Marketing a Renaissance How to Trigger a Spontaneous Re-Evolution & Scientific Renaissance of the...
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Transcript of Marketing a Renaissance How to Trigger a Spontaneous Re-Evolution & Scientific Renaissance of the...
Marketing a Renaissance
How to Trigger a SpontaneousRe-Evolution & Scientific Renaissance
of the Computer Industry & STEM Education
Elevation Partners HR Task: Training a CEO and VP-Marketing Our thesis is that the computer industry is far out of equilibrium and its market is depressed because programming technology was lowered 40 years ago, edging out higher DIY development by end-users, and nourishing a middleman guild, capable only of recycling staple mass-applications. Disconnected from R&D needs, this dominant guild has produced a BABEL of the low-to-middle programming languages, suitable only for its recycling agenda, thereby blocking DIY development in R&D and STEM education with very-high-level languages, where only end-users can act quickly enough to meet needs. The current era of tablet machines is just another cycle continuing this recycling agenda, but provides a means of changing the game. Monopoly domination in the PC era of software distribution caused opposition to rise in a reaction mass-movement with a viral license that depressed software prices below market subsistence. Now with the cloud (non-distributed software) this “open-source” mass movement has lost its viral leverage, and can be redirected with suitable PR and escalation tools, to spontaneously support an R&D DIY renaissance of the original (FORTRAN) growth of the industry. A new kind of escalated recycling (simulation to optimization) can usher the guild through a metamorphosis into a DIY-peer collaboration posture, with hyper-agility that can meet R&D narrow opportunity window (NOW) timeframes –the cause of the lack of R&D growth over the last 40 years. This R&D renaissance will be jump-started by a publishing revolution in STEM education, converting a host of engineering textbooks for tablet-computer use.
Android
MetaCalculus +
Machine Language101010110101010
HardwareASM: CLA ADD STO
MnemonicFORTRAN: A=B+C
Algorithmic Meta Calculus: Find x,y,z to Maximize f
Metaphoric
Meta Science: Find x,y,z to Maximize f
Peer Collaboration
Collaborative
Evolving End-User DIY Media:
Modeling Libraries
Our agenda is to re-open the DIY pathway that FORTRAN paved until the dominance of the IBM 360. That was a unique era (1957-1970) when the aim was to evolve higher-order DIY modeling languages built on top of FORTRAN. All software was new, and problem-oriented languages like DIANA, COGO, MIDAS, MIMIC, CSSL, GPSS, SIMSCRIPT, SIMULA, DYNAMO, and many others emerged from end-user modeling needs. All were designed for rapid iterative prototyping (RIP) of R&D applications in time to meet the “NOW or never” deadlines. These timelines could not be met by guild middlemen in “waterfall” development, but the 360 ushered in the staple recycling agenda (repeated conversion to new hardware), which led to C as a portable hardware-aligned lingua franca. The guild reclaimed the industry with this agenda, because old codes were specifications for new codes—the waterfall worked for recycling—if not for R&D, and staple mass-applications had economy of scale to amortize the immense labor costs. Now with baby boomers (who started careers in the earlier FORTRAN era) available to guide our VARs, we have the means to re-organize the guild into the R&D race posture that took us to the moon.
The VAR MetaScience Buildout
OperatingSystems
Conventional Languages
MetaCalculus
Diversified Optimization Modeling Languages & GUIs
GAEMYEverGlade
CAD
EECECAD
MECADChE CAD
Chemistry BMEBiology
CADPhysicsNECAD
ASECAD
IE CAD
PECAD
With PROSE platforms in the time-sharing era, we established a NOW-speed programming level three-tiers above the algebra-level of FORTRAN and all of today’s languages—beyond calculus. But early PCs interrupted our growth. They weren’t powerful enough for the calculus overhead in our virtual-machine platforms. But by 1990, we had extended FORTRAN compilers to this new calculus plateau. So, in principle, we were ready to restart the FORTRAN R&D renaissance from this beyond-calculus plateau. But the game had changed. Distributed software brought high-cost logistics, GUI and client-server web interfaces added enormous software-development burdens, and GNU introduced a subsistence no-man’s land between it and Microsoft, where non-staple software had little chance of becoming profitable. The time was not right. But the cloud changed the game back again. GNU’s copyleft no longer applied. Moreover, the GNU/Linux mass movement could potentially be mobilized to build-out the new diversified modeling languages and GUIs on top of our FORTRAN-based calculus platforms. They were already engaged in such activities on a large scale, but not for end-user DIY needs. Yet we could provide escalator tools to turn the tide. What if we could show them that there was “mother lode” in R&D and STEM education that had been ignored by the computer industry since 1970? FORTRAN had triggered a massive buildout in 1957 that had made IBM a monopoly. Why couldn’t we do it again, particularly if it didn’t have to build from nothing, like FORTRAN did, but could mostly recycle old simulation code into optimization to accelerate the buildout? Also, what if we could we could blanket all of this new buildout in a new collaborative web: “Spiritext”, which would become popular, like phone texting, and could retrofit all languages to facilitate software maintenance?
FUNMODEL PUMP
MODEL DESIGN
MetaFor
Legacy Simulation Code
SUBROUTINE DESIGN
FUNCTION PUMP
FORTRAN
Optimization Re-Engineering
Simulation Program Optimization Program
OPTIMIZATION
Nested AutomaticDifferentiation
FIND x,y,z BY JOVE_SOLVER TO MAXIMIZE f
Intern Porting Task
FIND x,y,z BY THOR_SOLVER TO MAXIMIZE f
FIND x,y,z BY ZEUS_SOLVER TO MAXIMIZE f
Solver Experiments
Since recycling has become the major programming pastime (Unix into Linux, Linux into Android, etc.), we now have a recycling elevator for a spontaneous metamorphosis of the computer industry and STEM education. ORE mining self-trains programmers to become solution mechanics and end-user evangelists. It is both painless and fun, for professionals and students. By porting legacy simulation programs to MetaCalculus-Fortran and applying the optimization solvers to convert the old code to new optimization applications, they learn to apply the library solution engines. They needn’t be concerned about the science equations or what they mean. That is the concern of their peer end-user partners, who take over the re-engineered program and adapt the imbedded equations for their own needs. Thus optimization re-engineering is a means of re-transforming the middleman culture back into the peer driver/mechanic “race” division-of-labor that took us to the moon. It will be especially important in training a new generation of engineers in universities, converting old CAD systems from simulation to optimization. A case in point is NASA’s Shuttle Flight Design System, next slide.
Fodder for ORE Mining - NASA FDS
Shuttle CAD System for Mission Design• Hundreds of FORTRAN programs
Historical Model Base to Train New Engineers
On Orbit Graph Generators
FDS was a CAD medium of “canned engineering” synthesis tools, which was designed as a serious game for relatively un-educated technicians to design shuttle missions without having the ability to understand the underlying engineering. Other such serious games, like SPICE (circuit design) and Syntha (turbine power-plant design) were examples of media which can be “elevated” in the MetaScience buildout. They were all simulation systems without overall design optimization. Yet their canned engineering content is very amenable to optimization synthesis via MetaCalculus. Thus they are excellent candidates for ORE mining to catalyze the MetaScience buildout and establish the new collaboration agenda between computer science and engineering departments of universities.
“Mechanic” Knows:• Equation types• Solver Engines
“Driver”Knows:• Equation meaning
in science
EXTREME
Part-time Support Application Spawning
MODELING Optimization Rapid Prototyping
Here is the payoff from ORE mining. Having learned the various MetaCalculus engines from porting old simulation code and experimenting with these solvers to find optimum solutions, programmers can become evangelists and mentors to end-user engineers and scientists to help them apply these engines to new application models. They don’t need to understand the science of the equations. This is how peers (drivers and mechanics) can achieve economy of scale while coping with rampant diversity of one-of-a-kind application needs of R&D. This bypasses the waterfall learning curves which have hobbled R&D for the last 40 years, creating enormous pent-up needs that most in the computer industry are oblivious to, reckoning that R&D markets are small, rather than repressed. The programmer’s role here, like any mechanic, is transient and intermittent. The end-user is the driver—the explorer. But such peering also teaches programmers how to build better software media for end-user modeling to tackle ever more complex problems (next slide).
SpiritextWebCodeCompiler
Wiki Sites
Domain
ModelingLanguages
Specific
MetaFor SourceMC7
MetaFor SourceMS8
WrapperTranslatorPublishers
MetaForIL
Compiler
FORTRAN/C/Perl /Python/SQLSource
MetaForIL
Compiler
MetaScience Buildout VAR Language Design Stack
Binary Code
F95Compiler
F95Compiler
F95Compiler
MC Kernel &Engine Library
Driver/Mechanic Collaboration Strategies
Modeling Component Library Domain Alphabet Dialects of Base MC Languages
Parallel PaaS Strategies
GAEMY Compiler Mechanics
Solution Engine Mechanics
Spiritext Wiki Mechanics
1
2
3
4MIDUS Numerical Engine Builders
R New Solver
Engines
The MetaScience buildout by the VARs will be like a diversification fan-out into the various R&D application domains. This “design stack” represents the rich division of labor that will evolve internally to the “escalation language PaaS terrace” above and below (in libraries) the current crop of compute-bound languages like FORTRAN and C, in concert with I/O bound scripting glue languages like Perl and Python, and database languages like SQL. This is the sustaining work by VAR programmers who as mechanics and evangelists support end-users part time. The result is a balanced organic culture of R&D, STEM education and software development, instead of the stratified computing culture we have today, where developers and end-users seldom interact.
“Mechanic” Knows:• Equation types• Solver Engines
“Driver”Knows:• Equation meaning
in science
HIGH
Part-time Support Application Spawning
MARGINOptimization Rapid Prototyping
A critical economic issue, which this plan can remedy, is the “one-price-fits all” staple package price-depression that IT-buyers have foisted on this industry. It exacerbates the subsistence effect of the bipolar monopoly (Microsoft vs. GNU) staple pricing, which effectively removes R&D solutions from the marketplace. Consultative selling bypasses IT buyers, selling to R&D engineers directly. IT selling is low margin vending. The IT buyer can’t judge the value you are selling, only the R&D end-user can. R&D business is high margin because we are selling solutions into a race condition, to assist the client in a winning situation– a winning proposal, first into the market, a vastly superior product, etc. This is consultative selling, where service price is based upon the situational value of winning—not on price competition in the market. The Master VAR will teach this value proposition in its VAR sales training—how to consult with a peer modeler who she is mentoring as solution provider (solver engine “mechanic”) to solve a sophisticated time-critical problem. She may even be a modeling consultant who also happens to know the MetaCalculus engines. This is why we seek VARs who are highly diversified application-domain specialists. MetaCalculus attracts prospects to Master VARs as service agents who screen opportunities, negotiate service prices, and assign VARs to the opportunities. Master VARs are the program office for the global VAR matrix they recruit with their webinars.
Promotion Leap: Textbook Content Re-Engineering
REPLACING
125 Statements
25 Statements
OLDCODE
FORTRANFortranCalculus
A Side Business to Jump Start our Marketing Campaign
Example ProblemsIn Old TextbooksConverted to Ebooks
Updating Diverse Examples in Existing Textbooks
NEWCODE MetaCalculus is about raising the level of expression of standard programming
languages to the level that scientists are used to modeling science. The computer industry has neglected this problem for the last 50 years. But thousands of engineering textbooks have been written containing problem-solving examples written in lower-level programming languages, beginning with FORTRAN in about 1958. All of them can be used to show the superiority of MetaCalculus languages. Object-oriented programming, a considerably more difficult paradigm to learn than MetaCalculus, was ushered into the world by "how to do it" OOP textbooks. It became a big part of the publishing business during the last three decades. But it was not a mass upgrade which could be blitzed with automation like this opportunity. Here is a 50-year backlog of recycle-able textbook content with thousands of embedded simulation examples convertible to simpler optimization examples which are dynamically executable on tablets and pedagogically explained with Spiritext. Moreover, all of these examples become regression test assets for automated validation of MetaCalculus and MetaScience products.
Software Launch Tasks
Publishing Automation Tasks“BookFlow” Launch
Old Textbook Copyrights
Textbook Re-Engineering Publishing Agency • Side PR Business• Cost Center in this model
– Feeds back regression tests– Obviates Advertising
Publisher Customers
Our Ebook Portfolio Solicitation
Re-Engineered TextbookProduction
Deals
MetaCalculus Re-Engineered E-TextBooks
BusinessPlan Tasks
(Diversified MC Manuals)
In our strawman business plan and projections, we are planning to offer publishers a textbook re-engineering service, taking their old printed versions, converting them to ebooks, and all of the examples in them to MetaCalculus optimization examples, primarily at our cost. Each such book will be essentially a MetaCalculus optimization modeling manual for a particular engineering discipline. We plan to tailor our own "manufacturing system" for this purpose in just the first 11 weeks (Task 2 - BookFlow Apparatus Deployment) as a mashup of our MIDUS IDE and WordPress. Since this is a wholesale blitz of recycled content production, we get them very quickly, before any competition can develop. We can probably achieve critical mass of MetaCalculus market penetration, establishing its brand across the world, just on the basis of this "side business".