unit Testing, Git, HTML, ES6, CSS, AJAX, RESTful APIs, MongoDB, NPM, SQL (MySQL, PostgreSQL), D3, Mithril, Mocha/Chai, jQuery, Bootstrap, Backbone.js, Webpack, Grunt, Babel

Github: https://github.com/Yangani

LinkedIn: https://www.linkedin.com/in/festuskipropy

Portfolio: http://www.festus.me

Résumé/CV: https://www.dropbox.com/s/lmtv8a7rjzy8iv0/Festus_Kiprop_Resu...

Blog: https://medium.com/@yanganif

Email: fyangani@gmail.com

I'm a Full Stack JavaScript Engineer experienced with modern stacks including: React, Angular, Node, Express, MongoDB, and PostgreSQL. ]I've built entire web apps from front-end to back-end and everything in between. I have a passion for creating beautiful, intuitive interfaces with clean, well-structured back-ends.

Location: Remote, willing to relocate

Remote: Yes

Willing to relocate: Yes

Technologies: Python, Django, Mobile(iOS, Android), jQuery, Bootstrap, Backbone, React, React-Native, Docker, Vagrant, Ansible, AWS, Golang, R, Hadoop, Spark

Resume: http://www.linkedin.com/in/sidmitra

Github: http://github.com/sidmitra

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Email: sidmitra.del@gmail.com

Some examples from the portfolio:

* http://www.igrowfit.com - A corporate fitness subscription platform

* http://www.grapevinelogic.com - A platform for advertisers to work with Youtube content creators.

* http://publish.saxo.com - publishing platform where anyone can publish e-books, and soon printed books (print on demand) and online courses.

* Intuit CPASelect - An online marketplace for tax experts. [Formerly Teaspiller, acquired by Intuit, now pivoted to another product]

* http://www.hypedsound.com - A platform for music artists to share their content from various networks.

I am a full stack developer, who's worked extensively with Python/Django. I also have a background in data science, scipy, numpy, pandas, scikit-learn, nltk etc.

Location: Toronto, ON ( Canada)

Remote: No. (though perfectly willing to work on a trial basis remotely before relocating.)

Willing to relocate: Yes (US and Canada only - If you are based in US, I will expect efforts from you to apply for a US visa that leads to a P.R. A TN permit for Canadians like me can be used as a stop gap measure.)

Technologies: Web, Android(out of touch) and Windows desktop apps. * 15 years of experience on the web(from frontend to back, though my pref is mostly backend). See résumé link below for further details.

Résumé/CV: http://tinyurl.com/n5bdsd9 (downloadable in .doc, github code also available)

Email: dennis_quicksort-1@yahoo.com

* =not that the number of years of experience matters, but 20 somethings shallow 'software-is-my-only-passion' kids are annoying,(I'm fine with the young, wise, humble ones). I prefer working with silver hairs(or ones born with silver hair - like I was :)) who have life/opinions/wisdom beyond software. Take Care.

---------Ignore below this, these are keywords that should be meaningless to you-----------

ASP.NET, .NET, Java (J2EE ) - Servlets, JSP, JDBC, EJB (Limited Experience), JavaMail, Junit, ANT, Maven, log4j, SQL, T-SQL, PL/SQL, stored procedures, cron jobs, LINQ, HTML, HTML5, DHTML, XHTML, ASP, PHP, C#, XML, SOAP, Web Services, WSDL, CXF, Axis, XPath, XSLT, AJAX, VB6, Windows Forms, Crystal Reports, JavaScript, CSS, CSS3, ActionScript, Flash, Object oriented programming, Optimizeit, javadoc, Popchart Java, VtigerCRM, Smarty, jQuery, Jquery UI, phpMyAdmin, Visual Studio.Net IDE, Netbeans, Eclipse, VJ++, VSS, SVN, Dreamweaver, TOAD, Winmerge, Microsoft Visio, Microsoft Project, Mantis, Java on Android, Android SDK, AVD Manager, Android Emulator, Android Debug Bridge, Databases – Oracle, SQL Server, MySQL, MS Access, SQLite

EMAIL: darryl@yourweb.expert

LOCATION: China (native English speaker)

REMOTE: Yes (3 years fully remote experience)

WILLING TO RELOCATE: Yes, to Singapore, Australia, USA. Visa required. British citizen.

TECHNOLOGIES:

* Front-end: JavaScript (Angular/React, Flux, ES2015 + Babel), Advanced CSS (BEM/OOCSS/Namespacing/Low-specificity/LESS/SASS/Stylus/Post CSS), Jade, Coffeescript, Typescript

* Workflow: Grunt/Gulp/Webpack/Make/NPM

* Server-side: Node (Express), SQL/NoSQL, PHP, Apache/Nginx.

* Testing: Karma, Jasmine, Mocha

* UI/UX: Living style guides (auto-generated through documentation & build tools), design in the browser

* Other: HTTP/2, AMP, SSL, SSH, Linux, Service worker, agile project management, Git, SVN, Slack, Hipchat, Github, Bitbucket, Jira, Trello, Heroku, Openshift, Digital Ocean, Cloudflare, experience with team management & training

Focus on performance, accessibility, device-agnosticism, scalability, and machine-readability (SEO). Extremely clean code. Good documentation.

RÉSUMÉ/CV:

https://yourweb.expert

https://www.linkedin.com/in/darrylsnow

https://github.com/darryl-snow

LOOKING FOR:

Passionate teams I can learn from. Full-time positions. Project Manager roles. Senior Front-end Developer roles. Opportunities to work on large-scale web projects, focus long-term on one product, hybrid mobile apps, JavaScript apps, dashboards, data visualisation.

Location: Oakland, California (neighboring San Francisco across the bay..)

Relocation?: Yes (with assistance) - San Francisco, San Diego, Seattle, Portland, Chicago, New York City

Resume/CV: Word: https://drive.google.com/open?id=0B-KnHM7a2EJ8SGJxSzIyd1FVUV... PDF: https://drive.google.com/open?id=0B-KnHM7a2EJ8enB3ZVRXcFl1eT...

Email: coroutines@gmail.com

Github: https://github.com/blitmap

Languages Used Daily: Coffeescript, ES6/ES7 Javascript, Lua, C

Infrequent Languages: Python, Ruby, Moonscript, Perl, PHP, C++

Looking For: remote Coffeescript (backend/frontend) work - or QA Testing and Technical Customer Support / TSE

Hi,

I'm Jonathon -

I'm looking for anything and everything Coffeescript. It is the language I enjoy most and I want to work with it everyday doing something interesting! I'm looking for my first real programming gig after 5 years of sysadmin work - I would welcome any opportunity to prove my worth (bring on your coding exercises!). I have a background working with the IRC protocol, low-level networking, and preparing documentation for projects mostly. I love writing integrations for Slack and gaining experience on the frontend side of things. Bots and building APIs are a particular curiosity. :-)

I think I can do my best work if I have a personal interest in the mission of the company I might join. I am of course a gamer so I would be thrilled to work for Twitch or Steam, but I also have an interest in doing some good for the public. I have an interest in companies like Watsi, the No-Starch Press, and Clever. If you're a humanitarian project, an educational institution, or are working to improve access to taxpayer-funded government data: I would love to hear from you!

I look forward to seeing what you do! - thank you for your time :-)


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Creative AI: On the Democratisation & Escalation of Creativity | killexams.com real questions and Pass4sure dumps

Chapter 01 by Roelof Pieters & Samim Winiger >>>> Find us at @CreativeDotAI <<<< Introduction

We live in times, where science fiction authors are struggling to keep up with reality. In recent years, there has been an explosion of research and experiments that deal with creativity and A.I. Almost every week, there is a new bot that paints, writes stories, composes music, designs objects or builds houses: Artificial Intelligence systems performing creative tasks?

Our research started by wondering about this phenomenon and playfully experimenting with it. This lead to an in-depth investigation, of what they call “CreativeAI”. This document is the first chapter of their adventure into CreativeAI, aiming at establishing a backstory and language they can use to talk about this intricate subject. Their initial intuition was, that creativity is a central force throughout human history — and is currently evolving in interesting ways. In their attempt to understand this phenomenon, they think about creativity and technology in a structured way. They focus on emerging creation patterns, Assisted Creation and Generative Creation, and argue that they are leading to the Democratization and Escalation of Creativity.

The goal of this project is to find a set of guiding principles, metaphors and ideas that inform the development of a CreativeAI praxis, new theories, experiments, and applications. To explore this space, they investigate history and technology, construct a narrative and develop a vision for a future where CreativeAI helps us raise the human potential.

INDEX
  • Creativity
  • Assisted Creation
  • Generative Creation
  • Conclusion
  • Authors & Acknowledgments
  • References
  • >>>> Subscribe to their Newsletter <<<<

    Interested to help out bringing AI to studios and agencies in creative industries around the world? We’re hiring at http://join.creative.ai/

    1. Creativity It is central to the human condition and takes many forms in their daily activities, yet defining creativity is challenging. This section provides a selective overview of historical, theoretical and technological metaphors for creativity, relevant for CreativeAI.

    Ancient cultures lacked their concept of creativity, including thinkers of Ancient Greece, China, and India [1]. They viewed creativity as a form of discovery. The rejection of creativity in favor of discovery would dominate the west until the Renaissance. By the 18th century, mention of creativity became more frequent, linked with the concept of imagination [2]. In late 19th century, theorists such as Walls, Wertheimer, Helmholtz and Poincaré [3] began to reflect on and publish their creative processes, pioneering the scientific study of creativity.

    The scientific study of creativity produced many theories, models and systems throughout the 20th century: philosophical, sociological, historical, technical and practical. While defining creativity in objective terms was and still is challenging, the systematic study of creativity and its enabling factors allowed industries such as advertising, architecture, design, fashion, film and music to adopt creative processes rapidly and reproduce them at scale.

    Science, technology and creativity have a long, intertwined history. Selecting which metaphors to explore is an important research decision. They explore three metaphors: Augmented Creativity, Computational Creativity and Creative Systems. Augmented Creativity

    In “As They May Think” (1949), Vannevar Bush imagines the “memex”, a desk-like device where people could search through a library of articles through a series of switches [4]. While entirely mechanical, Bush describes a device that features hyperlinked text, aggregated notes and bookmarks all extending human capacity to research and process information: The web.

    Vannevar Bush / Memex (1949)

    The article inspired a young Douglas Engelbart to quit his job and attend graduate school at UC Berkeley [5]. At Berkeley he wrote a paper, published in 1962 titled “Augmenting the Human Intellect: A Conceptual Framework”. In it, Engelbart, influenced by Bush’s memex concept, wrote about a “writing machine [that] would permit you to use a new process of composing text (..) You can integrate your new ideas more easily, and thus harness your creativity more continuously (..) This will probably allow you to devise and use even-more complex procedures to better harness your talents…” [6].

    Engelbart did not only provide a vision of interacting with a computer system but he had a guiding philosophy [7]. He believed that computers can be used to create an extension for the ways they do thinking, representation and association in their minds [8]. Engelbart’s vision was not just to automate processes but to multiply the power of people and collaborators by creating systems that augment their intellect, humanity and creativity. His goal was to raise the human potential [9].

    Sketchpad (1963) and First Virtual Reality Headset (1968) by Ivan Sutherland

    Ivan Sutherland, a student of Claude Shannon, who in turn was a student of Vannevar Bush, built a working system inspired by the Memex already in 1963. His seminal PhD project “Sketchpad” [10] is considered to be the ancestor of modern computer-aided design (CAD) programs [11]. It demonstrated the potential of interactive computer graphics for technical and creative purposes.

    Sketchpad (1963) and First Virtual Reality Headset (1968) by Ivan Sutherland

    Only a few years later, Engelbart’s Stanford Augmentation Research Center (ARC), invented a range of technologies, still widely used today. Among them, video conferencing and the mouse [12]. Simultaneously, John McCarthy had founded the Stanford Artificial Intelligence Laboratory (SAIL). McCarthy’s group wasn’t concerned with augmentation, but wanted to reproduce the human intelligence electronically [13]. Engelbart’s Center and McCarthy’s Laboratory brought together Ph.D.s, hardware and software hackers, and high school students, including Steve Wozniak and Steve Jobs [14], to experiment collectively.

    Xerox Parc Computers and GUI (1970s) Mass Market Video Chat (2005) / VR (2016)

    When Xerox (a paper company) decided to fund its Palo Alto Research Center (PARC) in 1970 [15], it quickly attracted ARC and SAIL veterans eager to work on personal computing, user interface design and graphics. This facility developed a number of innovations like ethernet and pioneered a new metaphor for doing creative work with computer systems: the Desktop. Soon after Xerox opened its center, a more informal but equally important outlet and movement emerged to explore computers: The Homebrew Computing Club. Homebrew attracted a mix of antiwar activists, makers and computer scientists. Ultimately, Dozens of companies, including Apple and Microsoft, and technologies such as the Personal Computer (PC) would come out the Homebrew movement [16].

    Apple Computer 1, by Apple Computer Company (1976) Computational Creativity

    Already in 1950, Claude Shannon was able to approximate proper English grammar and generate new sentences using computational methods [17]. Such early research in “computational creativity” lead to an interdisciplinary dialog, exploring the use of computational approaches for creative problems.

    IBM 7094 with IBM 7151 Console (1962) / Creative use of Computer Graphics by A. Michael Noll at Bell Labs (1962). Generative music video, by Raven Kwok (2015)

    Starting the early 1960s, researchers at Bell Labs were pioneering the use of computers for creativity. In a series of breakthrough experiments, they generate graphics, animations and art [18] with early computer systems. One of the most active researchers was Michael Noll. In 1970, he made a call to action: “What they really need is a new breed of artist-computer scientist” [19]. Noll’s call was soon echoed by artists and musicians, such as Brian Eno. Already in 1975, Eno was using algorithmic and generative principles to compose music — later describing his work as “using the technology that was invented to make replicas to make originals” [20].

    Backcover of Brian Eno’s Generative Music Album “Discreet Music” (1975) / Computer Generated Ballet — Michale Noll (1960s) Generating Music From Sport Data (2015) / Music Style Transfer (2015) / Machine Learning Drum Machine (2015)

    A further milestone was set in 1979 by Benoit Mandelbrot [21] with the discovery of the Mandelbrot set. He was the first to use computer graphics to display fractal geometric images. By doing so, he was able to show how visual complexity can be created from simple rules. Fractals had a profound effect on their perception of creativity and machine. It led many to ask “can a computer/algorithm be creative?” and inspired scientists, artists and engineers to experiment with creativity.

    Benoit Mandelbrot / Mandelbrot Fractal (1979) Generative Shoe Midsoles by Nervous System (2015) / Mandelbulb 3D Fractals (2009)

    Video games pioneered the industrial application of computational creativity. Around 1978, games started to make extensive use of procedural systems to define game maps and character behaviours [22]. Such methods allowed for the development of complex gameplay without having to spend excessive time creating games. Games such as Simcity [23] by Will Wright developed these concepts further with playful interactive simulations of complex systems.

    Procedural Games: Beneath Apple Manor (1978) / Akalabeth (1980) Procedural Game Universe — No Man’s Sky (2016)

    Since the 1980s, focused research in industry and academia has led to the formalisation of computational creativity as a scientific discipline [24]. At the same time, a wide range of fields — such as computer science, architecture and design — started intensely experimenting with computation creatively. Finding a single definition for computational creativity is challenging, yet many have tried. A currently often cited definition is: “create computations which — if they were made by humans — would be deemed creative” [25].

    DeepForger — Image Style Transfer with Deep Neural Networks (2016)

    Today, interest in creativity from an A.I perspective has begun to blossom, with yearly conferences, schools and PhD programs dedicated to computational creativity [26]. A steady surge of ideas and techniques, that are at least computationally creative in intention, have moved into the mainstream: A.I characters, artificial musicians, journalist bots, generative architecture and neural nets that “dream”. While such systems are nowhere near human capabilities, they are actively being used in culture, industry and academia to create outputs that are increasingly met with great curiosity by the public. In many areas, systems are making the leap from experimentation to production, leading to new creative processes and outputs.

    Woman working on ENIAC — The first electronic general-purpose computer (1940s). Creative Systems

    After World War II, the United States enjoyed a period of euphoria. The Allied Powers had triumphed — seemingly through science, technology and systems thinking. In this environment, the Josiah Macy Jr. Foundation organized a series of conferences from 1946 to 1953 “on the workings of the human mind” [27], later titled “Cybernetics”. The aim of the conferences was to promote meaningful communication across scientific disciplines and restore unity to science [28]. It included people like J.C.R. Licklider, Margaret Mead, Heinz von Foerster, John von Neumann, Claude Shannon and Norbert Wiener.

    Macy Conference attendees (1940s)

    Inspired by the conference, in 1948 Wiener published his seminal work “Cybernetics: or Control and Communication in the Animal and the Machine” [29] and Shannon published “A Mathematical Theory of Communication” [30]. Such works laid the foundation for today’s information age by providing a scientific theory for concepts such as “information”, “communication”, “feedback” and “control”.

    Wiener defined cybernetics as the science of adaptive, feedback-based control [31]. The name comes from the ancient greek word for steersman. Cybernetics takes the view that control in complex environments must be conversational. It requires not just action but also listening and adaptation: To steer a boat across a lake, you have to use your tiller and sails to adjust to changing winds and currents. The cybernetic model of control is circular, decisions depend not only on how well people carry out their intentions but also on how the environment responds.

    An early link between Cybernetics and Creativity was made in 1968 with the exhibition “Cybernetic Serendipity”, at the Institute of Contemporary Arts in London [32]. The show explored connections between creativity and technology. Artists such as Gordon Pask and Nam June Paik were using systems to generated music, poetry, movies, paintings and computer graphics [33].

    This new spirit of creation was addressed by Buckminster Fuller in his notion of the “comprehensive designer”, which he describes as “an emerging synthesis of artist, inventor, mechanic, objective economist and evolutionary strategist” [34].

    Cybernetic Serendipity Exhibition (1968) Cybernetics/Control Theory in action today: Robot, Boston Dynamics (2016), Robotic Painter (2013)

    In the following decades, cybernetic ideas would profoundly impact thinking in fields such as business, politics, art, design and architecture [35]. As Pask noted, “architects are first and foremost systems designers,” but they lack “an underpinning and unifying theory… Cybernetics is a discipline which fills the bill” [36]. By systematically integrating context and relationships, cybernetics pushed creation & design beyond its object-based approach.

    While cybernetics went out of fashion in the 1970s, its legacy lives on in fields such as Control Theory and Complex Systems Studies, Interaction Design and Design Thinking [37]. Today holistic approaches, that attempt to combine technological, human and social needs, are cited in many fields. Inspired by cybernetics, creative systems thinking has found “surprising” application in areas such as software (agile, open-source), management (Google 20% time), labour (Uber / Lyft) and resource allocation (algorithmic trading / amazon).

    Examples / Media The following a selection of projects from Augmented Creativity, Computational Creativity and Creative Systems research. The aim is to provide visual context and show progress over time.

    1. Computer Interaction Input Device (1968)2. Mouse — Mass market Input Device (1982)3. Touch Screen — Input Device (1982)4. Mass Market Voice Control (2011)5. Mass Market Virtual Reality Headset (2016)

    1. Sketchpad — Computer Aided Design (1963)2. Autocad — Mass market CAD Tools (1982)3. Maya — Mass Market 3D CAD (1998)4. Generative Bicycle — 3D Printed (2015)5. Generative Dress — 3D printed (2016)

    1. Tetris — Procedural Gameplay (1984)2. Simcity — Simulation of Complex Systems (1989)3. Spore — Procedural Game Characters (2008)4. Minecraft — Procedural 3D Worlds (2011)5. No Man’s Sky — Universe Simulation (2016)

    1. Hypercubes — Computer Graphics/Animation (1968)2. Fractals — Complexity from Simple Rules (1980)3. Mandelbulb — 3D Fractals(2009)4. DeepDream — Generative Painting (2015)5. NeuralPatch — Generative Style Transfer — (2016)

    — Intermission —

    In the previous section, they explored the long, intertwined history of science, technology and creativity. In this process they investigated three metaphors for creativity: Augmented Creativity, Computational Creativity and Creative Systems. In the following sections, they consider how these metaphors have developed further and extrapolate two main categories of activity today: Assisted Creation and Generative Creation.

    2. Assisted Creation

    Humans have used tools to extend their creative capabilities since the stone age — adapting to changing needs. While mastering creative skills used to be attainable only for few, assistive systems are making creativity more accessible. This section presents three generations of assisted creation systems and explores how they democratise and escalate creativity.

    Inspired by Engelbart’s vision from the 1960s, countless scientific papers and experiments explored how to assist humans to perform “creative” tasks — or as researcher Ben Shneiderman defined it, technologies that allow more people “to be more creative more of the time” [1]. Such research, coupled with the emerging PC revolution, allowed companies like Apple and Lotus to build early digital applications for creative tasks. Ultimately, this movement led to the founding of companies such as Autodesk (1979) [2] and Adobe (1982) [3], that exclusively focused on building tools and systems that enable creativity.

    Industry pioneered the development of first generation assisted creation systems in the 1980s: Photoshop, Autocad, Pro-Tools, Word and many more. First generation systems mimic analogue tools with digital means [4]. The human’s full attention is required to drive the creative process: Feedback is slow and assistance limited. Yet, such tools allowed expert and non-experts alike to be more creative, which lead to a flood of new creative processes and outputs.

    Adobe Photoshop 1.0 (1988) / Autodesk Autocad 1.0 (1982)

    The camera Autofocus, invented by Leica in 1976 [5], is an early example of a second generation assisted creation system. In these systems, humans and machines negotiate the creative process through tight action-feedback loops. The machine is provided with greater agency so control can be shared. Decisions are made collaboratively with the system. Second generation systems are ubiquitous today. They are being used in production across cultures and industries.

    Leica SLR Camera with Autofocus (1976) / Autocorrect (1991) / Autotune (1998)

    Autocorrect, invented in 1991 by Dean Hachamovitch at Microsoft [6], changed how millions of people write — Autotune, invented in 1998 by Andy Hildebrand at Exxon [7], transformed how music is made. The impact such systems had on creativity is hard to measure, yet clearly significant: By lowering the bar of mastery, assisted creation systems empowered experts and non-experts alike to shift their attention to higher level issues, perform complex creative tasks more reliably and experiment quickly. While such systems are not with out their risks and complications [8]- ultimately, they enable us to be more creative, more of the time.

    Assisted Creation 3.0

    Second generation systems are often limited and limiting: Negotiation for control is blunt and interactions not fine grained. Due to such limitation, widely used tools such as autocomplete have a mixed reputation. A set of new ideas and techniques, coming from diverse research disciplines, promise to overcome previous limitations. They define them as Third generation assisted creation systems (AC 3.0). A shared vision is to design systems that negotiate the creative process in fine-grained conversations, augment creative capabilities and accelerate the skill acquisition time, from novice to expert. Third generation assisted creation principles are finding practical use across an expanding range of creative tasks.

    To name a few examples:
  • Assisted Drawing helps illustrators to draw, by correcting strokes.
  • Assisted Writing helps authors to write, by improving text style.
  • Assisted Video helps directors to edit, by fine-tuning movie cuts.
  • Assisted Music helps musicians to make music, by suggesting ideas.
  • Assisted Photo Enhancement (2016) / Assisted User Gesture Map (2014) Assisted Freehand Drawing (2011)

    Describing the breadth of ongoing research in a few examples is challenging, as there are many ideas and domains to explore. To track assisted creation, they analyzed recent research publications across many organisations with the help of machine learning, graph theory and visualization. Judged on quantitative measures (publications and experiments), assisted creation research and use is on the rise across creative disciplines. Notably, Machine Learning (ML) and Human Computer Interaction (HCI) are contributing a steady stream of research, relevant for the design of assisted creation systems. Together, ML and HCI are providing us with a conceptual framework for machine intelligence in a human context.

    Already in 2011 Rebecca Anne Fiebrink, HCI/ML researcher at Goldsmith University, fittingly asked: “Can they find a use for machine learning algorithms in unconventional contexts, such as the support of human creativity and discovery?“ [9]. In the years since, Fiebrink’s call has been taken up by a multidisciplinary community: a wide range of new ideas, theories, experiments, approaches and products are being explored and developed. Ongoing HCI/ML research offer us new possibilities and metaphors for the design of assisted creation systems.

    Selection of graph analysis of ongoing HCI/ML research (AE, 2016) Democratisation and Escalation

    By researching Assisted Creation, they recognise emerging trends, with implications for creativity: 1. Assistive Creation Systems are making a wide range of creative skills more accessible. 2. Collaborative platforms, such as Online Video and Open Source, are making it easier to learn new creative skills. As these trends are increasingly converging, they are accelerating the skill acquisition time from novice to expert. This is leading to a phenomenon they have named “the democratisation of creativity”. They explore these trends further and extrapolate a vision.

    Assisted Handwriting Beautification (2013) / Assisted Fashion Style Selection (2015) / Assisted Animation with Webcam (2015) TREND 1: Creativity is becoming more accessible.

    While having a photo studio or music recording studio at home was but a dream for a 1980s creator, in today’s world it’s one click away. Such trends, observable for many creative tasks, are empowering non-experts and experts alike to be more creative, more of the time. One could say, the price of creation is falling. In this trajectory, a key challenge has been the “high barrier to entry” [10] for those without specific skills or talents. Today, Assisted creation systems are increasingly lowering this “high bar” by actively guiding creative processes and bootstrapping the learning of new skills.

    Assisted Reading (2015) / Assisted Hair Design from Photos (2015) / Assisted CV Writing (2015) TREND 2: Collaboration is becoming more accessible.

    Already in the 1960’s, Engelbart’s vision was not only about enhancing individuals: He wanted to augment the collective intelligence and creativity of groups, to improve collaboration and group problem-solving ability. With the rise of collaboration and social software, and a deeper theoretical understanding of how groups can use technology to self-organize and cooperate, systems are emerging that can make groups effectively more creative. A key notion is that creativity is a collective process that can be strengthened through technology, but goes beyond just technological means. Human capabilities and tool capabilities have to be raised in sync.

    The escalation of creativity

    By projecting these trends into the (near) future, they can start to imagine a scenario they call “the escalation of creativity”: a world where creativity is highly accessible and anyone can write at the level of Shakespeare, compose music on par with Bach, paint in the style of Van Gogh, be a master designer and discover new forms of creative expression. For a person who does not have a particular creative skill, gaining a new capability through assisted creation systems is highly empowering. If creative tasks can be master on-demand and access is democratically shared, age-old notions such as “expert” or “design” are bound to be redefined. Further, this escalation can lead us to scenarios such as using creativity as means of empathic communication — at scale.

    Assisted Drumming with Robotic Arm (2016) / Assisted Physical Table (2015)

    Even though such scenarios are currently fiction, thinking about the implications of the democratisation and escalation of creativity, influences today’s design decisions. Creating systems that are respectful of cultural practices, support different types of creativity, are responsive to human needs, provide feedback transparently and are ethically grounded is proving to be highly challenging already now.

    Automation or Augmentation

    A first question they should ask ourselves when talking about the democratisation and escalation of creativity is “Are they designing tools that empower us or autopilots that replace us?”. Such questions have been negotiated in a global discussion, starting over 5000 years ago with the use of oxen in agriculture [11]. History shows us that any technology has feedback dynamics and momentum, or in the words of Marshall McLuhan: “First they shape their tools, thereafter they shape us” [12]. Nonetheless, they see technology not as a primary force of nature: Human decisions and actions play a key role — negotiated through culture, politics & power.

    Collage of “Portrait of a Family in a Landscape” (1641) / Le Net — First convolutional neural network used to automatically read bank notes — (1989)

    A second question to address is “what does automation mean?”. Their current understanding of automation is heavily influenced by ideas from the industrial revolution: mass producing goods with mechanical butlers. While concerns about mass unemployment due to certain types of automation have to be taken serious, automation is not inherently bad; some “jobs” might be better left to machines, as they can be inhumane and wasteful of human potential. Shifting the discussion to one of human potential and capability — reflecting on their strengths and weaknesses, needs and dreams — allows us to reframe fears of automation as opportunities for augmentation.

    Augmentation is not the same as automation: Where automation promises to “free us from inhumane tasks”, augmentation aims at strengthening their capabilities. It is the notion of raising the collective human potential, not replacing it. To analyse this notion further, they refer to a framework introduced by NASA, for thinking about autonomy: The H-Metaphor [13]. It proposes to view their interactions with systems more as they do with horses, instead of butlers.

    Image from “The H-Metaphor” by NASA (2003)

    Think of a rider on a horse: If a rider uses deliberate movements, the horse follows exactly. As the control becomes vaguer, the horse resorts to familiar behaviour patterns and takes over control. Being able to “loosen or tighten the reins” leads to smooth ebb and flow of control between human and horse, rather than instructions and responses. Considering feedback and control is key.

    HCI/ML Researcher Roderick Murray-Smith suggests using the H-Metaphor and control theory when thinking about interface dynamics. He predicts:

    “Future devices will be able to sense much more on and around them, offering us more ways to interact. They can use this to let go sometimes and be casual about their interactions” [14]. First Film Recording of Race Horse by Eadweard Muybridge (1878)

    Having the ability to interact with systems casually, and let go of control at times, promises substantially improved forms of human-machine and human-human collaboration. By combining human intuition with machine intelligence, shared control principles lets us imagine new creative processes, not possible independently by either human or machine. Such principles promise to make creativity more accessible and raise their collective potential.

    While a range of dystopian outcomes can easily be imagined, they deliberately choose to explore a vision that focuses on opportunities, not fears. In order to prevent bleak future scenarios, complex metaphorical and ethical questions can not be an afterthought, but are an opportunity for collaborative exploration and a call to action for collective decision making.

    Examples / Media

    The following is a selective overview of ongoing assisted creation research, experiments and products, across a range of creative tasks / disciplines.

    Assisted Photography

    1. Assisted Photo Enhancement (2016) 2. Assisted prediction of photo memorability (2015).3. Assisted Categorisation and Tagging of Photos (2015).4. Auto Photo Colorisation (2016)5. Realtime Smile and Emotion Detection (2015).

    Assisted Drawing

    1. Assisted Handwriting Beautification (2013).2. Assisted Freehand Drawing with Real-time Guidance (2013).3. Autocomplete hand-drawn animations (2015).4. Animating drawings with face recognition (2015).5. Robotic Handwriting Assistance (2013).

    Assisted Read/Write

    1. Assisted CV text creation and optimisation (2015).2. Auto-respond to email (2015)3. User guided / Automatic summarization of text (2015).4. Text Style transfer from English to Shakespeare (2015).5. Word Processor with a Crowd Inside (2010).

    Assisted Music

    1.Music style and harmony transfer, genre to genre (2014).2. Composing Music with Augmented Drawing (2009).3. Assisted Musical Genre Recognition (2013).4. 909 Drum-machine that learns from behaviour (2015).5. Assisted Robo Guitarist (2013).

    Assisted Design

    1.Learning Visual Clothing Style (2015).2. Assisted Design of 3d models by merging shapes (2015)3. Learning Perceptual Shape Style Similarity (2015).4. Parsing Sewing Patterns into 3D Garments (2013).5. Shape Shifting Table (2015).

    Assisted Experiments

    1. Wearable Assisted Text-Reading Device (2015).2. Pain Visualization through patient text (2013).3. Hair Modeling with DB (2015).4. Assisted Ethical Decision Making, with a fan (2015).5. Text Entry for Novice2Expert Transitions (2014).

    Assisted Community

    1. Real Time video stream of creative processes (2015).2. Massive Open Course (2012) 3. Large scale Open Source Collaboration (2008).4. Creative Process Question Answer Sites (2010).5. Zero Cost Creative Content Distribution (2007).

    Assisted Culture

    1. Assisted drumming with third robot arm (2016).2. Assisted Vending, selects drinks based on looks (2016). 3. Computer Ballet (2016).4. Assisted Karaoke Singing with Face Swap (2016).5. Pingpong Assistant with AR Glasses (2015).

    3. Generative Creation

    Our abilities to represent complex creative problems is increasing. A fundamental shift in perspective is allowing us to revisit many creative problems. The following section presents generative creation and explores how it democratises and escalates creativity.

    Representation has played a pivotal role throughout human history. Ever more detailed representational systems have allowed us to communicate complex phenomena in understandable terms — to organise information, manage problems and make informed decisions. Since the invention of writing, representational strategies have evolved substantially. From the inclusion of measurement in the early 16th century [1], to the adoption of perspective drawing in the Renaissance [2]: New forms of representation have lead to revolutions in science and technology.

    Historic Representational Experiments

    Abstraction strategies, such as drawing and writing, try to represent big ideas with highly limited means. They force humans to keep all the moving parts in their heads. As Matt Jezyk (Autodesk) suggests, such tools were invented in the age of documentation, where the bandwidth to represent problems was low [3]. Jezyk describes the 20th century as the age of optimization: New techniques, such as simulation expanded their representational bandwidth significantly and allowed many disciplines and industries to adopt reproducible abstraction methods at scale.

    Early 20.Century Simulations

    Historically, the use of simulations were largely isolated in different fields. 20th century studies of systems theory and cybernetics combined with the proliferation of computers led to a more unified, systematic perspective: the age of models. A Model is a high-bandwidth, computational representation of reality. The model represents the system — its characteristics and behaviors — whereas the simulation represents the operation of the system over time. This new representational paradigm does not rely on abstraction methods but tries to make “things that behave like the thing they represent”.

    21st Century Digital Modeling

    Models give us an infrastructure for representing the overall problem. They help us to understand complex interconnected issues and develop a deeper understanding of inherent logic and relationships of parts. While modeling techniques go back till at least the early 1940s (Nuclear-bomb simulation) [4], it was the exclusive domain of experts and inhibitively expensive. Today, modeling and simulation methods are becoming highly accessible and cheap.

    We argue that accessible modeling techniques are allowing us to negotiate a wide range of creative problems from a higher level perspective and create differently. They explore this emerging pattern — culture, technology and implications — and name it the generative age.

    The Generative Age

    Already in the 1960s, Engelbart pointed to the impact digital technologies have on their representational ability: “We can represent information structures within the computer that will generally be far too complex to study directly” [5] . A conceptual link between these new representational abilities and creativity was made in the early 2000s by designers like Patrik Schumacher, co-founder of Zaha Hadid Architects. He describes an “ontological shift”, from the platonic ideal shapes of the past 5000 years, to new computational “primitives“ [6].

    What he was alluding to, is a fundamental shift in perspective, from 3D to nD: While the renaissance age gave us the ability to represent reality from a three dimensional perspective (3D), the generative age enables us to represent (model) complexity and see (infer) reality from a probabilistic, or high dimensional perspective (nD). Inspired by such ideas, influential design manifestos [7], books [8], and software [9] were published, laying the foundation for a new movement: Generative Design.

    Generative Column Design, created with digital manufacturing, by Michael Hansmeyer (2010) “Digital Grotesque”, by Michael Hansmeyer and Benjamin Dillenburger (2013)

    Michael Hansmeyer, an architect, describes Generative Design as “thinking about designing not the object but a process to generate objects” [10]. He is implying a shift from object to process — from certainty to probability — suggesting that instead of designing one “artefact”, they use computational models to design processes that generate infinite “artefacts”.

    “Housing Agency System: Mass-Customization System for Housing” by Autodesk (2012)

    Essentially, Generative Design is an umbrella term — describing ongoing research and developments in diverse fields, ranging from design, architecture, industrial design to machine learning. A shared vision is to empower human designers to explore a greater number of design possibilities from a new perspective and lower the time between intention and execution. In the generative age, the cost of creating diversity and complexity is falling. This allows us to create an order of magnitude more elaborate form and function. For example: bicycles that are mass-customizable to people’s individual taste, while using a fraction of the material traditionally required. While generative approaches are not constrained to any particular field, notably architecture and more recently design have been among the first disciplines to systematically take hold of these approaches, as illustrated in the following examples.

    Generative Dress: “Kinematics” by Nervous System (2014) / Generative Shoes: “Molecule-shoes” by Francis Bitonti (2014)/ Generative Shirts: “Processing Foundation” (2015) Generative Car: “Hack Rod” by Autodesk (2015) / Generative Bicycle: “Skeleton” by Gary Liao (2016) Generative Study “gaudism” by echonoise (2013) / Generative Architecture “Heydar Aliyev Centre” by Zaha Hadid (2012) / Generative Study by Designmorphine (2015) Generative Chair with “Dreamcatcher” by Autodesk (2015) / Generative Lamp “Hyphae” by Nervous System (2014) / Lamp made with 3d room scan by Hybrid Platform (2015) Generative Bow: “Tekina — Optimal Recurve Bow” by Aminimal Studio (2015) / Generative Motion Art by Raven Kwok (2015)

    While Generative Models have been used for creative applications since the 1970s (procedural game), recent research advances — driven notably by Machine Learning and Deep Learning — are leading to a quantitative and qualitative leap in generative modeling capabilities. Today, new models are released practically every week. Research projects with acronyms such as VAE [11], DRAW [12], VRNN [13], GAN [14], DCGAN [15], LAPGAN [16] and GRAN [17] are allowing us to model complexity with greater resolution and apply modelling techniques to a wider range of creative problems.

    Autoencoding images beyond pixels (2015)

    The application of generative machine learning models to creative tasks is a recent development, yet it is already leading to the discovery of new primitives for creation: Design building blocks that are applicable across many creative domains. Such creative generative models have been successfully used to generate fashion items, paintings, music, poems, song lyrics, journalistic news articles, furniture, image and video effects, industrial design, comics, illustrations and architecture, to name just a few of its applications. See “Examples” section for a selection of projects.

    “Semantic Shape Editing Using Deform Handles” (2015) / “Procedural Modeling Using Autoencoder Networks” (2015) “NeuralDoodle” — Semantic Image Style Transfer (2016) / Automatic Colorization of B/W images with neural nets (2016) / “Neural Image Analogies”(2001/2016) Deep Visual Analogy-Making (2015) / Generative Form Editor “Cindermedusae” (2015) / Exploratory Modeling with Collaborative Design (2012) Generated Street Sign Images (2015) / Generated Fake Chinese Characters (2015) / Generated Choreography and Animation (2016) RNN generated Super Mario Levels (2016) / RNN generated TED Talks (2015) / RNN generated Wikipedia Article (2015) Artist Agent: Reinforcement learning ink painting (2013) / BrainFM — Dynamic Generative Music for Relaxation (2015) / Jukedeck — Generative Music for Videos (2015)

    Generative models let us explore data in unprecedented ways. To give an example, imagine a chair: they can represent its characteristics, such as color, height or style, as dimensions in high-dimensional information spaces. This space can be filled with data about millions of chairs. Chairs with similar characteristics are mapped in vicinity of each other. This creates a chair model, which can be explored and visualized.

    “Joint Embeddings of Shapes and Images via CNN Image Purification” (2015)

    Such high-dimensional topologies allow us to easily retrieve information, explore data and ask questions about relationships, logic and meaning: e.g. “Show me all chairs that are red and tall”. Further, they allow us to make predictions and infer new characteristics: e.g. “Show me all chairs, that are similar to chair A and B but unlike chair C”. Finally, they can use high-dimensional spaces to generate new objects: e.g. “Make me a chair that resembles a car, and is comfortable to sit in”.

    Generative models invite designers to play with data and generated infinite imaginative variations and solutions to creative problems. By having powerful tools to explore, optimise and test creative design ideas rapidly, they computationally maximise the opportunity for serendipity. While generative models can be used to perform classical creative tasks efficiently, additionally they open up a range of new creative capabilities, incomparable with classical methods.

    Artificial Serendipity: Systems that maximise the opportunity for serendipity.

    Multi-Modal Network Diagram (2015) / Generating Stories about Images (2015)

    Recent advances in machine learning make it possible to include data from different “modalities” in a single model. It enables us to translate between modalities. The key insight is that all forms of information can be encoded in a shared information space. Early research into multimodality has lead to a set of widely adopted systems: “Auto Translate” [18] lets us translate from one language to another, “Speech2Text” [19] transcribes audio to text. Multi-modal machine learning is allowing for more complex scenarios, which go beyond simple translation of data: Generate Images from Text [20], Text from Videos [21], Music from Movement [22], 3D shapes from shopping data, etc. They call this:

    Artificial Synesthesia: Systems that enable inter-sensory experiences.

    Democratisation and Escalation

    The Generative age gives us a new canvas for creativity, which they have only just started to explore. While it is hard to predict where these developments will take us, emerging trends are worth investigating — as their impact can already be felt. By extrapolating these developments and thinking about their implications, they arrive at a scenario they call “the democratisation and escalation of creativity”.

    We explore this notion further and describe four trends: Image from Apollo 10 Space Mission (1969) Image from “Large-scale Image Memorability” (2015)

    1. Generative Perspective: For the first time in human history, they can create from a blended, or generative perspective — as it mixes elements of the collective human perspective, machine perspective and individual perspective. It gives us the ability to transcend creative constraints, such as habit, socialisation and education, and create objects which are altogether new.

    2. Generative Predictions: Take the concept of recommendation, personalization and customisation and apply it to the creative process. The vision is to have systems that suggest potential next “actions”, allow people to casually adjust aspects of designs according to personal needs and histories, and enable us to playfully discover creativity.

    Image from “Corporate Social Networking Platforms As Cognitive Factories” (2016) Image from “Generative strategies for welding” (2016)

    3. Generative Markets: In the future, generative models might be shared in an open collaborative model marketplace (OCMM). While current marketplaces allow us to trade artefacts/products, generative markets will facilitate the sharing of recipes to create unlimited new artefacts. In essence, think of it as GitHub for open-source creativity.

    4. Generative Manufacturing: Emerging digital manufacturing techniques, such as 3D printing, combined with generative systems used to create physical objects. Early signs of such trends can be observed in things such as Shapeways, Kickstarter and the “maker movement”. It’s starting to redefine the relationship between creation, production and consumption.

    Autodesk Project “Dreamcatcher” (2015)

    When projecting such scenarios even further into the future, they arrive at the realisation that Generative Creation has profound implications for fields like technology, manufacturing, resource allocation, economics and politics. Already today, Generative Creation methods are leading to the democratisation of creativity in many areas. By lowering the time between intention and realisation, Generative Creation is leading to an escalation of new “artefacts” — forms, functions and aesthetics. It allows us to explore what lies beyond the artefact.

    Combined with new manufacturing techniques, generative creation is redefining concepts such as production, consumption, labour and innovation. As current economic models are largely built around the notion of “artefacts”, a renegotiation of fundamentals is foreseeable. While predicting the future of the demo-cratisation and escalation of creativity is impossible, thinking about narrative, opportunities and implications, informs today’s decisions and visions. Or in the words of Robert Anton Wilson:

    Examples The following is a selective overview of generative creation research, experiments and products, across a range of creative tasks / disciplines. Generative Experiments

    1. Generating Flora and Fauna (2015).2. Generating Chairs, Tables and Cars (2015).3. Generative Font Design with Neural Networks (2015).4. Generative Manga Illustration (2015).5. Generating Faces with Manifold Traversal (2015).

    Generative Design

    1. Semantic Shape Editing Using Deform Handles (2015).2. Generative Motorcycle swingarm Design (2015).3. Generative airplane partition design (2015).4. Generative Data-Driven Shoe Midsole Design (2015).5. Generative Jewellery design (2015).

    Generative Text

    1. Generating Stories about Images (2015).2. Generating Sentences from a continuous space (2015).3. Generative Journalism (2010)4. Generating Cooking recipes with Watson (2015).5. Generating Clickbait Web content and site (2015).

    Generative Serendipity

    1. Exploratory Modeling with Collaborative Design (2015).2. Generative Music Score Composition with RNNs (2015).3. Messa di Voce, Generative Theater (2003).4. Generative Image Style Transfer (2015).5. Interactive Neural Net Hallucinations (2015).

    Generative Architecture

    1. Generative Columns Design and Manufacturing (2010).2. Generating House 3d Models with House agents (2012).3. Heydar Aliyev Center (2012).4. Generative Biological inspired form (2015).5. Francis Bitont on 3D printing (2015).

    Generative Design

    1. Generative strategies for welding (2015)2. Generative Car Chaise Design (2016).3. Generative mass customized knitwear (2016)4. Generative mass customized T-shirt and bags (2016)5. Generative Lampshade based on room 3D scan (2015).

    Generative Games

    1. Generative Creation of Universe (2014).2. Texture Synthesis (2015).3. Generative Character Controls (2012).4. Generative Game Map and Characters (2013).5. Generative enemy manager (2010).

    Generative Synesthesia

    1. Synesthesia Mask Lets You Smell Colors (2016).2.Cross-modal Sound Mapping Using ML (2013).3. Expressing Sequence of Images with Sentences (2015).4. Generative Graffiti, adapting to Music (2016).5. Music to 3d Game (2001).

    Conclusion

    Our research journey began with a series of experiments — playfully exploring the space between creativity and A.I. It led to an in-depth investigation into creativity, which reinforced their initial intuition that creativity is a central, evolving force throughout human history. New metaphors such as Augmented Creativity, Computational Creativity and Creative Systems allowed us to approach creativity from new perspectives and explore how it intersects with technology.

    During this journey, they have tried to think about creativity and technology in a structured way. This has allowed us to recognize, analyze and define emerging creation patterns. They focused on two general types: Assisted Creation and Generative Creation. Together, these patterns are leading to a vision they call the democratization and escalation of creativity: A world where creativity is highly accessible, through systems that empower us to create from new perspectives and raise the collective human potential. Through their research, they learned to appreciate creativity as an ever evolving, driving force of humanity and as a wide open frontier for interdisciplinary research and development.

    A primary goal of this research project was to find a set of guiding principles, metaphors and ideas, that inform the development of future theories, experiments, and applications. By combining different domains into one narrative, they formulate a new school, or praxis for creativity: CreativeAI. Its desire is to explore and celebrate creativity. Its goal is to develop systems that raise the human potential. Its belief is that addressing the “what” and “why” is as important as the “how”. Its conviction is that complex ethical questions are not an afterthought, but an opportunity to be creative collectively.

    Finally, CreativeAI is a question, rather than an answer. Its only demand is more collaboration and creativity. It is an invitation for play!

    “The creation of something new is not accomplished by the intellect but by the play instinct acting from inner necessity. The creative mind plays with the objects it loves” — Carl Jung



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