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P2020-007 - IBM Optimization Technical Mastery Test v1 - Dump Information

Vendor : IBM
Exam Code : P2020-007
Exam Name : IBM Optimization Technical Mastery Test v1
Questions and Answers : 30 Q & A
Updated On : October 18, 2018
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P2020-007 Questions and Answers

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P2020-007 IBM Optimization Technical Mastery Test v1

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P2020-007 exam Dumps Source : IBM Optimization Technical Mastery Test v1

Test Code : P2020-007
Test Name : IBM Optimization Technical Mastery Test v1
Vendor Name : IBM
Q&A : 30 Real Questions

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IBM IBM Optimization Technical Mastery

IBM’s O’Connell and Currie discuss the way to contain Automation within the WAN | killexams.com Real Questions and Pass4sure dumps

In nowadays’s company mannequin transformations, community capabilities should be consumed in a flexible, agile, optimized, and economical manner. With its Multi-community WAN carrier (MWS), IBM hopes that automation above and across a variety of providers, aspect technologies, and digital features will tackle these client needs.

in this interview, IBM’s Brian O’Connell, unusual Engineer and master Inventor, and Steve Currie, unique Engineer, discuss IBM’s networking method, which has a laser focus on the organizations and functions of its consumers, in place of the normal strategy of focusing basically on the network itself. based on IBM, SD-WAN is not just a new know-how rollout however a way towards new consumption models.

SDxCentral: What are one of the crucial challenges that corporations are experiencing with the WAN?

Currie: I’ve been in networking for more than 30 years and software-defined networking (SDN) for the past 5 years, so I’ve viewed lots of market alterations going on over time. What’s wonderful is that the business upheaval we’re seeing these days is awfully an awful lot like what happened within the late Nineties, when applied sciences have been altering abruptly and enterprise fashions had been evolving. nowadays, our clients are asserting: “We deserve to radically change our company in a value-constructive manner, and We deserve to do it with greater bandwidth as a result of our functions are demanding it, and We need to do it all very securely.” So those types of adjustments within the industry are riding ordinary players to adopt new models and technologies, and giving new gamers chance to trigger disruption. overall, our purchasers are pushing difficult for flexibility, can charge effectivity, and protection.

O’Connell: Steve and i come from distinctive backgrounds. I’m a application engineer by way of change, and a lot of what I do focuses on automation. When it involves the challenges that enterprises are facing within the WAN, I approach it from the utility, utility engineering and DevOps viewpoint. The problem from the utility standpoint is that, historically, WAN and networking are often viewed as whatever of an inhibitor. as an example, you've got app builders pushing alterations out to production, working with web site reliability engineers to help accelerate adjustments and aid organizations meet their dreams, but the WAN and the networking are starting to trap up when it comes to automation and orchestration to assist swift deployment and swift alterations to purposes. The challenge amongst enterprise networking is to keep up with the swift adjustments that developers are pushing out.

Currie: a lot of what Brian is talking about speaks to flexibility and the need to be a lot more attentive to the utility. The work he’s doing in orchestration and automation will bring purposes more quickly to the WAN however also introduce more advantageous demands on the network, and that ends up in the want for superior capability and enhanced security as these applications are pushing things out. when we look at the want for potential, the questions are: “How am i able to scale bandwidth on demand greater quickly the usage of orchestration? How am i able to reply to these utility needs?” IBM’s point of view isn't always, this is a new technology and you should use it, but somewhat, How do I enable our consumers, their organizations, and their applications to satisfy our consumers’ wants and ambitions?  It’s more of a precise-down approach, considering that the application going into the community. It’s a metamorphosis in perspective. The software and the business are now able to drive the expertise to where clients want it. It’s a thrilling time!

what is IBM doing to clear up one of the vital issues you’ve mentioned?

O’Connell: The change is that IBM thinks of network transformation as more than just fresh the community with new gadget. We see SD-WAN as a complete transformation of the network. We consider about where we are able to force automation and how we can combine community automation into the broader orchestration and service automation dreams that a firm might have. the manner I suppose about it is that customers don’t easily automate their community as an conclusion purpose. They automate the community to permit features and to attain their patrons. Whereas traditional suppliers could have a network end view, we take the application view. We think about the situation in such a means that the network is there to enable the software and to enable the experiences that our clients need their clients to have.

Currie: And this business mannequin transformation that Brian is speakme about opens up terrific percentages for our purchasers so far as how they’re even capable of account for the cost of a network. “can i make it part of my facility charge for my department? am i able to make it part of my actual estate can charge?”  Now, At IBM, we’re extracting the functionality it really is effective to the utility and company, above the physical connectivity, and bringing in a brand new market and new company models so that the actual layer becomes a part of the real property and the physical wants. We’re now able to use distinct applied sciences on the base level whereas having consistent beginning of performance, of security, and of capacity at a better level. the base transport should be a mix of typical MPLS and broadband cyber web the usage of both business category or consumer grade in line with the company necessities and the geographic places. Our clients can gain can charge efficiencies in some parts of the area the use of customer grade broadband with adequate reliability and performance for their needs, while other areas need the steadiness of MPLS. global purchasers aren't any longer locked into a limited set of applied sciences by using SD-WAN solutions. The larger stage capabilities brings in network functions virtualization (NFV) and the way I’m in a position to deploy key functions on the correct vicinity within the community, services like firewall, intrusion detection and prevention, optimization, and business certain capabilities.

Many companies are given that a hybrid cloud approach. How does IBM’s SD-WAN solution aid a hybrid cloud ambiance?

Currie: at the connectivity stage, inside IBM SD-WAN, we’re utilising the right transport and the appropriate connectivity for specific functions and placement kinds. A client may be capable of use cyber web broadband, or one certain telecommunications issuer for MPLS, or another carrier’s MPLS functions—and they can combine different carriers together to achieve the premier insurance. And here is in particular important for global groups. It aligns very nicely with equal-nation shoppers, however as we get into foreign and multinational companies, having company flexibility offers them cost optimization, and it offers them the most appropriate technical solution with the appropriate insurance and the correct provider-stage objectives. It offers them greater manage over their spend and their capabilities within the WAN. That’s probably the most key aspects of what we’re doing with SD-WAN.

O’Connell: Our solution changed into built to help hybrid cloud from the very starting. Our global community peering aspects (GNPP) with backbone and regional nodes truly goes past aiding hybrid cloud. Our answer basically supports multi-cloud. loads of our customers are moving towards a multi-cloud method, and one element that’s awesome is the way it leverages our world network peering points is that we are able to readily facilitate that multi-cloud.

What makes IBM’s SD-WAN answer distinct from different WAN choices?

Brian O’Connell: IBM is offering the capacity to optimize between service suppliers in areas for a particular deployment. as far as rate, traditionally it’s most excellent to go along with between three and five provider suppliers per transport, for a selected deployment. as a result of IBM is transport company–agnostic, it lets us work with our consumers and deliver them the optimum cost for the SD-WAN solution. and since the GNPP is co-located with cloud suppliers and telecommunications partners, we get optimized conduct with elements of presence in the regions that our purchasers need. So, to me, it truly is the key differentiator: that transport company–agnostic view of multi-network WAN features delivered with automation.

Currie: On right of that, we take these providers and latest them as one service to our consumers. So, they don’t face the problem of managing distinct providers; we do this on their behalf. We’re doing all of the carrier assurance for them, monitoring availability and capacity, and working with providers on the underlying transport to give the appropriate tiers of service. Our customers don’t ought to cope with the entire complexity, so that they adventure a way of consolation and effectivity. And that ties into how we’re riding automation into all points of the carrier. As we see opportunities for gaining effectivity with automation, we’re applying that impulsively in a DevOps model. It’s using loads of what’s occurring within the business, however doing it in a cross-carrier, move-transport approach instead of simply within one certain infrastructure.

O’Connell: From my utility engineering history, I view it as a transport issuer abstraction in that we can install functions throughout diverse transport provider implementations, but the adventure is the same for the buyer. That’s a key differentiator—the journey, the billing, our capability to abstract working with those providers for those customers.

Describe some of the can charge benefits that firms will experience in the event that they migrate to IBM’s SD-WAN provider.

Steve Currie: We talked concerning the capability to use the correct expertise for the correct circumstance so that i can optimize in keeping with who has the gold standard presence and cost for our consumers in a particular place, country, or town—and use that to the competencies of our clients. It’s no longer all the time MPLS or traditional transport capabilities; it’s additionally about who’s the optimal broadband provider. If the client has availability concerns and wants varied suppliers or several types of technologies to give superior assurance, we are able to do this in a really reasonably priced means as a result of we will go to the appropriate broadband company and get the appropriate answer there, as well because the correct MPLS, and do a hybrid WAN solution with ease. It gives our customers a brand new negotiating point with the a lot of suppliers as a result of they could bring it together in one community for their business that can be managed securely on the appropriate carrier stages.

O’Connell: To me, it’s all concerning the capacity to leverage broadband cyber web connectivity; to steadiness the skill, availability, and protection necessities of specific purposes; and to summary that transport issuer implementation from the consumer in order that we are able to go into quite a few markets and examine distinct transport providers and judge the most reasonable company for that consumer’s needs.

Any last recommendations?

Currie: here is a massive possibility for our purchasers to take capabilities of quick trends within the WAN or networking house. This chance makes it possible for them to seriously change their IT business to power expertise to aid them rather than form them. historically, networks formed the manner business operated, however these days we are able to shift that perspective in such a method that the business can shape the community to most reliable tackle its market and leverage different applied sciences and capabilities to meet their necessities. The outcome is more desirable flexibility, stronger cost handle, and an more desirable protection posture. during this unexpectedly changing and strict regulatory ambiance, that safety attitude is standard to the enterprise transformation.

O’Connell: this is the most exciting time in networking that I’ve skilled. The total landscape is altering to utility-defined—each LAN and WAN. It’s a fine time for consumers to reevaluate what they want their community to seem like, and the way their network can enable their enterprise to fulfill the demands of their valued clientele.


IBM: a long Work-In-development | killexams.com Real Questions and Pass4sure dumps

No effect discovered, try new key phrase!however, looking from the technical charting standpoint ... the SVP and CFO of IBM, pointed out within the profits name that the company has been searching for to increase its "workforce optimization productivity ...

artificial Intelligence is Upon Us — Are We in a position? | killexams.com Real Questions and Pass4sure dumps

“Philosophically, intellectually — in each method — human society is unprepared for the upward push of artificial intelligence.”

Henry Kissinger, How the Enlightenment Ends

synthetic Intelligence (AI) is getting loads of attention these days, mainly within the expertise industry and in company boardrooms. AI is also becoming general in patrons regular lives. consumers don’t all the time admire it as such, as corporate advertising and marketing consultants prefer to evade technical jargon and as a substitute use client friendly names like Siri and Alexa — however for individuals which are more technically inclined, the ubiquitous presence of AI is hard to omit.

AI isn't a brand new concept. definitely, its roots go again a few many years. So why so a great deal buzz now? is that this just a different know-how hype that is going to fade, or does it in reality have the skills to bring on transformations, both respectable or dangerous, of epic proportions?

A old standpoint

Let’s take a look at how we came and why AI is abruptly capturing so lots consideration. we can revisit a little bit of the background of AI and the convergence of three increase vectors: Algorithmic advances, computing vigour, and statistics explosion. These vectors have their personal ancient landmarks as outlined beneath, until they converge around 2007, when the iPhone became first introduced.

the primary vector, algorithmic advances, goes back as far as 1805 when French mathematician Adrien-Marie Legendre posted the least rectangular method of regression which provides the groundwork for many of nowadays’s desktop-discovering fashions. In 1965 the architecture for machine deep-discovering the use of synthetic neural networks become first developed. Between 1986 and 1998 we noticed a couple of algorithmic advances: backpropagation, which allows for for optimization devoid of human intervention; image recognition; natural language processing; and Google’s famous PageRank algorithm.

The 2d vector, computing energy, had a major old landmark in 1965 when Intel cofounder Gordon Moore recognized the exponential growth in chip vigour: the variety of transistors per square inch doubles every year. This became called Moore’s law, and has correctly envisioned the doubling of computing energy every 18 months to the existing day and into the foreseeable future. at the time the state-of-the-paintings laptop became capable of processing within the order of 3 million FLOPS (floating-element operations per 2nd). via 1997, IBM’s Deep Blue accomplished 11 giga FLOPs (eleven billion FLOPS), which ended in its victory over Gary Kasparov, the world chess champion. In 1999 the graphics Processing Unit (GPU) became unveiled — a simple computing means for deep learning. In 2002 we noticed the introduction of Amazon’s web services (AWS) making computing power conveniently available and affordable through cloud computing. In 2004 Google launched MapReduce, which enables computer to contend with huge quantities of records by using parallel processing, resulting in the introduction of Hadoop in 2006, which allowed businesses to deal with the avalanche of information produced by way of the net.

ultimately, the third vector, statistics explosion, begun is 1991 when the all over web became made accessible to the public. in the early 2000’s we noticed large adoption of broadband, which opened the doors to many information superhighway improvements, ensuing within the debut of fb in 2004, and Youtube in 2005. at present, the variety of web users global surpassed a billion.

The yr 2007 grew to become a major landmark. it's at this aspect that the applied sciences begin converging and intercepting new horizons as the cellular explosion got here to lifestyles with Steve Job’s announcement of the iPhone in 2007. From right here a couple of significant advances provide beginning to a renewed enthusiasm for synthetic Intelligence. In 2009, Stanford tuition scientists confirmed they could train deep-belief networks with one hundred million parameters the usage of GPUs at a cost 70 instances sooner than the usage of CPUs. by way of 2010, 300 million smartphones had been bought, and information superhighway traffic reached 20 exabytes (20 billion gigabytes) per 30 days.

In 2011 a key milestone changed into accomplished. IBM Watson defeated both superior Jeopardy champions, Brad Ruttner and Ken Jennings. Such fulfillment changed into made feasible via IBM servers capable of processing 80 teraFLOPS (80 trillion FLOPS). remember that after Moore’s legislation became reported within the mid 60’s, the most powerful computer could only system three million FLOPS.

by way of 2012 giant development had been made in deep studying for photograph recognition. Google used 16,000 processors to educate a deep artificial neural community to respect photos of cats in YouTube videos with out featuring any information to those machines in regards to the photographs. Convolutional Neural Networks (CNN) became in a position to classifying images with a excessive diploma of accuracy. meanwhile, the information explosion endured, with the number of mobile gadgets on the earth exceeding the variety of humans, producing 2.5 quintillion bytes of records per day by means of 2017. Computing power reached new heights as Google introduced its Tensor Processing instruments (TPU) able to a hundred and eighty million teraFLOPS.

it is at this element in the heritage of artificial Intelligence that many americans began to know we might no longer be too far from achieving, or even exceeding, what's known as synthetic frequent Intelligence (AGI). To the astonishment of the world, Google’s DeepMind hit another primary milestone when its AlphaZero algorithm discovered to play by itself the games of chess, shogi and Go (Go is a very complicated game, tons greater difficult than chess). now not handiest did AlphaZero learn to play by using itself, it defeated the top of the line computer systems that had been fed instructions from human specialists! And it did this in exactly eight hours of self-play!

AlphaZero, through itself, came up with taking part in ideas that humans had under no circumstances concept of earlier than — it's a significant event that would clarify why there is so plenty buzz about AI at this factor in time.

Human vs. computing device Intelligence Milestones

Has Human Intelligence Already Been Defeated by using Machines?

these milestones, that you may’t aid but ask yourself if human intelligence has already been defeated my laptop intelligence. The reply is not any. There are certain areas through which machines seem to have defeated the human brain, however there's a whole lot more to human-stage intelligence than the narrow applications through which human brains had been defeated. The stage of complexity and capacity of the human mind is nowhere close being matched by using computer systems — at the least no longer yet.

The human mind is a good, wondrous and mysterious creation. Princeton researchers have proven that people make judgements about other humans inside a hundred milliseconds of assembly them. through a procedure that scientists name skinny cutting, people make judgmental selections about others akin to no matter if they're faithful, smart, high-status, homosexual or straight, a success, adventurous, and greater. All in less than a 2nd.

Even a three-12 months-old newborn is capable of making visual identifications that nowadays’s most sophisticated tremendous-powered computing device algorithms are nowhere near able to matching.

The (small) Elephant within the Room

To prove the factor, a brand new analyze discovered that synthetic intelligence techniques fail a vision test that a child may accomplish without difficulty. The study presented a synthetic clever device a living room scene which the gadget processed neatly, recognizing images similar to a chair, a sofa, a television, and many others. however when the scientists delivered an anomalous object into the scene — the photo of an elephant, the gadget grew to become very ‘confused’ and made a number of blunders, choosing a chair as a couch, and fully lacking other objects that it had identified before.

“There are all types of weird issues going on that show how brittle existing object detection methods are,” pointed out Amir Rosenfeld, a researcher at York tuition in Toronto and co-writer of the examine along with his York colleague John Tsotsos and Richard Zemel of the school of Toronto.

The problem is as a result of the laborious methods during which synthetic Intelligence processes visual impressions, one layer at a time. contrast this to how the human mind takes in a dazzling amount of advice without delay and methods it instantaneously. “We open our eyes and everything happens,” observed Tsotsos.

Critics of the ‘over-hyping’ of synthetic Intelligence are quick to aspect out these flaws and many others. They pronounce that the AI revolution hasn’t came about yet. They remind us that the time period artificial Intelligence as it was coined within the late 1950’s to refer to the heady aspiration of realizing in utility and hardware an entity possessing human-degree intelligence, may still now not be used interchangeably with desktop gaining knowledge of. computing device discovering is an algorithmic container that blends concepts from information, laptop science and different disciplines to method information, make predictions and assist make choices — however does equate to human-level intelligence.

It is apparent that we may well be misusing the time period synthetic Intelligence because it become initially supposed and that we may be making use of it too commonly to discuss with a lot less ready technologies. it's additionally clear that machines are nowhere close the level of capability of the human mind.

besides the fact that children, we will’t ignore the astonishing rate of improvement accompanied within the closing few years. Most scientists agree with that the artificial Intelligence challenges faced today can be conquered sooner than we suspect. basically, most experts agree that we'll achieve human-stage synthetic intelligence this century, many believing it'll happen as quickly as in the next 20 to 40 years.

Intelligence Explosion

to be able to keep in mind the challenges that lay forward, we must first seem at the distinct classes of synthetic Intelligence:

1) artificial slim Intelligence (ANI) — you could think of ANI as a really good intelligence. for example, the algorithms of a search engine, herbal language processing, self-using vehicles, Siri, and even the self-learning machines that mastered the game of Go.

2) synthetic established Intelligence (AGI) — This refers back to the human-stage intelligence that the normal synthetic Intelligence time period become coined after. developing AGI is a a good deal harder assignment than growing ANI, and we are, at this element in time, no longer even close to attaining it. This level of intelligence requires a extremely general cognitive capacity that, amongst different issues, contains the capacity to cause, plan, remedy complications, consider abstractly, realise advanced concepts, study immediately, and gain knowledge of from adventure.

3) artificial Superintelligence (ASI) — Nick Bostrom, writer of the ebook Superintelligence: Paths, hazards, innovations, defines superintelligence as “an mind it is a whole lot smarter than the optimal human brains in well-nigh each container, together with scientific creativity, accepted wisdom and social advantage.” An ASI agent can be trillions of times smarter than human beings. that's the place issues get very scary. To make matters worse, as soon as AGI is done, ASI may also not be too a long way behind.

As we have exemplified above, ANI is already right here. AGI, however, remains just a sparkle in a scientist’s eye. As we have viewed within the ancient progression of artificial Intelligence, computational energy has at all times been a pre-requisite to the next stage of growth. attending to AGI is not any distinctive. To offer you a sense of the current state of affairs, scientists have estimated that the human brain is able to approximately 10 quadrillion computations per second (cps). the world’s fastest laptop, the Tianhe-2 from China, has already crushed this number, because it can do about 34 quadrillion cps. despite the fact, the Tianhe-2 is an immense beast that costs $390 million to build.

Ray Kurzweil, a futurist who is terribly positive about synthetic Intelligence, estimates that AGI can become common when we will get 10 quadrillion cps for about $1,000. using Moore’s legislations which, as we now have viewed, has correctly anticipated the growth of computing vigour to date, it's estimated that we may be there through 2025, most effective 7 years away.

but computing vigour is only one prerequisite. so as to obtain AGI, we nonetheless need to determine a way to create human-level intelligence. How are going to get there? Scientists are engaged on reverse engineering the human mind. The techniques have already been developed. basically, we are able to emulate a 1 mm lengthy flatworm mind with 302 neurons nowadays. Now we simply deserve to work out the way to scale this approach to one hundred billion neurons, the approximate dimension of the human brain. There are positive estimates that we'll obtain this with the aid of 2030.

There are also efforts to build computer systems that concentrate on AI research. That capacity that the computing device themselves will turn into smarter at every generation of growth, except AGI is accomplished. This may turn up very soon. remember that computers can function at exponential increase, something that it's difficult for us humans to understand. What seems to be progressing very slowly might all of sudden explode into truth.

as soon as we reach AGI, given the equal iterative progression on an exponential curve, it isn't tough to foresee that ASI will arrive almost immediately after. The intelligence explosion will then reach a degree that we will’t even have an understanding of — and therein lies the hazards.

people have dominated the earth because of our intelligence. it is effortless for us to look that with intelligence comes vigor. as soon as we create an ASI agent it really is vastly more clever than we are, what might be our fate?

here's a extremely problematic question to contemplate. Let’s study ways in which we may additionally are attempting to cope with Superintelligence and have an impact on its results.

living with Superintelligence

If the creation of Superintelligence is inevitable, do we manage it that will behave in ways which are really helpful and never detrimental to humanity?

Nick Bostrom, in his ebook Superintelligence: Paths dangers recommendations, describes a couple of control methods, all of which have their personal sets of knowledge advantages and shortcomings.

Boxing — This could be one of the vital obvious manage mechanisms that comes to intellect. you place the ASI agent in a confinement where contact with the external world is limited by people. If it misbehaves you close it down. do we expect that this Superintelligent agent won’t outsmart us in discovering a method to escape if it is motivated to achieve this?

Incentive methods — We might build an ASI in such a means that its behaviors are managed with the aid of suggestions, or incentive mechanisms. youngsters, if the ASI obtains a ‘decisive strategic talents’, prison and financial incentives and sanctions can also not be adequate to have an impact on its conduct. The ASI may additionally internalize and improve its own norms and ideologies and regard these human-made suggestions as meaningless.

Stunting — here is a technique wherein you impose constraints on the ASI’s cognitive capabilities. This approach has three primary flaws: First, limiting the capabilities of an algorithm would be counter-productive; 2nd, it's not likely that we are able to resisting the motives to build fully able ASIs; Third, how do we define the brink of cognitive means and get rid of human biases? Set it incorrectly and it might work out via itself the way to become more clever and pass the boundaries imposed by using people.

Tripwires — This comprises establishing a collection of tests, probably devoid of the ASI’s competencies, to observe any negative conduct or intent, and upon detection shut it down. You could, for example, try to detect if the ASI contained in a field is making an attempt to escape by means of establishing an internet connection. The problem right here goes again to the fact that you are coping with a Superintelligent agent who may conveniently subvert any tripwire devised by using the human mind.

control methods, as defined above, may have constrained effectiveness in fighting ASIs from behaving in undesirable methods. however what if we could take a special method to the issue and take a look at to avoid undesirable outcomes by shaping what the ASIs want to do? here's referred to as Motivation preference.

There are distinct strategies inside Motivation selection, as described beneath. again, they have their own units of expertise advantages and shortcomings:

Direct Specification — This strategy tries to explicitly outline a collection of suggestions or values that might be used as a compass by the ASI. This can also appear easy, until you consider what set of rules and values we would hope the ASI to be guided with the aid of. And even though we acquired that completely appropriate (which is enormously not going) how would we go about expressing these guidelines and values in desktop-readable code?

Domesticity — This approach is designed to severely limit the scope of the ASI’s ambitions and activities. here we'd need to define what appropriate barriers to use, in order that the ASI would nonetheless be helpful, however best to the extent that it minimizes its have an impact on on the realm. once more, we run into the identical challenge raised with the stunting handle method of the way to withstand the reasons to create a greater powerful ASI.

indirect Normativity — this is an enchanting method, however as we are able to see later, raises many moral questions. The theory here is that as an alternative of specifying the suggestions that e-book the ASI’s habits at once, you define the process during which the rules might be derived. In other words, as a substitute of telling the ASI what to do, tell it to move determine what we might have told it to do had we standard improved, and put into effect that. we are able to discuss this extra when we overview the thought of ‘extrapolating our volition’.

Augmentation — during this strategy, as an alternative of developing a new motivation gadget, we delivery with an current one and enhance its cognitive potential to make it Superintelligent. we're talking about brain emulations and organic enhancements. will we, people, have the correct motivation methods to start with? despite the fact that the reply is effective, what prevents it from becoming corrupt because it receives superior? And do we in fact need to are living in an international where ‘brains in a field’ with full human recognition are copied, manipulated and discarded for scientific purposes?

in case you haven’t realized yet the depth of the moral dilemmas that are about to hit us in the following few decades, you might also delivery to get a way for it now. in addition to confronting the perplexing complications of manipulating the mind while conserving human dignity, we are starting to question complete price and motivation systems.

The (massive) Elephant in the Room

As discussed, the way to manage the habits of an ASI are restricted in effectiveness. the most promising strategy seems to be the use of Motivation choice to design an ASI this is seeded with the ‘correct’ values to being with. however what are the ‘correct’ values? And who decides?

If we, with all of our flaws, are incapable of seeding the values as a way to force the habits of a more clever being, should we go away it as much as this sophisticated being to verify its personal set of values to power our behaviors?

Eliezer Yudkowsky, an American Researcher who's popularizing the theory of a friendly AI, has proposed seeding the AI with what he calls our ‘Coherent Extrapolation Volition’ (CEV). right here is how CEV is described via Yudkowsky:

“Our coherent extrapolation volition is our desire if we knew extra, notion sooner, had been greater the americans we wish we had been, had grown up farther collectively; where the extrapolation converges in place of diverges, where our desires cohere in place of intrude; extrapolated as we want that extrapolated, interpreted as we want that interpreted.”

Dissecting the which means of CEV is beyond the scope of this article. besides the fact that children, what Yudkowsky is trying to do is to come up with a morality model that encapsulates moral growth however keeps humanity sooner or later in can charge of its own fate.

There are different morality fashions which are proposed, but the simple question is this: What ethical values will we need to seed the AI with? If we don’t comprehend, are we relaxed letting an all-powerful algorithm assess the ethical values which will e-book humanity for the relaxation of its existence?

Conclusion

we've some truly large and bushy considerations in front of us. We don’t know how we will handle these difficult questions, but the time to delivery these conversations is now. As we cited past, AGI, and almost immediately after, ASI, is likely going to be a huge part of our fact in a brief few many years.

what number of congressmen do you know who have a pretty good grasp of the concerns above? what number of CEOs are thinking about how values book the decisions of their groups, and the way those values may affect the conduct of desktop Superintelligence?

If we don’t beginning addressing these considerations now, we can also run out of time. The penalties are unfathomable.

informed studying:

- How the Enlightenment Ends by means of Henry Kissinger

- The AI Revolution: The road to Superintelligence by using Tim urban

- An government’s guide to AI by McKinsey & enterprise

- Superintelligence: Paths, hazards, suggestions by means of Nick Bostrom

in the beginning posted at www.interceptinghorizons.com.


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Mergers and Acquisitions 25.7% Strategic Sales 22.2% Change Management 18.7% Civil Engineer Prolog 9.6% ANSYS 7.6% Business Development 6.3% Claims Adjuster / Appraiser / Examiner / Investigator Verbal Communication 9.3% Construction Estimating 7.7% Complex Litigation Case Management 7.1% Clergy People Management 3.3% Bereavement Counseling 2.0% Patient Counseling 1.3% Occupation Top 3 Skills/Specialties Pay Boost Clinical Laboratory Technologist / Technician Budget Management 7.1% Medical Terminology 5.3% GMP Compliance 4.6% Compensation and Benefits Manager ADP Payroll System 8.2% Payroll Tax Compliance 7.5% Accounting 6.7% Compensation, Benefits, or Job Analysis Specialist Payroll Tax Compliance 10.4% ADP Human Resources System 7.5% 401k Plan Administration 6.7% Compliance Officer Data Analysis 3.6% Research Analysis 3.5% Project Management 3.5% Computer and Information Analyst Big Data Analytics 11.8% Agile 11.3% IBM WebSphere 10.1% Computer and Information Research Scientist 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Financial Analysis 3.7% Mortgage Loans 3.4% Database and Systems Administrator / Network Architect Cisco UCCE/IPCC 20.8% Puppet 20.7% Software Development 17.3% Dental Hygienist Clinical Education 6.4% Anesthesia 6.0% Software Knowledge 5.1% Dentist Oral Surgery 10.8% Medicine / Surgery 5.8% Public Health 0.2% Designer Autodesk AliasStudio 9.0% NX Unigraphics 8.4% Powerpoint 6.9% Occupation Top 3 Skills/Specialties Pay Boost Desktop Publisher Adobe Illustrator 3.7% Adobe Photoshop 3.2% Technical Writing 2.1% Detective / Criminal Investigator Geographic Information Systems 6.4% Forensic Interviewing 4.8% Signals Intelligence 4.6% Diagnostic Related Technologist or Technician Obstetrics 9.7% Interventional Radiology 4.0% Pediatrics 3.7% Dietitian / Nutritionist Training 9.2% Diabetes Educator 2.9% Nutrition Support 1.7% Drafter CATIA 8.7% AutoCAD MEP 6.0% Autodesk Revit Structure 5.9% Economist Policy Analysis 5.5% Data Modeling 5.0% Statistical Analysis 2.5% Education Administrator Strategic Marketing 7.7% Operations Management 4.6% Strategic Planning 3.7% Electrical or Electronics Engineer Instrument Control 6.4% Engineering Design, Semiconductor 6.2% AutoCAD MEP 5.9% Elementary or Middle School Teacher Spanish Language 3.4% Curriculum Planning 1.9% Childhood Education 1.3% Engineering Technician C (programming language) 14.1% Industrial Hydraulics 10.3% Distributed Control Systems 9.7% Environmental Engineer Title V Permitting 14.6% Windows Operating System General Use 2.4% Project Management 2.4% Environmental Scientist / Geoscientist Data Modeling 3.7% Regulatory Compliance 2.3% Project Management 2.3% Farmer / Rancher / Agricultural Manager Budget Management 13.2% Operations Management 8.6% Machine Operation 0.6% Financial Analyst / Advisor Valuation 9.0% Due Diligence 8.7% Market Research 8.1% Financial Examiner Financial Applications 7.8% Anti-Money Laundering 6.4% Microsoft Word 3.7% Financial Manager Business Strategy 14.0% Sarbanes-Oxley Compliance Audit 13.1% Sarbanes-Oxley Knowledge 10.4% First-Line Supervisor of Office and Administrative Support Workers Facility Maintenance & Coordination 15.8% Process Improvement 15.7% Contract Management 12.1% First-Line Supervisor of Sales Workers Profit & Loss Statements 12.3% Budget Management 6.8% Human Resources 6.6% General or Operations Manager Field Sales 18.2% Mergers and Acquisitions 16.0% Tax Consulting 14.0% Health Practitioner Support Technologist or Technician Oncology 8.1% Open Heart 5.7% Long-Term Care 4.3% Occupation Top 3 Skills/Specialties Pay Boost Health Teacher (Postsecondary) Critical Care 5.2% Emergency Room 3.2% Clinical Education 2.6% Human Resources Manager Strategy 11.0% Change Management 10.8% Applicant Tracking System 9.0% Human Resources Worker Legal Compliance 14.7% Payroll Tax Compliance 10.5% Strategic Planning 9.7% Industrial Engineer Chemical Process Engineering 17.0% Linux 8.3% Operations Management 6.6% Industrial Production Manager Six Sigma Black Belt 11.3% Six Sigma Green Belt 8.7% Engineering Design 7.9% Instructional Coordinator Design 3.6% Training Program Development 2.5% Articulate – E-Learning Software 2.4% Insurance Sales Agent Field Sales 14.3% Leadership 6.1% Account Sales 2.6% Lawyer or Judicial Law Clerk Mergers and Acquisitions 23.8% Corporate 15.7% Tax Compliance 9.8% Librarian Legal Research 10.5% Budget Management 3.9% Operations Management 1.6% Licensed Practical or Licensed Vocational Nurse Nursery 6.8% Triage 5.3% Intensive Care Unit 4.1% Logistician Project Management 6.5% Logistics Support Analysis 6.0% SAP Enterprise Resource Planning 5.7% Management Analyst Big Data Analytics 17.7% Mergers and Acquisitions 16.7% Strategy 15.6% Market Research Analyst or Marketing Specialist Financial Modeling 28.2% R (programming language) 16.5% Tableau Software 13.6 Marketing or Sales Manager Software as a Service 11.8% Marketo 11.7% Vendor Management 10.3% Materials Engineer Robotic Welding 4.2% Manufacturing Process Engineering 3.7% Material Science 3.4% Mechanical Engineer SCADA 6.1% SQL 5.5% Simulation 5.4% Medical or Health Services Manager Business Development 12.2% Six Sigma Black Belt 9.3% Surgery 9.2% Medical Records and Health Information Technician Microsoft Excel 7.3% Data Analysis 5.2% Microsoft Office 4.9% Medical Scientist Oncology 9.1% Molecular Biology 6.1% Generalist Duties 5.8% Medical, Dental, or Ophthalmic Laboratory Technician Wound Care 2.8% CAD/CAM 1.8% Casting 0.6% Occupation Top 3 Skills/Specialties Pay Boost Meeting, Convention, or Event Planner Strategic Marketing 9.4% Project Management 6.1% Budget Management 6.0% Natural Sciences Manager Engineering Design 8.0% Technical Writing 5.9% Problem Solving 4.0% News Analyst, Reporter or Correspondent Financial Reporting 10.6% Video Editing 1.8% Social Media Optimization 0.7% Nuclear Engineer Project Management 5.6% Engineering Design 3.6% Technical Analysis 2.5% Nurse Anesthetist Obstetrics 9.1% Pediatrics 6.8% Obstetrical Anesthesia 6.2% Nurse Practitioner Emergency Room 6.9% Dermatology 5.3% Pain Management 4.1% Occupational Health and Safety Specialist or Technician Risk Management / Risk Control 5.2% Regulatory Compliance 4.7% Microsoft Word 3.8% Operations Research Analyst R (programming language) 11.9% Automation Scripting 10.0% Statistical Analysis System 9.1% Paralegal or Legal Assistant Prosecution 10.8% Data Analysis 10.7% Concordance Discovery Management Software 10.6% Petroleum Engineer Microsoft Excel 7.8% Well Production Engineering 6.3% Simulation 4.1% Pharmacist Operations Management 6.4% Oncology 4.0% Critical Care 3.8% Physician Assistant Emergency Medicine 9.4% Psychiatry 6.5% General Surgery 5.6% Physician or Surgeon Obstetrics 17.8% Critical Care 15.8% Emergency Medicine 12.9% Podiatrist Surgery 7.2% Medicine / Surgery 1.7% Wound Care 1.6% Property, Real Estate, or Community Association Manager Financial Analysis 8.6% Negotiation 7.7% Budget Management 6.4% Psychologist Health and Wellness 6.3% Pain Management 5.0% Clinical Supervision 3.3% Public Relations or Fundraising Manager Strategic Planning 6.7% Branding 5.0% Corporate Communications 4.5% Purchasing Manager Business Analysis 10.7% Product Development 10.2% Strategic Planning 8.7% Real Estate Broker or Sales Agent Property Management 4.3% Customer Relations 1.5% Leasing Software 1.3% Registered Nurse Trauma Intensive Care Unit 4.7% Open Heart 4.4% Pediatric Intensive Care Unit 3.9% Sales Engineer IT Security & Infrastructure 22.2% Cisco Networking 15.5% Field Sales 9.1% Sales Representative (Wholesale and Manufacturing) Strategic Planning 10.1% Financial Analysis 10.0% Business Analysis 8.0% Secondary School Teacher Leadership 3.2% Information Technology Support 2.7% Mathematica 1.3% Secretary or Administrative Assistant Event Management 12.3% Information Technology Support 11.9% Benefits Administration 8.7% Securities, Commodities, or Financial Services Sales Agent Investment Management 11.2% Financial Analysis 5.4% Business Development 2.8% Social or Community Service Manager Clinical Supervision 8.4% Budget Management 7.5% Counseling 7.0% Occupation Top 3 Skills/Specialties Pay Boost Social Worker Utilization Review 18.1% Hospice Care 14.4% Oncology 9.8% Sociologist Research Analysis 5.5% Legislative Policy 4.4% Policy Analysis 3.5% Software Developer or Programmer Machine Learning 25.2% Scala 21.5% Algorithm Development 18.9% Statistical Assistant PL/SQL 13.7% Financial Modeling 12.3% R (programming language) 11.8% Statistician Data Modeling 7.9% Data Mining / Data Warehouse 5.0% SQL 4.3% Survey Researcher Financial Modeling 28.2% R (programming language) 16.5% Tableau Software 13.6% Surveying or Mapping Technician Computer Aided Drafting & Design 5.1% Global Positioning Systems 4.7% AutoCAD 2.9% Surveyor, Cartographer, or Photogrammetrists AutoCAD Civil 3D 4.8% Global Positioning Systems 2.6% Land Surveying 2.5% Tax Examiner, Collector or Preparer, or Revenue Agent Legal Research 9.1% Tax Consulting 3.2% Auditing 3.1% Teacher Assistant Spanish Language 3.2% Curriculum Planning 1.3% Activities for Children 0.1% Television, Video, or Motion Picture Camera Operator or Editor Adobe InDesign 8.5% Project Management 5.0% Microsoft Office 2.0% Teller Account Sales 11.0% Sales Management 8.6% Operations Management 5.8% Therapist Geriatrics 3.4% Home Care 3.2% Home Health / Home Care 2.7% Training and Development Manager Strategic Planning 13.1% Change Management 8.9% Leadership 6.2% Training and Development Specialist Clinical Education 14.4% Project Coordinating 6.8% Leave of Absence Administration 6.6% Transportation, Storage, or Distribution Manager SAP Materials Management 10.1% Risk Management / Risk Control 6.3% Budget Management 5.8% Urban or Regional Planner Project Management 6.5% Adobe InDesign 4.7% Graphic Design 3.7% Veterinarian Anesthesia 5.0% Emergency Medicine 4.3% Phlebotomy 3.7% Writer or Editor Advertising 6.5% Strategic Marketing 5.5% Dreamweaver 5.0%

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 we call “CreativeAI”. This document is the first chapter of our adventure into CreativeAI, aiming at establishing a backstory and language we can use to talk about this intricate subject. Our initial intuition was, that creativity is a central force throughout human history — and is currently evolving in interesting ways. In our attempt to understand this phenomenon, we think about creativity and technology in a structured way. We 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, we 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
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    1. Creativity It is central to the human condition and takes many forms in our 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 our 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. We explore three metaphors: Augmented Creativity, Computational Creativity and Creative Systems. Augmented Creativity

    In “As We 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 we do thinking, representation and association in our 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 our 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 we 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 our 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, we explored the long, intertwined history of science, technology and creativity. In this process we investigated three metaphors for creativity: Augmented Creativity, Computational Creativity and Creative Systems. In the following sections, we 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. We 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, we 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 we 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, we 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 we have named “the democratisation of creativity”. We 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, we can start to imagine a scenario we 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 we should ask ourselves when talking about the democratisation and escalation of creativity is “Are we 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 we shape our tools, thereafter they shape us” [12]. Nonetheless, we 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?”. Our 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 our 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 our capabilities. It is the notion of raising the collective human potential, not replacing it. To analyse this notion further, we refer to a framework introduced by NASA, for thinking about autonomy: The H-Metaphor [13]. It proposes to view our interactions with systems more as we 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. We can use this to let go sometimes and be casual about our 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 our collective potential.

    While a range of dystopian outcomes can easily be imagined, we 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 our 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. We 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 our 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”, we 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: we 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, we 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, we 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. We call this:

    Artificial Synesthesia: Systems that enable inter-sensory experiences.

    Democratisation and Escalation

    The Generative age gives us a new canvas for creativity, which we 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, we arrive at a scenario we 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, we 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, we 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 our 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, we have tried to think about creativity and technology in a structured way. This has allowed us to recognize, analyze and define emerging creation patterns. We focused on two general types: Assisted Creation and Generative Creation. Together, these patterns are leading to a vision we 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 our research, we 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, we 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


    How to win at the digital transformation of Procurement? | killexams.com real questions and Pass4sure dumps

    “Procurement-as-a-platform” is what digital Procurement is about!

    All the companies I mentioned at the beginning of this article share the same operating model. This model has been brilliantly described by Aaron Dignan:

    His model (Purpose, Process, People, Product, and Platform) is interesting as it extends the classic People, Process, and Technology (or Talent, Transition, and Technology — How Do We Drive Technological Advances? Part I, Sourcing Innovation). It also puts front and center crucial aspects unique to the full potential of a true digital transformation.

    “[Companies like Amazon, Airbnb, and Uber] aren’t just market matchmakers using data-driven algorithms to drive better buyer-seller matches; they invest in new value creation. In platform markets, cultivating user capability becomes as strategically important as reducing transaction costs. Successful platforms empower their users”. The Best Platforms Are More than Matchmakers, Harvard Business Review.

    Envisaging Procurement as a platform brings together, in a coherent and consistent “package” several characteristics and attributes that will contribute to Procurement’s survival and/or to elevating its role to a strategic one. It also creates the foundations required to be more adaptable and responsive to trends in the modern economy and society.

    For example, in another report, he Hackett Group lists the Five Imperatives for Creating Greater Procurement Agility:

  • Reallocate resources from transactional focus to value adding
  • Embrace digital transformation
  • Leverage analytics-based decision making
  • Adopt stakeholder/customer centric service design and delivery
  • Re-skill the Procurement function
  • PaaP addresses all five because platform-based operating models:

  • remove friction and focus on the object of the “transaction” by empowering and engaging parties to get and deliver more. They embed the principle that efficiency fuels effectiveness.
  • will also enable Procurement to support the digital transformation of the whole company.
  • can not exist without data (and a lot of data) as it fuels their recommendation engines and is the base of tailoring experiences to the specific needs of each user.
  • are a means to get access to more and, potentially, on-demand expertise by changing the way the workforce is managed and delivery channels (Procurement-as-a-Service, contingent workforce, gig economy…)
  • Also, looking at Procurement as a platform is coherent with the peculiar role that technology plays in today’s digital and physical business world.

    “The effect of digital technologies is to blur the distinction between creating value in the tangible world of atoms and creating value out of the data that comes from sensing and controlling that tangible world”. Digital to the Core: Remastering Leadership for Your Industry, Your Enterprise, and Yourself, Graham Waller & Mark Raskino.

    Purpose: empowered relationships as competitive advantage

    As a response to a world in constant change, long-term relationships between customers and suppliers in the supply chain is emerging as a critical way to get competitive advantages. It is what I described as SRM²:

    PaaP allows organizations to translate the SRM² principles into practice. It is because to function, platforms rely on:

  • an excellent understanding of each party (stakeholder/supplier) through data
  • a laser focus on delivering value (the definition of what the value is being variable from company to company and from category to category)
  • generating growth (reduction of waste, new markets, innovation…) by enabling participants to realize their full potential
  • This is what creates adherence and adoption.

    It is important to remember that it is not just about digital solutions. PaaP is about the whole experience of working with (and in) Procurement. It encompasses all the various touchpoints in the Procurement processes: the digital ones and the physical ones. Another key characteristic of PaaP is to deliver a first-class and experience (digital and in real life) removed of frictions, of unnecessary bottlenecks, and of unnecessary intermediaries.

    Process: experience, journeys, and touchpoints

    “Speed and access change everything. [M]assive organizations are feeling intense pressure to innovate, as unencumbered startups take shots across their bows. Legacy processes that enforce bureaucracy, command-and-control structures, waterfall development, and risk management are still largely the standard among big corporations, yet they are liabilities in this fight”. The Operating Model That Is Eating The World, Aaron Dignan.

    In many organizations, people complain about the technology that they use at work (and not just Procurement technology) and say “It should be as easy as Google or Amazon.” This explains why technology providers describe their offering to be Google-like or Amazon-like as they are what people often use as benchmarks. This shows that polishing and simplifying the design of the experience (the term experience covers the process and the technology that it supports) is important. (But, it would be a misunderstanding of the psychology of change to believe that it is the only aspect that matters for long-term adoption).

    “There can be little question that the relatively mundane, repetitive jobs in today’s Procurement functions will disappear. Indeed, that extends to many jobs that SHOULD be mundane and repetitive and are only ‘complex’ because we choose to make them so (and it should be noted, this is an issue that is endemic to business activity, in no way specific to Procurement)”. Procurement — it’s time for a re-birth, Tim Cummins, CEO of the International Association for Contract & Commercial Management (IACCM).

    As technology usually follows process, blaming technology to be too complex is very often a reject of the process itself. And, as the quote above illustrates, processes became complex by design (maybe not consciously, though).

    So, Procurement has to also move to an experience-based approach to designing its processes. Like it is the standard practice on the sales/marketing side of business. To do so, it should not only aim at automating current processes but at obliterating them:

    “Companies tend to use technology to mechanize old ways of doing business. They leave the existing processes intact and use computers simply to speed them up.” Reengineering Work: Don’t Automate, Obliterate, Harvard Business Review.

    Striving to remove frictions changes the perspective. Instead of continuing to do the same as before (and doing most probably like all others — the so-called best practices), it encourages to look at doing things that were previously impossible.

    “Challenging people to visualize processes as platforms completely changes how trade-offs between innovation, optimization, and user experience are debated and discussed”. Instead of Optimizing Processes, Reimagine Them as Platforms, Harvard Business Review.

    Here are a couple of examples of processes illustrating a user-experience based approach.

    - Touchless:

    The first example that comes to my mind is what Amazon did with Amazon Go.

    Beyond the buzz and technobabble, Amazon Go should provoke the interest of Supply Chain professionals.

    By the way, it is “just” taking the use of vending machines (for MRO, for office supplies, or other consumables) a step further.

    “We can start to imagine all those occasions and processes where the administration of stuff being handled, moved, bought, consumed could be made “touchless” — no need for the humans involved to do” anything consciously in order for the parties involved to know that the activity has taken place”. Amazon Supply Chain Innovation — Just Help Yourself!, Spend Matters UK

    Compare the seamless experience of Amazon Go with a typical consignment process! Now, imagine what that consignment process could be if it was designed in the same spirit as Amazon Go. The design of the experience is centered on “growth”. Meaning that it enables parties to mutually realize their potential and remove the superfluous from the foreground and make it happens behind the scenes.

    - Assistants and chatbots:

    Another illustration of entirely rethinking processes as experiences, journeys, and touchpoints to make them more efficient and effective (and in par with mainstream ones) is what I called a Procurement assistant:

    The idea behind the assistant is what the technology world is currently doing (Siri, Cortana…). Chatbots and conversational commerce are hot topics in the tech sphere. Even if no one has yet fully figured it out and moved beyond the gadget/geeky aspects (except, maybe, Amazon — again! — with Alexa), it is a massive opportunity to revamp experiences regarding interactions and personalization.

    - Blockchain:

    The blockchain, beyond Bitcoins and other digital currencies, has the potential to drastically change the way organizations manage transactions and the exchange of value. But there is more.

    As I mentioned earlier, PaaP supports the SRM² model. And, for the model to work, a certain number of conditions exist. One is trust:

  • between the supplier and the customer,
  • between the Procurement organization and the other departments,
  • between the Sales organization and the other departments.
  • These three “circles of trust” are the building blocks of a genuine dialogue (instead of one-way communications). Blockchain is the means to create digital trust between parties by bringing security and transparency in transactions.

    “[The] blockchain is a major breakthrough. That’s because its decentralized approach to verifying changes in important information addresses the centuries-old problem of trust, a social resource that is all too often in short supply”. Blockchain technology: Redefining trust for a global, digital economy, Michael Casey

    One of the more direct benefits of using blockchain is to lower transaction costs. From contracting (more on that later) to payments, blockchain removes intermediaries and fees. It is therefore the ideal solution to streamline processes where what you do to buy costs more (or close to) that what you actually buy. It will have Procurement rethink what to do with the tail spend! Also, the experience is quite different when compared to traditional processes. Easier and faster are the keywords as explained in this episode of the Thriving At The Crossroads podcast.

    In addition to payments, the blockchain opens new horizons for contract management. As Alec Ross says in “the industries of the future” (using the purchase of a house as an example), contracting hasn’t fundamentally changed in the last 45 years and involves a lot of papers, lots of signatures and lengthy procedures. It is because of the lack of trust between parties with regards to the financial transaction and the timeframe of such transactions. Also, the need to prove/define ownership adds to the burden. As already mentioned earlier, blockchain can make payments real-time, and it also solves ownership issues as blockchain records log who owns what in an easily retrievable (public), up-to-date, and secure manner (which also represents benefits for IP protection).

    On top of that, execution of contracts can be drastically streamlined and automated with smart contacts:

    “A smart contract has the capability to facilitate, execute and enforce the performance of negotiation of a contract. The entire lifecycle of a smart contract is automated and can provide valuable as a complement to or substitute to a legal contract”. Blockchain enabled Trust & Transparency in supply chains, Jørgen Svennevik Notland

    Source: How smart contracts automate digital business, PwC

    Other applications tap into the traceability and transparency that the blockchain embeds. By being a permanent and secure record of all transactions between all tiers of the supply chain, it can replace all existing paper-based systems. Plus, as it is available publicly (anyone or all partners in a network), it is broader and deeper than usual practices that only apply to Tier 1 (sometimes Tier 2) suppliers.

    “The project — a collaboration between Walmart, IBM and Tsinghua University in Beijing — was first unveiled in October, when the project partners claimed they were creating a new model for food traceability, supply chain transparency and auditability. “By harnessing the power of blockchain technology designed to generate transparency and efficiency in supply chain record keeping, this work aims to help enhance the safety of food on the tables of Chinese consumers,” noted an IBM press release. In fact, the pilot project was initially planned to track and trace Chinese pork; U.S. produce was added later.” Walmart Testing Blockchain Technology for Supply Chain Management, Bitcoin Magazine.

    Blockchain may sound like science-fiction or for the uber-geeks only, but it is not. Mainstream companies and actors use blockchain or enable the use of it. For example, Microsoft. Also, blockchain exemplifies the “platform” philosophy. Organizations can build numerous applications on top of the blockchain architecture (technical platform). Also, it removes friction and intermediaries allowing

    “The notion of shared public ledgers may not sound revolutionary or sexy. Neither did double-entry book-keeping or joint-stock companies. Yet, like them, the blockchain is an apparently mundane process that has the potential to transform how people and businesses co-operate”. The promise of the blockchain. The trust machine, The Economist

    All the examples I just mentioned illustrate the fact that Procurement has to think about engaging internal customers/stakeholders and suppliers in different ways. Procurement has to build omnichannel and replicable but unique experiences that fits with:

  • the type of purchase,
  • who purchases,
  • the context of the purchase.
  • - Human touchpoints:

    The concept of omnichannel experiences extends to the real world. Not everything is about technology and digital. Human touchpoints are also very critical to manage, maintain, and nurture relationships.

    For example, organizing business reviews (BR) with key stakeholders and with major suppliers is an opportunity to look at the business from a different perspective than during day-to-day interactions. Here too, it is crucial to design the BRs in a way that matches the relation and the business needs. BRs are moments where involved parties have more intimate and trust-based exchanges about where they are and where they want to go.

    In the same spirit, setting up “single points of contacts” (SPOC) participates, like the BRs, to creating more proximity and intimacy between Procurement and stakeholders.

    “The client contact is mapped out, and the procurement team becomes aware of every senior new role or person in the organisation they need to consider. They execute these important steps — they contact and communicate with them; they make a point of understanding their particular business objectives; they understand what type of person they are; they collect information and keep up to date with issues and goals in those business teams”. The Fujitsu Procurement Transformation Story (Part 2), Spend Matters UK

    The same is transposable to the supply side: one buyer is assigned as the main contact of a supplier. He is able to speak for the whole organization during BRs, negotiations, and escalations. That gives him access to a higher level of interlocutors at the supplier. It also provides the suppliers with privileged access to information, market, and power.

    People: new ways to manage talent

    Physical touchpoints exemplify that business is Human-to-Human (H2H) and that all of it must not be left to technology. Considering Procurement as a platform whose primary objective is to efficiently and effectively manage the dependencies between an organization and its suppliers has the effect to position the choice of

  • delivery models (in-house, BPO, Procurement-as-a-Service,…),
  • organizational models (centralized, decentralized, Center of Excellence…),
  • workforce/talent management and enablement,
  • as somehow secondary and as a consequence of their purpose.

    Also, the various possibilities listed above can co-exist in the same organization. The principles of omnichannel and personalization also apply there. Depending on the category, the stakeholders, the internal resources, the organization can select the most appropriate model. The objective being to employ the best talent (from an individual and collective point of view) and to continuously adapt to the needs and the market.

    Many organizations do not have the required expertise available in-house for all the categories they manage. They focus on the most critical ones. And, because of a particular project or because of a merger & acquisition or because of the emergence of new technologies on the supply market, what was marginal may become vital. To ramp-up capabilities and to temper its talent, the organization can decide to:

  • train his current resources
  • source and hire new talent
  • tap into the gig economy (the market of independent contractors and freelancers)
  • rely on the wisdom of crowds (crowdsourcing, for example, is one of the many ways to use external resources for a specific task or problem instead of hiring a consultant or expert).
  • Technology (automation and AI) redistributes work and redefines skill sets. Cognitive Procurement is about “People + Technology” and not “People vs. Technology”:

    This reinforced collaboration with machines has profound impacts that PaaP can help mitigate as it provides a system that is more agile, scalable, and adaptable.

    “Automation has begun to displace human workers, as some predicted, but the effect is more than just replacement — it’s advancement. The influx of sophisticated technologies will enable us to think of work in new and innovative ways. […] We are witnessing the emergence of the “liquid workforce” and the “human cloud” as new workforce models. The “liquid workforce” refers to employees who are able to re-train and adapt to their environment in order to stay relevant during the digital revolution”. The future is automated. Here’s how we can prepare for it, The World Economic Forum.



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