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

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

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IBM IBM Information Management Content

8,769 Shares in IBM (IBM) got with the aid of providence Wealth Advisors LLC | killexams.com real Questions and Pass4sure dumps

windfall Wealth Advisors LLC received a fresh stake in IBM (NYSE:IBM) within the 4th quarter, according to the enterprise in its most concomitant 13F filing with the Securities and change fee (SEC). The institutional investor got 8,769 shares of the technology company’s stock, valued at approximately $991,000.

other hedge cash and different institutional buyers likewise these days added to or reduced their stakes within the business. Sharkey Howes & Javer boosted its stake in shares of IBM by 75.9% outright the artery through the fourth quarter. Sharkey Howes & Javer now owns 721 shares of the know-how enterprise’s inventory value $82,000 after purchasing an extra 311 shares outright the artery through the length. Sonora funding management LLC boosted its stake in shares of IBM through 166.2% perquisite through the fourth quarter. Sonora investment management LLC now owns 1,637 shares of the expertise business’s inventory cost $186,000 after purchasing an additional 1,022 shares perquisite through the duration. Lathrop funding management Corp obtained a fresh stake in shares of IBM outright through the third quarter worth about $215,000. Paloma partners management Co boosted its stake in shares of IBM by using 127.4% throughout the third quarter. Paloma partners administration Co now owns 1,453 shares of the expertise company’s stock value $220,000 after buying an further 6,757 shares outright through the duration. eventually, Harvest Fund administration Co. Ltd received a brand fresh stake in shares of IBM outright through the third quarter cost about $233,000. Institutional traders personal 56.09% of the company’s stock.

NYSE:IBM traded up $0.sixty five outright over mid-day buying and selling on Wednesday, accomplishing $136.20. The inventory had a trading volume of forty nine,828 shares, in comparison to its regular extent of 5,564,224. The commerce has a short ratio of 1.24, a latest ratio of 1.29 and a debt-to-equity ratio of two.10. The enterprise has a market cap of $123.19 billion, a P/E ratio of 9.86, a PEG ratio of 2.39 and a beta of 1.25. IBM has a twelve month low of $one hundred and five.ninety four and a twelve month unreasonable of $162.eleven.

IBM (NYSE:IBM) remaining issued its revenue effects on Tuesday, January twenty second. The technology commerce pronounced $4.87 salary per share (EPS) for the quarter, beating the Zacks’ consensus estimate of $four.82 by artery of $0.05. IBM had a internet margin of 10.ninety seven% and a return on fairness of 68.sixty one%. The company had revenue of $21.76 billion throughout the quarter, in comparison to analysts’ expectations of $21.79 billion. outright the artery through the equal age last 12 months, the commerce posted $5.14 income per share. The company’s quarterly salary changed into down 3.5% in comparison to the equal quarter ultimate 12 months. Analysts prognosticate that IBM will set aside up 13.ninety two EPS for the current 12 months.

IBM declared that its Board of directors has authorized a share buyback software on Tuesday, October 30th that makes it practicable for the company to buyback $4.00 billion in shares. This buyback authorization enables the expertise enterprise to purchase as much as three.5% of its shares via open market purchases. Shares buyback classes are often an indication that the business’s board of directors believes its inventory is undervalued.

The company likewise recently disclosed a quarterly dividend, which will be paid on Saturday, March ninth. Stockholders of checklist on Friday, February 8th will be paid a dividend of $1.57 per share. The ex-dividend date of this dividend is Thursday, February seventh. This represents a $6.28 dividend on an annualized basis and a dividend succumb of 4.sixty one%. IBM’s dividend payout ratio (DPR) is forty five.47%.

a number of equities analysis analysts Have these days weighed in on the inventory. americaneighborhood set a $a hundred and eighty.00 goal rate on shares of IBM and gave the stock a “purchase” rating in a research observe on Wednesday, October tenth. JPMorgan Chase & Co. reiterated a “impartial” rating and set a $160.00 goal fee on shares of IBM in a analysis notice on Wednesday, October 17th. ValuEngine downgraded shares of IBM from a “dangle” rating to a “sell” rating in a research notice on Wednesday, October 17th. fiscal institution of the usa reiterated a “purchase” score and set a $200.00 target cost on shares of IBM in a analysis note on Wednesday, October 17th. at last, Morgan Stanley diminished their goal fee on shares of IBM from $185.00 to $168.00 and set an “obese” score for the company in a analysis notice on Wednesday, October seventeenth. 4 analysts Have rated the stock with a promote rating, ten Have assigned a cling score and eight Have issued a purchase score to the inventory. The commerce at this time has an ordinary score of “grasp” and a consensus target cost of $154.56.

TRADEMARK VIOLATION WARNING: “eight,769 Shares in IBM (IBM) bought via providence Wealth Advisors LLC” became at the genesis suggested by means of Dispatch Tribunal and is owned by artery of of Dispatch Tribunal. when you are viewing this piece of content material on an extra e-book, it became stolen and republished in violation of united states & international trademark & copyright legislation. The felony version of this piece of content can be examine at https://www.dispatchtribunal.com/2019/02/06/8769-shares-in-ibm-ibm-bought-with the aid of-providence-wealth-advisors-llc.html.

IBM company Profile

international commerce Machines agency operates as an built-in know-how and services company international. Its Cognitive options angle presents Watson, a computing platform that interacts in language, techniques massive records, and learns from interactions with americans and computers. This angle likewise offers information and analytics options, including analytics and information management structures, cloud statistics capabilities, commerce convivial software, skill management options, and tailored trade solutions; and transaction processing software that runs mission-critical systems in banking, airlines, and retail industries.

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Institutional Ownership by Quarter for IBM (NYSE:IBM)

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IBM iX and Sitecore® Launch international marketing capabilities and technology agreement | killexams.com real Questions and Pass4sure dumps

SAN FRANCISCO, Jan. 29, 2019 /PRNewswire/ -- Sitecore [®] , the world chief in digital journey administration software, nowadays introduced a brand fresh world partnership with IBM iX, probably the most world's largest digital organizations and international commerce design companions. The partnership will obtain accessible to valued clientele Sitecore's leading web content management, commerce, and advertising options by means of IBM iX designers, expertise experts, and trade strategists in 40 IBM Studios international.

IBM iX is a proven leader in supplying Sitecore solutions, with greater than a decade's success of both agencies working together in Europe to address CMOs' needful requisite for elevated return on advertising and marketing funding. The expanded partnership brings collectively the full breadth of IBM iX's capabilities to capitalize on the becoming claim for digital advertising capabilities that create particularly-customized consumer experiences throughout outright digital touchpoints.

Matthew candy, world leader, IBM iX, pointed out that "consumer event is the key strategic objective of many businesses and core to those businesses' capability to radically change. i'm very excited that they are expanding out their latest relationship with Sitecore into a world partnership, as they revolve into a crucial participant in their ecosystem of companions." 

As a world Platinum accomplice within the Sitecore respond company application, IBM iX provides the realm-category consulting, design, construction and implementation functions required to deploy options on the Sitecore platform and deliver astonishing results for shoppers. Matched to Sitecore's main digital event management capabilities, agencies can give conclusion-valued clientele with seamless, omnichannel experiences to constrain differentiation, promote company transformation, and boost earnings and consumer lifetime value. The IBM iX and Sitecore partnership is extra empowered with top-rated practices and accelerators, as neatly as the skill to leverage the power of IBM Cloud and IBM Watson know-how. IBM iX additionally brings to abide the unparalleled skills of Bluewolf, an IBM enterprise, developing experiences with Salesforce, with whom Sitecore has a strategic alliance.

"IBM iX offers the power, scale, and lifestyle of innovation required to carry immersive, conclusion-to-conclusion digital options for their joint consumers," observed note Zablan, Chief salary Officer for Sitecore. "Our partnership makes a robust mixture for companies who requisite to accelerate the digitization of their commerce and foster a consumer-centric strategy to digital transformation."

For extra assistance on IBM iX, search counsel from www.ibm.com/ibmix  and supervene @IBM_iX on Twitter.

About SitecoreSitecore is the global chief in digital sustain administration software that combines content material administration, commerce, and customer insights. The Sitecore journey Cloud™ empowers marketers to deliver personalised content in actual time and at scale throughout every channel—earlier than, outright over, and after a sale. greater than 5,200 manufacturers––together with American express, Carnival Cruise lines, Dow Chemical, and L'Oréal––have relied on Sitecore to deliver the personalized interactions that satisfaction audiences, build loyalty, and drive salary.  

ContactMatt KrebsbachSr. Director, Public & Analyst members of the family at Sitecorematt.krebsbach@sitecore.com

Sitecore Media RelationsWE CommunicationsTeamSitecore@we-worldwide.com

Sitecore®, own the experience®, Sitecore adventure Cloud™, Sitecore xConnect™, Sitecore Cortex™, Sitecore® sustain Platform™, Sitecore event supervisor™ and Sitecore® event Database™ are registered logos or emblems of Sitecore corporation A/S within the u . s . a . and other international locations. outright different manufacturer names, product names or logos belong to their respective holders.

View original content material to download multimedia:http://www.prnewswire.com/information-releases/ibm-ix-and-sitecore-launch-world-advertising-services-and-technology-agreement-300785315.html

source Sitecore

Copyright (C) 2019 PR Newswire. outright rights reserved


Sitecore® pronounces world Partnership with IBM iX to allow main internet content administration, Commerce, and advertising options | killexams.com real Questions and Pass4sure dumps

Sitecore®, the  leader in digital event administration utility, these days announced a brand fresh world partnership with IBM iX, some of the world's biggest digital companies and global enterprise design partners. The partnership will obtain available to customers Sitecore’s leading internet content administration, commerce, and advertising and marketing options by artery of IBM iX designers, technology experts, and commerce strategists in forty IBM Studios global. 

The increased partnership brings together the entire breadth of IBM iX’s capabilities to capitalize on the becoming claim for digital marketing capabilities that create enormously-personalised consumer experiences across outright digital touchpoints. 

Matthew candy, global chief, IBM iX, pointed out that “client sustain is the needful thing strategic purpose of many companies and core to those businesses’ talent to transform. i am very excited that they are increasing out their existing relationship with Sitecore into a global partnership, as they become a crucial participant in their ecosystem of partners.” 

As a world Platinum accomplice within the Sitecore solution issuer software, IBM iX gives the world-classification consulting, design, progress and implementation capabilities required to installation solutions on the Sitecore platform and carry astonishing consequences for customers. Matched to Sitecore’s main digital sustain management capabilities, enterprises can give end-clients with seamless, omnichannel experiences to pressure differentiation, promote enterprise transformation, and expand salary and customer lifetime price. The IBM iX and Sitecore partnership is extra empowered with top-rated practices and accelerators, as smartly because the talent to leverage the vigour of IBM Cloud and IBM Watson know-how. IBM iX likewise brings to undergo the unparalleled edge of Bluewolf, an IBM company, growing experiences with Salesforce, with whom Sitecore has a strategic alliance. 

 


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IBM Information Management Content Management OnDemand Technical Mastery Test v1

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Beta Testers obtain First watch at Windows Vista | killexams.com real questions and Pass4sure dumps

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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 hold 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 fresh bot that paints, writes stories, composes music, designs objects or builds houses: simulated 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 muster “CreativeAI”. This document is the first chapter of their adventure into CreativeAI, aiming at establishing a backstory and language they can expend to talk about this intricate subject. Their initial intuition was, that creativity is a central constrain throughout human history — and is currently evolving in appealing ways. In their attempt to understand this phenomenon, they reason about creativity and technology in a structured way. They focus on emerging creation patterns, Assisted Creation and Generative Creation, and bicker 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 progress of a CreativeAI praxis, fresh 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 capitalize 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, material for CreativeAI.

    Ancient cultures lacked their concept of creativity, including thinkers of Ancient Greece, China, and India [1]. They viewed creativity as a configuration 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 noiseless 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 needful 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 progression of switches [4]. While entirely mechanical, Bush describes a device that features hyperlinked text, aggregated notes and bookmarks outright extending human capacity to research and process information: The web.

    Vannevar Bush / Memex (1949)

    The article inspired a youthful 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 expend a fresh process of composing text (..) You can integrate your fresh ideas more easily, and thus harness your creativity more continuously (..) This will probably allow you to devise and expend even-more tangled 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 enact 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 revolve 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 decendent 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, noiseless widely used today. Among them, video conferencing and the mouse [12]. Simultaneously, John McCarthy had founded the Stanford simulated 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 towering 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 enthusiastic to travail on personal computing, user interface design and graphics. This facility developed a number of innovations like ethernet and pioneered a fresh metaphor for doing creative travail with computer systems: the Desktop. Soon after Xerox opened its center, a more informal but equally needful outlet and movement emerged to explore computers: The Homebrew Computing Club. Homebrew attracted a coalesce of antiwar activists, makers and computer scientists. Ultimately, Dozens of companies, including Apple and Microsoft, and technologies such as the Personal Computer (PC) would attain 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 fresh sentences using computational methods [17]. Such early research in “computational creativity” lead to an interdisciplinary dialog, exploring the expend of computational approaches for creative problems.

    IBM 7094 with IBM 7151 Console (1962) / Creative expend 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 expend of computers for creativity. In a progression of breakthrough experiments, they generate graphics, animations and technique [18] with early computer systems. One of the most dynamic researchers was Michael Noll. In 1970, he made a muster to action: “What they really requisite is a fresh breed of artist-computer scientist” [19]. Noll’s muster 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 travail as “using the technology that was invented to obtain replicas to obtain 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 expend computer graphics to parade fractal geometric images. By doing so, he was able to demonstrate how visual complexity can be created from simple rules. Fractals had a profound upshot on their perception of creativity and machine. It led many to anticipate “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 obtain extensive expend of procedural systems to define game maps and character behaviours [22]. Such methods allowed for the progress of tangled gameplay without having to disburse unreasonable time creating games. Games such as Simcity [23] by Will Wright developed these concepts further with prankish interactive simulations of tangled 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 separate 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 abysmal 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, simulated 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 fresh 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 age of euphoria. The Allied Powers had triumphed — seemingly through science, technology and systems thinking. In this environment, the Josiah Macy Jr. Foundation organized a progression of conferences from 1946 to 1953 “on the workings of the human mind” [27], later titled “Cybernetics”. The plane of the conferences was to promote meaningful communication across scientific disciplines and restore solidarity 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 travail “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 designation comes from the ancient greek word for steersman. Cybernetics takes the view that control in tangled environments must be conversational. It requires not just action but likewise listening and adaptation: To steer a boat across a lake, you Have to expend 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 likewise 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 concomitant Arts in London [32]. The demonstrate 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 fresh 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 repercussion thinking in fields such as business, politics, art, design and architecture [35]. As Pask noted, “architects are first and foremost systems designers,” but they requisite “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 mode in the 1970s, its legacy lives on in fields such as Control Theory and tangled Systems Studies, Interaction Design and Design Thinking [37]. Today holistic approaches, that attempt to combine technological, human and convivial 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 plane is to provide visual context and demonstrate progress over time.

    1. Computer Interaction Input Device (1968)2. Mouse — Mass market Input Device (1982)3. finger 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 tangled 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 reckon 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 accomplish “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 progress 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 unhurried and assistance limited. Yet, such tools allowed expert and non-experts alike to be more creative, which lead to a flood of fresh 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 specimen of a second generation assisted creation system. In these systems, humans and machines negotiate the creative process through taut 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 repercussion such systems had on creativity is arduous 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 plane issues, accomplish tangled 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 fresh 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 expend across an expanding range of creative tasks.

    To designation 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 obtain 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 capitalize of machine learning, graph theory and visualization. Judged on quantitative measures (publications and experiments), assisted creation research and expend is on the tower across creative disciplines. Notably, Machine Learning (ML) and Human Computer Interaction (HCI) are contributing a steady stream of research, material 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 expend for machine learning algorithms in unconventional contexts, such as the champion of human creativity and discovery?“ [9]. In the years since, Fiebrink’s muster has been taken up by a multidisciplinary community: a wide range of fresh ideas, theories, experiments, approaches and products are being explored and developed. Ongoing HCI/ML research proffer us fresh 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 fresh 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 mode 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 cost 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 fresh 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 tower of collaboration and convivial software, and a deeper theoretical understanding of how groups can expend technology to self-organize and cooperate, systems are emerging that can obtain 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 instrument 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 muster “the escalation of creativity”: a world where creativity is highly accessible and anyone can write at the plane of Shakespeare, compose music on par with Bach, paint in the style of Van Gogh, be a master designer and ascertain fresh forms of creative expression. For a person who does not Have a particular creative skill, gaining a fresh 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, champion 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 anticipate ourselves when talking about the democratisation and escalation of creativity is “Are they designing tools that empower us or autopilots that supersede us?”. Such questions Have been negotiated in a global discussion, starting over 5000 years ago with the expend 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 contemplate technology not as a primary constrain 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 mention 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 enact 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 chummy 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 expend this to let vanish sometimes and be casual about their interactions” [14]. First Film Recording of Race Horse by Eadweard Muybridge (1878)

    Having the talent to interact with systems casually, and let vanish 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 fresh creative processes, not practicable independently by either human or machine. Such principles promise to obtain 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 forestall bleak future scenarios, tangled metaphorical and ethical questions can not be an afterthought, but are an occasion for collaborative exploration and a muster 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 grin 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 visage 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 mob 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. twinge 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. big scale Open Source Collaboration (2008).4. Creative Process Question respond 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 visage Swap (2016).5. Pingpong helper with AR Glasses (2015).

    3. Generative Creation

    Our abilities to portray tangled 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 particular representational systems Have allowed us to communicate tangled phenomena in understandable terms — to organise information, manage problems and obtain 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]: fresh forms of representation Have lead to revolutions in science and technology.

    Historic Representational Experiments

    Abstraction strategies, such as drawing and writing, try to portray expansive ideas with highly limited means. They constrain humans to hold outright the poignant parts in their heads. As Matt Jezyk (Autodesk) suggests, such tools were invented in the age of documentation, where the bandwidth to portray problems was low [3]. Jezyk describes the 20th century as the age of optimization: fresh 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 expend 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 fresh representational paradigm does not rely on abstraction methods but tries to obtain “things that behave like the thing they represent”.

    21st Century Digital Modeling

    Models give us an infrastructure for representing the overall problem. They capitalize us to understand tangled interconnected issues and develop a deeper understanding of inherent logic and relationships of parts. While modeling techniques vanish 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 bicker that accessible modeling techniques are allowing us to negotiate a wide range of creative problems from a higher plane perspective and create differently. They explore this emerging pattern — culture, technology and implications — and designation it the generative age.

    The Generative Age

    Already in the 1960s, Engelbart pointed to the repercussion digital technologies Have on their representational ability: “We can portray information structures within the computer that will generally be far too tangled to study directly” [5] . A conceptual link between these fresh 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 exemplar shapes of the past 5000 years, to fresh 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 talent to portray reality from a three dimensional perspective (3D), the generative age enables us to portray (model) complexity and contemplate (infer) reality from a probabilistic, or towering dimensional perspective (nD). Inspired by such ideas, influential design manifestos [7], books [8], and software [9] were published, laying the foundation for a fresh 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 demur but a process to generate objects” [10]. He is implying a shift from demur to process — from certainty to probability — suggesting that instead of designing one “artefact”, they expend computational models to design processes that generate sempiternal “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 fresh perspective and lower the time between end 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 intricate configuration 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 grasp 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 margin scan by Hybrid Platform (2015) Generative Bow: “Tekina — Optimal Recurve Bow” by Aminimal Studio (2015) / Generative Motion technique 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 abysmal Learning — are leading to a quantitative and qualitative leap in generative modeling capabilities. Today, fresh 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 fresh primitives for creation: Design building blocks that are applicable across many creative domains. Such creative generative models Have been successfully used to generate mode items, paintings, music, poems, song lyrics, journalistic advice articles, furniture, image and video effects, industrial design, comics, illustrations and architecture, to designation just a few of its applications. contemplate “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 configuration Editor “Cindermedusae” (2015) / Exploratory Modeling with Collaborative Design (2012) Generated Street mark 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 portray 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 anticipate questions about relationships, logic and meaning: e.g. “Show me outright chairs that are red and tall”. Further, they allow us to obtain predictions and infer fresh characteristics: e.g. “Show me outright chairs, that are similar to chair A and B but unlike chair C”. Finally, they can expend high-dimensional spaces to generate fresh objects: e.g. “Make me a chair that resembles a car, and is restful to sit in”.

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

    Artificial Serendipity: Systems that maximise the occasion for serendipity.

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

    Recent advances in machine learning obtain it practicable to comprise data from different “modalities” in a separate model. It enables us to translate between modalities. The key insight is that outright 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 tangled scenarios, which vanish 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 muster this:

    Artificial Synesthesia: Systems that enable inter-sensory experiences.

    Democratisation and Escalation

    The Generative age gives us a fresh canvas for creativity, which they Have only just started to explore. While it is arduous to prognosticate where these developments will grasp us, emerging trends are worth investigating — as their repercussion can already be felt. By extrapolating these developments and thinking about their implications, they arrive at a scenario they muster “the democratisation and escalation of creativity”.

    We explore this notion further and picture 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 talent 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 intimate potential next “actions”, allow people to casually adjust aspects of designs according to personal needs and histories, and enable us to playfully ascertain creativity.

    Image from “Corporate convivial 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 fresh artefacts. In essence, reason 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 end and realisation, Generative Creation is leading to an escalation of fresh “artefacts” — forms, functions and aesthetics. It allows us to explore what lies beyond the artefact.

    Combined with fresh 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 configuration (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 margin 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 odor 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 progression 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 constrain throughout human history. fresh metaphors such as Augmented Creativity, Computational Creativity and Creative Systems allowed us to approach creativity from fresh perspectives and explore how it intersects with technology.

    During this journey, they Have tried to reason about creativity and technology in a structured way. This has allowed us to recognize, analyze and define emerging creation patterns. They focused on two common types: Assisted Creation and Generative Creation. Together, these patterns are leading to a vision they muster the democratization and escalation of creativity: A world where creativity is highly accessible, through systems that empower us to create from fresh perspectives and raise the collective human potential. Through their research, they scholarly to esteem creativity as an ever evolving, driving constrain 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 progress of future theories, experiments, and applications. By combining different domains into one narrative, they formulate a fresh 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 credence is that addressing the “what” and “why” is as needful as the “how”. Its conviction is that tangled ethical questions are not an afterthought, but an occasion to be creative collectively.

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

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


    Avoid Bothersome Garbage Collection Pauses | killexams.com real questions and Pass4sure dumps

    Many engineers complain that the non-deterministic deportment of the garbage collector prevents them from utilizing the Java environment for mission-critical applications, especially distributed message-driven displays (GUIs) where user responsiveness is critical. They harmonize that garbage collection does occur at the worst times: for example, when a user clicks a mouse or a fresh message enters the system requiring immediate processing. These events must be handled without the leisurely of in-progress garbage collection. How enact they forestall these garbage collection pauses that tamper with the responsiveness of an application ("bothersome pauses")?

    We Have discovered a very efficient technique to forestall bothersome garbage collection pauses and build responsive Java applications. This technique or pattern is especially efficient for a distributive message-driven parade system with soft real-time constraints. This article details this pattern in three simple steps and provides evidence of the effectiveness of the technique.

    Pattern to Control Garbage Collection PausesThe Java environment provides so many benefits to the software community - platform independence, industry momentum, a plethora of resources (online tutorials, code, interest groups, etc.), object-oriented utilities and interfaces (collections, network I/O, sway display, etc.) that can be plugged in and out - that once you Have experienced working with Java it's arduous to vanish back to traditional languages. Unfortunately, in some mission-critical applications, like message-driven GUIs that must be very responsive to user events, the requirements constrain you to grasp that step backward. There's no margin for multiple second garbage collection pauses. (The garbage collector collects outright the "unreachable" references in an application so the space consumed by them can be reused. It's a low-priority thread that usually only takes priority over other threads when the VM is running out of memory.) enact they really Have to lose outright the benefits of Java? First, let's reckon the requirements.

    A system engineer should reckon imposing requirements for garbage collection like the following list taken from a telecom industry specimen (see References).1.  GC sequential overhead on a system may not be more than 10% to ensure scalability and optimal expend of system resources for maximum throughput.2.  Any separate GC suspension during the entire application rush may be no more than 200ms to meet the latency requirements as set by the protocol between the client and the server, and to ensure worthy response times by the server.

    Armed with these requirements, the system engineer has defined the worst-case deportment in a manner that can be tested.

    The next question is: How enact they meet these requirements? Alka Gupta and Michael Doyle obtain excellent suggestions in their article (see References). Their approach is to tune the parameters on the Java Virtual Machine (JVM). They grasp a slightly different approach that leaves the expend of parameter definitions as defined by the JVM to be used as a final tuning technique.

    Why not recount the garbage collector what and when to collect?

    In other words, control garbage collection via the software architecture. obtain the job of the garbage collector easy! This technique can be described as a multiple step pattern. The first step of the pattern is described below as "Nullify Objects." The second step involves forcing garbage collection to occur as delineated in "Forcing Garbage Collection." The final step involves either placing persistent data out of the attain of the collector or into a data pool so that an application will continue to accomplish well in the long run.

    Step 1: Nullify ObjectsMemory leaks strike concern into the hearts of programmers! Not only enact they abase performance, they eventually terminate the application. Yet remembrance leaks prove very subtle and difficult to debug. The JVM performs garbage collection in the background, freeing the coder from such details, but traps noiseless exist. The biggest danger is placing an demur into a collection and forgetting to remove it. The remembrance used by that demur will never be reclaimed.

    A programmer can forestall this sort of remembrance leak by setting the demur reference and outright underlying demur references ("deep" objects) to null when the demur is no longer needed. Setting an demur reference to "null" tells the garbage collector that at least this one reference to the demur is no longer needed. Once outright references to an demur are cleared, the garbage collector is free to reclaim that space. Giving the collector such "hints" makes its job easier and faster. Moreover, a smaller remembrance footprint likewise makes an application rush faster.

    Knowing when to set an demur reference to null requires a complete understanding of the problem space. For instance, if the remote receiver allocates the remembrance space for a message, the relaxation of the application must know when to release the space back for reuse. Study the domain. Once an demur or "subobject" is no longer needed, recount the garbage collector.

    Thus, the first step of the pattern is to set objects to null once you're certain they're no longer needed. They muster this step "nullify" and comprise it in the definition of the classes of frequently used objects.

    The following code snippet shows a mode that "nullifies" a track object. The class members that consist of primitives only (contain no additional class objects) are set to null directly, as in lines 3-5. The class members that accommodate class objects provide their own nullify mode as in line 9.

    1 public void nullify () {23 this.threatId = null ;4 this.elPosition = null ;5 this.kinematics = null ;67 if (this.iff != null)8 {9 this.iff.nullify();10 this.iff = null ;11 }12 }

    The track nullify is called from the thread that has completed processing the message. In other words, once the message has been stored or processed, that thread tells the JVM it no longer needs that object. Also, if the demur was placed in some Collection (like an ArrayList), it's removed from the Collection and set to null.

    By setting objects to null in this manner, the garbage collector and thus the JVM can rush more efficiently. Train yourself to program with "nullify" methods and their invocation in mind.

    Step 2: "Force" Garbage CollectionThe second step of the pattern is to control when garbage collection occurs. The garbage collector, GC, runs as Java priority 1 (the lowest priority). The virtual machine, VM, runs at Java priority 10 (the highest priority). Most books recommend against the usage of Java priority 1 and 10 for assigning priorities to Java applications. In most cases, the GC runs during idle times, generally when the VM is waiting for user input or when the VM has rush out of memory. In the latter case, the GC interrupts high-priority processing in the application.

    Some programmers like to expend the "-Xincgc" directive on the Java command line. This tells the JVM to accomplish garbage collection in increments when it desires. Again, the timing of the garbage collection may be inopportune. Instead, they intimate that the garbage collector accomplish a full garbage collection as soon as it can in either or both of two ways:1.  Request garbage collection to chance as soon as possible: This mode proves useful when the programmer knows he or she has a "break" to garbage collect. For example, after a big image is loaded into remembrance and scaled, the remembrance footprint is large. Forcing a garbage collection to occur at that point is wise. Another worthy locality may be after a big message has been processed in the application and is no longer needed.2.  Schedule garbage collection to occur at a fixed rate: This mode is optimal when the programmer does not Have a specific second when he knows his application can desist shortly and garbage collect. Normally, most applications are written in this manner.

    Listing 1 introduces a class named "BetterControlOfGC". It's a utility class that provides the methods described earlier. There are two public methods: "suggestGCNow()" and "scheduleRegularGC(milliseconds)" that respectively correspond to the steps described earlier. Line 7 suggests to the VM to garbage collect the unreachable objects as soon as possible. The documentation makes it limpid that the garbage collection may not occur instantaneously, but sustain has shown that it will be performed as soon as the VM is able to accomplish the task. Invoking the mode on line 25 causes garbage collection to occur at a fixed rate as determined by the parameter to the method.

    In scheduling the GC to occur at a fixed rate, a garbage collection stimulator task, GCStimulatorTask, is utilized. The code extends the "java.util.timer" thread in line 10. No fresh thread is created; the processing runs on the separate timer thread available genesis with the Java 1.3 environment. Similarly, to hold the processing lean, the GC stimulator follows the Singleton pattern as shown by lines 18-23 and line 27. There can be only one stimulator per application, where an application is any code running on an instance of the JVM.

    We intimate that you set the interval at which the garbage collector runs from a Java property file. Thus you can tune the application without having to recompile the code. Write some simple code to read a property file that's either a parameter on the command line or a resource bundle in the class path. dwelling the command parameter "-verbose:gc" on your executable command line and measure the time it takes to garbage collect. Tune this number until you achieve the results you want. If the budget allows, experiment with other virtual machines and/or hardware.

    Step 3: Store Persistent Objects into Persistent Data Areas or Store Long-Lived Objects in PoolsUsing persistent data areas is purely optional. It supports the underlying premise of this article. In order to bind the disruption of the garbage collector in your application, obtain its job easy. If you know that an demur or collection of objects would live for the duration of your application, let the collector know. It would be nice if the Java environment provided some sort of flag that could be placed on objects upon their creation to recount the garbage collector "-keep out". However, there is currently no such means. (The Real-Time Specification for Java describes an locality of remembrance called "Immortal Memory" where objects live for the duration of the application and garbage collection should not run.) You may try using a database; however, this may unhurried down your application even more. Another solution currently under the Java Community Process is JSR 107. JCache provides a measure set of APIs and semantics that allow a programmer to cache frequently used data objects for the local JVM or across JVMs. This API is noiseless under review and may not be available yet. However, they believe it holds much promise for the Java developer community. hold this avenue open and in intellect for future architectures. What can they enact now?

    The pooling of objects is not fresh to real-time programmers. The concept is to create outright your expected data objects before you commence processing, then outright your data can be placed into structures without the expense of instance creation during processing time. This has the edge of keeping your remembrance footprint stable. It has the disadvantage of requiring a "deep copy" mode to be written to store the data into the pool. (If you simply set an demur to another, you're changing the demur reference and not reusing the same space.) The nanosecond expense of the abysmal copy is far less than that of the demur instance creation.

    If the data pooling technique is combined with the proper expend of the "nullify" technique, garbage collection becomes optimized. The reasons are fairly straightforward:1.  Since the demur is set to null immediately after the abysmal copy, it lives only in the youthful generation portion of the memory. It does not progress into the older generations of remembrance and thus takes less of the garbage collector's cycle time.2.  Since the demur is nullified immediately and no other reference to it exists in some other collection demur in the application, the job of the garbage collector is easier. In other words, the garbage collector does not Have to hold track of an demur that exists in a collection.

    When using data pools, it's sane to expend the parameters "-XX:+UseConcMarkSweepGC -XX:MaxTenuringThreshold=0 -XX:SurvivorRatio=128" on the command line. These recount the JVM to flow objects on the first sweep from the fresh generation to the old. It commands the JVM to expend the concurrent note sweep algorithm on the ancient generation that proves more efficient since it works "concurrently" for a multi-processor platform. For separate processor machines, try the "-Xincgc" option. We've seen those long garbage collector pauses, which occur after hours of execution, vanish using this technique and these parameters. Performing well in the long rush is the actual capitalize of this last step.

    Performance ResultsTypically, most engineers want proof before changing their approach to designing and coding. Why not? Since we're now suggesting that even Java programmers should be concerned about resource allocation, it better be worth it! Once upon a time, assembly language and C programmers spent time tweaking remembrance and register usage to improve performance. This step was necessary. Now, as higher-level object-oriented programmers they may disdain this thought. This pattern has dared to imply that such considerations, although not as low plane as registers and remembrance addresses (instead at the demur level), are noiseless necessary for high-performance coding. Can it be true?

    The underlying premise is that if you know how your engine works, you can drive it better to obtain optimal performance and endurance. This is as actual for my 1985 300TD (Mercedes, five cylinder, turbo diesel station wagon) with 265,000 miles as for my Java code running on a HotSpot VM. For instance, knowing that a diesel's optimal performance is when the engine is warm since it relies on compression for power, I let my car warm up before I "push it." Similarly, I don't overload the vehicle with the tons of stuff I could dwelling in the tailgate. HotSpot fits the analogy. Performance improves after the VM "warms up" and compiles the HotSpot code into the native language. I likewise hold my remembrance footprint gaunt and light. The comparison breaks down after awhile, but the basic verisimilitude does not change. You can expend a system the best when you understand how it works.

    Our challenge to you is to grasp statistics before and after implementing this pattern on just a small portion of your code. please recognize that the gain will be best exemplified when your application is scaled upward. In other words, the heavier the load on the system, the better the results.

    The following statistics were taken after the pattern was applied. They are charted as:1.  Limited nullify mode invocation is used where only the incoming messages are not "nullified." (The residuum of the application from which the statistics were taken was left intact with a very gaunt remembrance usage.) There is no forced garbage collection.2.  Nullify mode invocation and forced garbage collection is utilized.

    The test environment is a Microsoft Windows 2000 X86 Family 15 Model 2 Stepping 4 Genuine Intel ~1794MHz laptop running the BEA WebLogic Server 7.0 with Service Pack 7.1 with a physical remembrance size of 523,704KB. The Java Message Server (JMS server), a track generator, and a tactical parade are outright running on the same laptop over the local developer network (MAGIC). The server makes no optimizations, even though each application resides locally. The JVMs are treated as if they were distributed across the network. They're running on the J2SE 1.4.1 release.

    The test target application is a Java sway Tactical parade with full panning, zooming, and track-hooking capabilities. It receives bundles of tracks via the Java Message Service that are displayed at their proper location on the given image. Each track is approximately 88 bytes and the overall container size is about 70 bytes. This byte measurement does not comprise outright the additional class information that's likewise sent during serialization. The container is the message that holds an array of tracks that contains information such as time and number of tracks. For their tests, the tracks are sent at a 1Hz rate. Twenty sets of data are captured.

    To illustrate the test environment, a screen capture of a 5,000 track load (4,999 tracks plus the ship) is shown in figure 1. The background shows tracks rendered with the Military measure 2525B symbology over an image of the Middle East. The small window titled "Track Generator Desktop" is a minimized window showing the parameters of the test set through the track generator application. Notice that 45 messages had been sent at the time of the screen capture. Directly beneath this window sits the Windows job Manager. Note that the CPU utilization is at 83%. At first this doesn't look that bad. But at that rate, there isn't much margin for the user to commence zooming, panning, hooking tracks, and so on. The final command window to the perquisite is that of the tactical parade application. The parameter "-verbose:gc" is placed on the Java command line (java -verbose:gc myMainApplication.class). The VM is performing the listed garbage collection at its own rate, not by command of the application.

    The final test of 10,000 tracks performed extremely poorly. The system does not scale; the CPU is pegged. At this point most engineers may jeer at Java again. Let's grasp another watch after implementing the pattern.

    After implementation, where the nullify methods are invoked properly and garbage collection is requested at a periodic interval (2Hz), stagy improvements are realized. The last test of 10,000 tracks proves that the processor noiseless has plenty of margin to enact more work. In other words, the pattern scales very well.

    Performance SummaryThe pattern to capitalize control garbage collection pauses most definitely improves the overall performance of the application. Notice how well the pattern scales under the heavier track loads in the performance bar chart in figure 2. The darker middle bar shows the processor utilization at each plane of the message (track) load. As the message traffic increases, the processor utilization grows more slowly than without the pattern. The last light-colored bar shows the improved performance. The main energy of the pattern is how well it scales under heavy message loads.

    There is another subtle energy to the pattern. This one is difficult to measure since it requires very long-lived tests. If Step 3 is faithfully followed, those horribly long garbage collection pauses that occur after hours of running disappear. This is a key capitalize to the pattern since most of their applications are designed to rush "forever."

    We're confident that many other Java applications would capitalize from implementing this very simple pattern.

    The steps to control garbage collection pauses are:1.  Set outright objects that are no longer in expend to null and obtain certain they're not left within some collection. "Nullify" objects.2.  constrain garbage collection to occur both:

  • After some major memory-intense operation (e.g., scaling an image)
  • At a periodic rate that provides the best performance for your application3.  deliver long-lived data in a persistent data locality if feasible or in a pool of data and expend the arrogate garbage collector algorithm.

    By following these three simple steps, you'll avoid those bothersome garbage collection pauses and esteem outright the benefits of the Java environment. It's time the Java environment was fully utilized in mission-critical parade systems.

    References

  • Gupta, A., and Doyle, M. "Turbo-Charging the Java HotSpot Virtual Machine, v1.4.x to improve the Performance and Scalability of Application Servers": http://developer.java.sun.com/developer/ technicalArticles/Programming/turbo/
  • JSR 1, Real-Time Specification for Java: http://jcp.org/en/jsr/detail?id=1
  • Java HotSpot VM options: http://java.sun.com/docs/hotspot/VMOptions.html
  • Java Specification Request for JCache: http://jcp.org/en/jsr/detail?id=107


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