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P2020-795 - IBM Decision Optimization Technical Mastery Test v2 - Dump Information

Vendor : IBM
Exam Code : P2020-795
Exam Name : IBM Decision Optimization Technical Mastery Test v2
Questions and Answers : 44 Q & A
Updated On : February 22, 2019
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P2020-795 IBM Decision Optimization Technical Mastery Test v2

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P2020-795 exam Dumps Source : IBM Decision Optimization Technical Mastery Test v2

Test Code : P2020-795
Test Name : IBM Decision Optimization Technical Mastery Test v2
Vendor Name : IBM
Q&A : 44 Real Questions

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IBM IBM Decision Optimization Technical

IBM Db2 query Optimization the usage of AI | killexams.com Real Questions and Pass4sure dumps

In September 2018, IBM introduced a brand new product, IBM Db2 AI for z/OS. This synthetic intelligence engine monitors facts access patterns from executing SQL statements, uses computing device discovering algorithms to pick out premiere patterns and passes this information to the Db2 question optimizer to be used by subsequent statements.

laptop researching on the IBM z Platform

In may additionally of 2018, IBM introduced edition 1.2 of its machine studying for z/OS (MLz) product. this is a hybrid zServer and cloud utility suite that ingests efficiency records, analyzes and builds models that characterize the health status of a variety of indications, screens them over time and provides true-time scoring capabilities.

a few features of this product providing are geared toward aiding a community of model developers and bosses. for example:

  • It supports distinctive programming languages equivalent to Python, Scala and R. This allows for records modelers and scientists to use a language with which they're regular;
  • A graphical user interface called the visual model Builder publications model builders devoid of requiring enormously-technical programming skills;
  • It includes varied dashboards for monitoring mannequin results and scoring services, in addition to controlling the gadget configuration.
  • This computer gaining knowledge of suite become at the beginning aimed at zServer-primarily based analytics purposes. probably the most first evident decisions become zSystem performance monitoring and tuning. system management Facility (SMF) facts that are automatically generated by the operating equipment give the uncooked data for device resource consumption corresponding to primary processor usage, I/O processing, memory paging and so on. IBM MLz can assemble and keep these facts over time, and construct and coach fashions of system conduct, score these behaviors, establish patterns no longer without problems foreseen by humans, develop key efficiency indicators (KPIs) after which feed the model effects again into the system to have an effect on equipment configuration adjustments that may increase efficiency.

    The subsequent step become to enforce this suite to research Db2 efficiency statistics. One answer, called the IBM Db2 IT Operational Analytics (Db2 ITOA) answer template, applies the computing device discovering expertise to Db2 operational facts to benefit an figuring out of Db2 subsystem fitness. it could actually dynamically construct baselines for key performance symptoms, give a dashboard of those KPIs and give operational team of workers precise-time perception into Db2 operations.

    while common Db2 subsystem efficiency is a vital element in ordinary software fitness and performance, IBM estimates that the DBA aid body of workers spends 25% or more of its time, " ... combating entry course problems which cause performance degradation and service have an effect on.". (See Reference 1).

    AI involves Db2

    accept as true with the plight of modern DBAs in a Db2 ambiance. In modern-day IT world they need to aid one or extra big facts applications, cloud application and database capabilities, utility installation and configuration, Db2 subsystem and software efficiency tuning, database definition and management, catastrophe recovery planning, and greater. question tuning has been in existence considering the origins of the database, and DBAs are always tasked with this as smartly.

    The heart of query direction evaluation in Db2 is the Optimizer. It accepts SQL statements from purposes, verifies authority to entry the records, studies the places of the objects to be accessed and develops a listing of candidate facts entry paths. These access paths can encompass indexes, desk scans, a variety of table join strategies and others. within the data warehouse and big data environments there are usually additional decisions obtainable. One of those is the existence of summary tables (sometimes referred to as materialized question tables) that contain pre-summarized or aggregated information, as a result allowing Db2 to prevent re-aggregation processing. another choice is the starjoin entry route, usual within the information warehouse, where the order of table joins is modified for performance explanations.

    The Optimizer then reports the candidate entry paths and chooses the entry path, "with the bottom cost." can charge in this context potential a weighted summation of useful resource usage including CPU, I/O, reminiscence and other components. ultimately, the Optimizer takes the lowest cost entry route, stores it in reminiscence (and, optionally, within the Db2 listing) and starts access route execution.

    massive records and records warehouse operations now encompass software suites that permit the company analyst to use a graphical interface to build and manipulate a miniature statistics model of the facts they wish to analyze. The packages then generate SQL statements in keeping with the clients’ requests.

    The issue for the DBA

    in order to do decent analytics to your dissimilar statistics stores you need a pretty good figuring out of the facts necessities, an figuring out of the analytical capabilities and algorithms attainable and a high-efficiency information infrastructure. alas, the quantity and location of records sources is expanding (both in dimension and in geography), records sizes are growing, and applications continue to proliferate in number and complexity. How may still IT managers aid this atmosphere, specially with probably the most experienced and mature workforce nearing retirement?

    keep in mind additionally that a large part of decreasing the full charge of ownership of these techniques is to get Db2 purposes to run sooner and extra successfully. This always interprets into the use of fewer CPU cycles, doing fewer I/Os and transporting much less records across the network. given that it's frequently problematic to even identify which purposes may benefit from performance tuning, one strategy is to automate the detection and correction of tuning considerations. here's the place machine getting to know and synthetic intelligence will also be used to remarkable effect.

    Db2 12 for z/OS and synthetic Intelligence

    Db2 version 12 on z/OS makes use of the desktop learning amenities mentioned above to gather and save SQL question textual content and entry route details, as well as precise performance-related ancient counsel akin to CPU time used, elapsed instances and outcomes set sizes. This offering, described as Db2 AI for z/OS, analyzes and retailers the records in machine learning models, with the model analysis effects then being scored and made attainable to the Db2 Optimizer. The subsequent time a scored SQL commentary is encountered, the Optimizer can then use the model scoring facts as input to its entry route option algorithm.

    The outcome should still be a discount in CPU consumption as the Optimizer makes use of mannequin scoring input to select better entry paths. This then lowers CPU fees and speeds application response instances. a big expertise is that the usage of AI software does not require the DBA to have facts science expertise or deep insights into question tuning methodologies. The Optimizer now chooses the best access paths based now not only on SQL question syntax and facts distribution statistics however on modelled and scored historical performance.

    This can also be in particular critical if you save information in numerous areas. as an example, many analytical queries towards big statistics require concurrent access to certain data warehouse tables. These tables are often referred to as dimension tables, and they contain the information facets continually used to control subsetting and aggregation. for instance, in a retail atmosphere agree with a table referred to as StoreLocation that enumerates each keep and its place code. Queries towards store earnings information may additionally wish to combination or summarize revenue via area; therefore, the StoreLocation table may be used by means of some huge records queries. in this ambiance it's standard to take the dimension tables and duplicate them continually to the huge facts utility. in the IBM world this region is the IBM Db2 Analytics Accelerator (IDAA).

    Now believe about SQL queries from both operational applications, facts warehouse clients and large data enterprise analysts. From Db2's point of view, all these queries are equal, and are forwarded to the Optimizer. although, within the case of operational queries and warehouse queries they should undoubtedly be directed to access the StoreLocation table in the warehouse. in spite of this, the query from the business analyst against large statistics tables should still likely access the replica of the table there. This outcomes in a proliferations of talents entry paths, and extra work for the Optimizer. thankfully, Db2 AI for z/OS can give the Optimizer the assistance it needs to make smart entry path selections.

    the way it Works

    The sequence of events in Db2 AI for z/OS (See Reference 2) is commonly the following:

  • all through a bind, rebind, put together or explain operation, an SQL remark is handed to the Optimizer;
  • The Optimizer chooses the facts entry route; because the option is made, Db2 AI captures the SQL syntax, access path option and query performance records (CPU used, etc.) and passes it to a "gaining knowledge of project";
  • The studying project, which may also be carried out on a zIIP processor (a non-popular-intention CPU core that does not element into utility licensing costs), interfaces with the computing device learning application (MLz mannequin features) to shop this counsel in a mannequin;
  • because the volume of information in each and every mannequin grows, the MLz Scoring provider (which also can be completed on a zIIP processor) analyzes the mannequin facts and scores the habits;
  • throughout the next bind, rebind, put together or clarify, the Optimizer now has entry to the scoring for SQL fashions, and makes applicable changes to entry direction selections.
  • There are also a lot of person interfaces that give the administrator visibility to the reputation of the amassed SQL statement efficiency data and model scoring.

    summary

    IBM's machine researching for zOS (MLz) offering is being used to first rate impact in Db2 edition 12 to improve the performance of analytical queries as well as operational queries and their associated functions. This requires management consideration, as you must verify that your enterprise is prepared to eat these ML and AI conclusions. How will you measure the expenses and merits of the use of desktop gaining knowledge of? Which IT guide body of workers should be tasked to reviewing the influence of mannequin scoring, and maybe approving (or overriding) the results? How will you review and justify the assumptions that the software makes about access route choices?

    In different words, how smartly did you know your facts, its distribution, its integrity and your present and proposed access paths? this could determine where the DBAs spend their time in supporting analytics and operational utility performance.

    # # #

    Reference 1

    John Campbell, IBM Db2 extraordinary EngineerFrom "IBM Db2 AI for z/OS: boost IBM Db2 application performance with desktop researching"https://www.worldofdb2.com/activities/ibm-db2-ai-for-z-os-increase-ibm-db2-utility-efficiency-with-ma

    Reference 2

    Db2 AI for z/OShttps://www.ibm.com/assist/knowledgecenter/en/SSGKMA_1.1.0/src/ai/ai_home.html


    decision Optimization and laptop gaining knowledge of: Complementary techniques for an AI-driven future | killexams.com Real Questions and Pass4sure dumps

    Shutterstock graphic

    As groups wake up to the competencies of AI purposes, statistics science and laptop gaining knowledge of concepts are more and more seen as primary to force this growth. whereas data science applied sciences like predictive analytics continue to force innovation throughout agencies, laptop researching suggestions are instrumental to scaling information science for businesses.

    From the box of medical research to self-riding vehicles, from personal assistants to product recommendation engines, we see the influence of computing device discovering all around us. The position of resolution Optimization technology in fueling the success of computing device learning deserves particular mention. resolution Optimization know-how uses superior mathematical and synthetic intelligence concepts to resolve choice-making complications that contain tens of millions of choice variables, business constraints and alternate-offs.

    decision Optimization + computing device studying = imaginitive options

    The interplay between decision Optimization and machine studying is most excellent liked when one knows how each technique enhances the different. desktop getting to know models convey the potential to supply accurate forecasts (demand forecasts, equipment failure predictions, and many others.) by way of when you consider that real-time inputs as well as ancient facts. while a reputable forecast is precious, having the means to make analytics-pushed choices across the top-rated route of action to take is beneficial. This will also be completed via feeding the forecasts generated by using laptop-gaining knowledge of fashions as inputs to a call Optimization model that may then trust the a lot of tradeoffs and constraints to recommend the top of the line solution to fulfill enterprise desires.

    nevertheless, as soon as an optimization model has advised an action plan and that plan is in operation, the facts on the execution of that plan can be used through desktop-learning fashions to enrich forecasts, to automatically make the choice fashions greater correct, and to hedge against risks.

    We’ll discover precise-world functions of this interplay between computing device studying and choice Optimization at the IBM suppose 2018 conference. you could be taught the changes and complementary strengths of those two innovations, be trained premier practices, and spot examples of combining these applied sciences to obtain fiscal gains and efficiencies by way of attending Session 4227 (Make greater, faster, Smarter decisions with the aid of combining IBM machine learning and IBM determination Optimization).

    also, don’t miss the demo (IBM’s decision Optimization for Watson Studio: Predict renovation must keep creation Going) that lets you explore developing creative solutions that mix computer studying and decision optimization. you could additionally gain knowledge of in regards to the new facts science points integrated into IBM Watson Studio by using attending the arms-on-labs — “decision Optimization for Watson Studio allows Prescriptive Analytics on Watson Studio”

    IBM is internet hosting user community Day at this year’s suppose 2018 on March 18th which is open to all registrants of believe 2018. individuals can be a part of IBM experts for a full day, where we’ll carry you the latest information on product route, share most excellent practices & use cases from our specialists and champions. Attendees should talk over with this Session hyperlink and add the decision Optimization Session identification 9034 to register.

    IBM resolution Optimization for Watson Studio will assist records scientists take talents of optimization and other records science applied sciences in a unified environment to create interesting options to complicated company complications.

    be part of us on the IBM think 2018 convention (March 19–22) at Las Vegas to find out about all these exciting announcements. talk over with the think 2018 experience page to be taught more and register.


    determination Optimization injects motion into analytics | #IBMVision | killexams.com Real Questions and Pass4sure dumps

    determination Optimization has been around in some kind for a long time, although many business owners are only now discovering what it might probably do.

    IBM’s resolution Optimization core provides a platform to assist organizations with analytics to clear up difficult planning and scheduling challenges. choice Optimization is the third component of analytics, the action step that works alongside descriptive and predictive tools.

    “choice Optimization is used across industries,” Susara van den Heever, product supervisor of resolution Optimization for IBM, advised theCUBE all over IBM vision. “It’s used for planning selections, scheduling … any choice the place you've got a big number of alternatives to believe. Optimization takes the complete difficulty in a single go and suggests one or extra options when it comes to making plans and scheduling.”

    The platform is tightly integrated with IBM’s Maximo Asset administration, Algorithmics software, Unica software and SPSS Modeler.

    For operations researchers, determination Optimization presents a drag-and-drop interface by way of the Cloud. For developers, who may additionally combine a mannequin or utility into a bigger enterprise-large utility, decision Optimization uses DOcloud API. it's going to work with Java and another language,” spoke of van den Heever. “It takes about 5 traces of code to integrate our cloud solver with a developer’s application.”

    van den Heever urges agencies to analysis the platform to discover how it will work for them. “if you’re now not speakme to us, you’re basically lacking out on what that you may do when it comes to aggressive capabilities,” she said

    Watch the total interview below, and make sure to try more of SiliconANGLE and theCUBE’s insurance of IBM vision 2015.

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    Day one

    On Tuesday, I attended the annual DMP Owners Forum, which is “one the ways we show our gratitude to our dealers,” Jon Adams, DMP dealer development manager, told a room full of DMP’s top 100 dealers.

    This year’s speakers included Major Gen. (Ret.) Vincent Boles, who shared the wisdom he has gained throughout his military career, as well as some of his views on what makes a true leader. Boles gave out his book—4-3-2-1 Leadership: What America’s Sons and Daughters Taught Me on the Road from Second Lieutenant to Two-Star General—to all attendees. The other featured speaker was former Southwest Airlines senior level leader, Jason Young, author of the books The Culturetopia Effect and Servicetopia.

    Attendees also learned how DMP is giving dealers access to new markets through a variety of new platforms, including DMP’s CompanyStore, which enables dealers to participate in the DIY marketplace; Secura, a new high-volume residential dealer program; OnDemand Monitoring, a new revenue generator for dealers to offer to consumers that don’t want to sign long term contracts; and new avenues in home automation, including integration with AppleTV, Amazon Echo and Google Home.

    DMP executive management also featured sessions on new technology that will be available this summer, including previews of the DMP’s Gunshot Detector that goes on Existing DMP XR Series panels and DMP’s new Doorbell Camera, which Mark Hillenburg, DMP’s executive director of marketing, said is “deeply integrated” and will “provide new highly sought after functionality to the existing DMP Virtual Keypad, App and control panel family."

    Jeff Britton, DMP’s VP of product design, also walked attendees through DMP’s Tech App Platform, which was “developed to save time on new installs and service calls and really to eliminate some of the service calls you are having to make today. The platform will continue to grow because we feel there are a lot of things that your techs should be able to do right from their phone or their tablet.”

    Day Two

    On Wednesday, I began my day at Axis Communications’ 10th annual press breakfast. With the theme of “The Magic of IoT,” Fredrik Nilsson, VP Americas, and Martin Gren, founder, who were both dressed as magicians—you gotta love these guys!—talked about how the company plans to bring the same innovation and “magic” that it brought to camera technology to other products, such as its Network Speakers, which make up an integrated audio system that is ideal for retail settings. Axis also teamed up with Cognimatics to develop analytic capabilities for its True View People Counter, which is also ideal for retail environments, providing valuable business intelligence data.

    Gren also provided a preview of its newest technology—the AXIS D2050-VE Network Radar Detector, an outdoor motion detector with radar technology that can provide cost-efficient perimeter protection and decrease false alarms. The Company is also previewing at ISC West a selection of products that will be launched later this year, including new high resolution fixed domes in AXIS M30 Series, AXIS P32 Series, and AXIS Q35 Series. Axis also unveiled its AXIS FA Series with modular cameras for highly discreet, cost-efficient video surveillance, and three new additions to AXIS P13 Series that offer 4K resolution in full frame rate and that enable coverage of large areas with high image detail.

    Following the breakfast I headed to the show floor, which was abuzz with high traffic and early indicators point to a record year in terms of attendance for this year’s show.

    My first booth visit was at Legrand, a global specialist in electrical and digital building infrastructures. Manny Linhares, director of strategy, IoT, told me about Eliot, a Legrand global IoT program, that “brings the promise of interoperability, providing the building blocks and infrastructure to enable IoT and bring connectivity to where it is needed.”

    Moving to the Qolsys showcase room, I was able to test drive the company’s second generation panel, which is sleeker and more powerful with some very cool new capabilities. Kevin Woodworth, director of technical account management, pointed out that the panel is the culmination of “all of the lessons we learned from our first panel, and what we learned from dealer feedback.” The panel has a 7-inch HD touchscreen with built-in 5MP camera and multiple wireless radios, as well as LTE connectivity, advanced encryption, a built-in router, a glass break detector, dual path connectivity and Bluetooth disarming, to name just a few of the upgrades.

    Over at the FLIR Systems booth, I was given a demo of the FLIR United VMS 8.0, the latest version of the company’s comprehensive, enterprise-level video management solution for managing video security operations. The company introduced three high-performance security cameras, including the FLIR PT-Series HD thermal and visible camera system, the FLIR Quasar 4K fixed box camera, and the FLIR Ariel 3MP (3-megapixel) corner camera. All three cameras integrate with FLIR United VMS 8.0 and expand the company’s end-to-end line of security solutions. I also got a demo of the company’s Cameleon command and control platform.

    At the Interlogix booth, the company was highlighting TruVision Navigator 7, the latest release of its popular video management software. TruVision Navigator 7 now works seamlessly with Interlogix TruPortal access control systems and IFS network switches, allowing end users to manage their entire security system from one single, easy-to-use interface.

    This year’s ASSA ABLOY Press Event focused on “Security in the IoT Age,” and Martin Huddart, president, looked at how IoT will affect security. He aptly pointed out, “We are producing data, not products today,” noting that the key as we move forward as an industry is “bringing all these devices together.”

    Next, I caught up with Denis Hebert, president, Feenics, a provider of cloud-based access control solutions. The company launched its enterprise Keep by Feenics platform that delivers scalability, flexibility and advanced security. The new cloud-based access control as service (ACaaS) platform integrates native visitor management, and incorporates RESTful API that dramatically simplifies integrating complementary systems plus connecting Keep to applications outside of the organization. Built specifically for the cloud and hosted by Amazon Web Services, Keep significantly minimizes costs through simple, maintenance-free upgrades and patches. 

    At the LifeSafety Power booth, Joe Holland, vice president of engineering, gave me a glimpse into how the company continues to “innovate the category of power,” with new predictive analytics and critical reporting capabilities. The company announced a breakthrough in connecting intelligent power management analytics to leading software manufacturers directly through Authentic Mercury Security hardware.  “This truly enhances and expands the potential for systems integrators to offer remote power management to customers who will benefit from predictive analysis and detailed reporting on the health and well-being of their solutions,” said Holland.

    I also met with Glen L. Smith, director, dealer operations, ADT, who told me about the great strides the ADT Dealer Program has made in the past year alone. In the area of support for dealers, the company went from a net promoter score of -9 to 76 in just 18 months. How did they do this? “We asked our dealers what was broken,” said Smith, who noted that response time was a big issue for dealers. In response to this concern, Smith said they went from a two-hour hold time to a zero hold time in just one year.

    At the Bosch Security Systems booth, the company featured a completely new portfolio of IP cameras and video analytics that extend surveillance beyond security, services that create recurring monthly revenue opportunities for dealers, and an all-in-one wireless multi-sensor that can be configured as a door/window contact, water or tilt sensor. Bosch also introduced In-Store Analytics, a solution to provide retailers with valuable insights on store traffic for improving operations, customer engagement and sales.

    At the Alarm.com booth, Brian Lohse, director, commercial sales, talked about the company’s strategy to take what it has been doing on the residential side and bring that to the SMB space. On the residential side, the company’s open platform continues to expand integrations with a broad range of devices to help its dealer partners extend their footprint in the smart home. Alarm.com has also extended 4G LTE communications capabilities.  In addition, Alarm.com highlighted its Business Intelligence service that helps its dealer partners lower attrition by analyzing data points.

    Next, I caught up with Jeff Whitney, VP of marketing for Arecont Vision, who went over some of the big announcements for the company at the show, including Arecont Vision SNAPstream bandwidth reduction technology that supports multiple new and existing megapixel cameras; SurroundVideo Omni G3 Omnidirectional multi-sensor cameras with remote setup; Arecont Vision Compact MicroBullet indoor/outdoor, day/night megapixel camera; and Arecont Vision MicroDome G2 with Integrated IR indoor/outdoor day/night ultra-low profile megapixel camera.

    At the Pelco by Schneider Electric booth, Sharad Shekhar, CEO, Pelco SVP, video line of business, and Diane Feliciano, VP, global marketing, spoke about how the company has spent the last 12-18 months “transforming and repositioning the company” for the future. “We have been making changes across the entire organization for the last couple of years, from technology developments to new sales, support and operations programming,” said Feliciano. Pelco also showcased several new and enhanced products and systems, including its VideoXpert Video Management System (VMS); Sarix Enhanced IP Cameras with preloaded analytics on every model; ExSite Enhanced Explosion-Proof Cameras; and Optera Panoramic Multi-Sensor Cameras.

    I finished my day with a visit to the Altronix booth, where Kirby Han, art director, took me through the company’s latest upgrades and new products, including its new NetWay Spectrum Fiber Solutions, its eBridge 800E EoC Receiver with Integral PoE Switch and its expanded NetWay Line with new managed midspans and endspans.

    Day Three

    I started day three at the Allegion Trends Talk breakfast, where I was able to speak with Rob Martens, futurist and VP, strategy & partnerships, about how the company is leveraging the latest technologies to provide the best solutions for customers. “I am the tip of the spear when it comes to new technology,” he said. “I create the chaos that the company has to deliver on.” Product wise, the company’s Engage platform is expanding to include many more products, “so that level of connectivity is now getting rolled out and becoming a reality.”

    At the Genetec booth, I spoke with Andrew Elvish, VP, marketing and product management, about the success of the company’s Mission Control system, which he said is “a game changer,” as the decision support system provides organizations with heightened levels of situational intelligence, visualization, and complete incident management capabilities. He also pointed to how the company’s Retail Intelligence application provides business intelligence and operational efficiencies. The company also highlighted the latest capabilities of its Synergis access control system, part of its Security Center unified offering.

    At the AMAG Technology booth, I met company president Kurt Takahashi, and spoke with Kim Rahfaldt, public relations manager. The company unveiled Symmetry CONNECT, its new policy-based identity management system that delivers an easy to use, automated software platform to manage employee, contractor and visitor identities, satisfy regulatory audit and compliance requirements and reduce the administrative costs of the security team. When used with Symmetry Access Control, organizations have a robust identity management system that utilizes data to secure people, property and assets, and automates business processes for better operational efficiency.

    At the HID Global booth, I spoke with Anthony Petrucci, director of global public relations and corporate communications, about the company’s new indoor Location Services for workforce optimization, which won the SIA New Product Showcase award. The Location Services provide organizations with visibility into the location of their workforce in a facility, making it possible to analyze room usage for better building management and increased operational efficiency. He also spoke about how the company is leveraging Bluetooth low energy (BLE) for new and emerging applications in the connected environment, as well as the role of mobile access, advanced smart card technology and biometrics in the company’s overall strategy.

    At the Milestone booth, I spoke with Courtney Dillon Pederson, communications manager, about the company’s strategic alliance with Lenel, which will begin selling Milestone Systems Video Management Software (VMS) solutions directly through the Lenel Value Added Reseller (VAR) channel in North America. “We are really excited about this partnership and believe this is a great combination,” she said, noting that this alliance will reduce the total cost of ownership for users.

    I next visited the Eagle Eye Systems/Brivo booth, and sat down with Dean Drako, president and CEO for Eagle Eye, who shared his views on the “continued adoption of cloud.” He also talked about the technology partnership with Hikvision to deliver customers a seamless and cybersecure cloud video surveillance solution. Eagle Eye also highlighted that is has added Camera Cyber Lockdown to all its products, which blocks cameras from communicating with the Internet, stops them from being attacked and compromised. Drako also spoke about the company’s implementation of two-factor authentication, which “will be the standard for the industry moving forward.”

    At the IDIS booth, I was able to sit down with company president and COO Albert Ryu, who shared his views on how the company is poised to leverage technology to provide the customized solutions for customers. He also spoke about how the company, which is celebrating 20 years, is “bringing AI to security.” On the product side, the company featured H.265 IPC/NVR products, including the new IDIS 12MP Super Fisheye Camera. Also featured were new low light and long distance solutions, and end-user focused enhancements to the company’s award-winning PTZ tracking controls, IDIS Smart UX Controls v2.0 and retail analytics/business intelligence suite IDIS VA in the Box. The company also highlighted its powerful 64-channel IDIS DR-8364 NVR, which delivers enterprise-level performance at an NVR price. Moving over to the Hanwha Techwin booth, Miguel Lazatin, director of product and channel marketing, North America, walked me through all of the new features and capabilities of the Wisenet 5 chip, which was unveiled recently. The new chip powers the Wisenet X series, a new product line that features 27 models split up between 2MP and 5MP camera lines. Features include 150dB WDR performance and clear images in extreme low light conditions, convenient USB port is also available for easy setup and installation, as well as license-free audio and video analytics, dual SD card slot for increased onboard storage and image stabilization using gyro sensors. The company also announced that it is investing $100 million to create a security-only manufacturing facility in Vietnam.

    Next, Dahua product marketing manager Jennifer Hackenburg, gave me a tour of the booth, showing me how the company is embracing “deep learning,” pointing out that the company is “ahead of the curve” in leveraging all of the enhanced capabilities that deep learning can provide. The company announced that it is working with NVIDIA to bring artificial intelligence (AI) functionality to its next-generation deep learning products. Equipped with NVIDIA GPUs, the Dahua DeepSense advanced high-capacity video analytics server provides deep learning capabilities. The technology delivers a powerful, scalable method of extracting rich metadata and processing structural data to deliver fast and accurate analytics. Dahua also announced that Movidius’ Myriad 2 Vision Processing Unit (VPU) technology will power select Dahua video surveillance cameras with advanced intelligence functions that train the devices to gather, analyze, and retain information much like the human brain.

    I next visited with Jim Hoffpauir, president, Zenitel, at the Vingtor Stentofon booth. He showed me how the company’s Turbine Intercom Station can provide a multitude of solutions, in any setting, from schools to airports to military and government applications. He also talked about his dedication to educating customers on how to pick the right system, and he created a scorecard to follow. He noted that it is important to have three key features—intelligibility, interoperability and the “ilities,” which relates to “high availability, scalability, defensibility and maintainability.”

    At the Tyco-Johnson Controls booth, I spoke with Roger Barlow, senior director, product management, TycoSP about a number of new products across its intrusion, access control and video divisions, and how the company is benefitting from the “synergies between Tyco and Johnson Controls,” he said. He also showed the DSC iotega, the company’s new innovative security and lifestyle management platform. A full-featured security technology, iotega facilitates home automation enhancements and add-ons via software apps. The company also announced its SG System 5 now supports PSTN line cards on the SG-System 5 receiver to give central stations the flexibility to monitor both IP and PSTN alarm signals, and how its EntraPass Go Support for Apple Watch is the “industry’s first Apple Watch mobile credential that allows card holders to use the watch as a mobile access control credential."

    At the Sureview Sytems booth, Simon Morgan, chief technology officer, gave me a demo SureView PSIM Solutions, which he pointed out, “improve security, optimize operations and reduce costs. He showed me how the Immix CC Platform delivers more integrations, easy deployment and simple operation. He said SureView Immix CC Simplifies PSIM deployment and operation, featuring “intuitive UI, more integrations and unique architecture, which deliver unprecedented ease of operation and versatility.”

    Next, I visited Ditek, a surge protection company that works closely with the security industry to protect vital systems and equipment from being damaged by power surges, saving money in the process. Jorge Andino, field sales engineer, pointed out that the company does this by providing a site survey for each project, giving that customer the right surge protection equipment to meet their needs. “Our surge protection equipment acts as the sacrificial lamb, preventing costly damage to the equipment.”

    I finished my day speaking with Rick Caruthers, executive VP, Galaxy Control Systems, who told me about the company’s new announcement that is “committing resources and manpower to providing cloud solutions to our customers,” he said. The Galaxy Cloud Concierge solution is a complete turnkey, cloud-based, fully hosted and managed access control and monitoring solution that includes professional 24/7 management and supports industry standard access technologies.

    Day Four

    On Friday I started the day at the Morse Watchmans booth speaking with George Lawson, account executive, and Joseph Granitto, COO, about the company’s key control and access management security systems, which are getting smarter and safer with a lot more functionality. “We take feedback from customers to add new features and make our products easier to use,” said Lawson. In addition to an improved motherboard powering its systems, the company is unveiling a new enterprise-based software system. And its Asset Watcher, which the company tested exhaustively over the past year, is set to come out soon as well.

    At the Pivot3 booth, I spoke with Brandon Reich, surveillance business leader, about the company’s strategic original equipment manufacturer (OEM) relationship with Intelligent Security Systems (ISS), an innovator in the development of advanced video management and video analytics software. Under the agreement, ISS will license the Pivot3 hyperconverged infrastructure (HCI) platform and integrate it with ISS video management software (VMS) to supply more robust security intelligence and video storage options to customers. “We are really excited about that partnership,” said Reich. “We can provide a great solution set together.”

    At the Eizo booth, I spoke with Keisuke Akiba, product and marketing manager, and Brian Cote, about the company’s security and surveillance monitoring solutions. The IP decoding monitors come equipped with visibility enhancement technology for improving the clarity of video footage in real time. Akiba demonstrated the multi-monitor flexibility and showed me the 4k IPS 31.5-inch monitor with 3840 x 2160 native resolution.

    Over at the Boon Edam booth, Tracie Thomas, marketing manager, gave me an overview of the company’s many entry solutions, as well as news on the company’s expanding sales and customer service staff to support record sales growth. The company also announced the expansion of its product-training schedule, with more programs and locations. And, soon, Boon Edam will offer its revolving door specifications on the ARCOM MasterSpec platform, which is widely used by architect and design firms in North America for commercial buildings.

    At the 3xLOGIC booth, Suzi Abell, tradeshow and events manager, shared show news, including the company’s new integrated, hosted video and access control solution for the SMB market and the company’s partnership with Bold Technologies to offer an advanced video solution. The companies recently completed integration of Bold’s ManitouNEO with the 3xLOGIC VIGIL video platform, which supports cloud-managed video. The company also announced the release of a new multi-sensor camera and updated VIGIL software.

    At the Panasonic booth, Charlie Hare, national category manger, security and evidence management solutions, walked me through some of the company’s big announcements at the show, including the company’s expanded portfolio of i-PRO Extreme Surveillance Technology and its new i-PRO Extreme PTZ Camera with Advanced Analytics, as well as its latest video surveillance solutions. The i-PRO features a new chip set that produces “amazing picture quality,” said Hare.

    Over at the ReconaSense booth, Clayton Brown, product manager, described the company’s next-generation situational awareness platform, which provides “the tools to perform real-time critical analysis,” he said. John Carter, the CTO of ReconaSense, is a former NASA engineer. The software leverages the IoT and big data with access control and video analytics to drive “proactive response” across diverse systems. By connecting the dots and alerting operators of suspicious events, the technology facilitates better decision-making and optimal resource allocation.

    My final booth visit was over at NVIDIA, which was showcasing some of its latest IVA-related, AI technology for smart and safe cities, featuring many partners such as IBM, Hikvision and Dahua. Deepu Talla, VP/GM, autonomous machines, showed me how the company’s GPU computing is not only powering its many partners but “driving the latest applications for safety and security in AI cities,” he said, including public safety, traffic management, behavior analysis, video analytics, anomaly detection, facial recognition, fast forensics, parking management and robotics.

    NOTE: In addition to daily reports, look for ssnTVnews video interviews from the show floor, including my video interviews with Bill Bozeman, president and CEO, PSA Security Network; Dan Moceri, executive chairman and founder, and Ken Lochiatto, president and CEO, Convergint Technologies; Maureen Lally, VP of marketing, TycoIS; Richard Tampier, senior director, sales and product strategy, Red Hawk Fire & Security; Craig Layers, senior VP. sales and marketing, ADS Security; and Jon Cropley, principal analyst, video vurveillance, IHS Markit.


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    Ask HN: Huge enterprise customer wants to see our source code 513 points by throwawaysource on Dec 6, 2017 | hide | past | web | favorite | 277 comments We are a small 5-person enterprise software startup, operating in the data analytics/ML space. We are working on starting a proof-of-concept with a huge potential customer ($50+bn in revenue). We recently identified our first use case, and are ready to get into contract negotiations.

    But then they got back to us with an odd request: they want to see our source code (likely upon completion of the PoC). Given that our core IP is our models and algorithms, we are reluctant to agree. Their justification is: "we want to see how your algorithms made their decisions."

    We know that they have lots of resources and are building up internal data science team. And yet it was pointed out to me that their goal might not necessarily be to outright steal our IP, but rather to cover their bases. But we are still worried they might be "inspired" by the parts they see and get their internal teams to replicate across other sites or use cases. And we don't have the resources to litigate, nor any way of knowing they do this.

    My questions: 1) Has anybody run into a request like this? How would you respond? 2) How likely do you think their goal is to genuinely "see what happens under the hood" as opposed to replicate in the future? 3) Are there any legal protections we can put in place to prevent them from not just copy-pasting our code, but also from "learning from it" or so?


    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



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    IBM P2020-795 Exam (IBM Decision Optimization Technical Mastery Test v2) Detailed Information



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