Monthly Archives: February 2012

System Map v4.0 + Business PLAN-ter sketch

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System Map v4.0 (work-in-progress)

Working on the system map… Work-in-progress, but looking nice and sort of insane / frantic. It has been interesting to illustrate the entire project as an algorithm / system as opposed to just the business plan generator. More soon…

Cryptic Commemoration

I have been working on researching the best approaches to creating the plaques that will be placed above the location of the Business PLANts in Silicon Valley.

Consideration 01: The language ~

What kind of language should be used to commemorate the burial? Should it even be the burial that is commemorated, or should it be the future retrieval? Or should it commemorate “the era of sameness” instead? My project is not necessarily about making a “time capsule,” but it is an appropriate reference as it is the only kind of burial that plans for retrieval. Here are just a few of the examples of time capsule plaques that I found on the internet:

There definitely is a formula to the way these plaques are written. Here is a breakdown of the necessary elements:

  • The name of the individual or institution this is associated with
  • The time span (when is it buried, when is it to be open)
  • Clear declaration that there is something beneath it
  • Reason for they “why now” – why is it buried at that time in history?
  • Optional: Sponsor of the burial

Consideration 02: The form / production ~

I have been simultaneously researching various vendors that might be worth going through. It is sort of funny that when these kinds of plaques are made, or plaques in general, there is little room for design – very templated. I kind of like that as it speaks to some of the signals of the end of entrepreneurship.

[thesis project brief]

Project Conspiracy ~

I believe we are headed towards an era of sameness – an era in which innovation by the human species alone is impossible because all humanly perceivable problems are solved. While, to some, the elimination of problems may seem to be a great success, I find it to be the most pressing dilemma of mankind. Entrepreneurship, the design of new stuff as a result of our innate empathy towards others, is what makes us human. To strip innovation and ingenuity out of the human equation is to strip the very thing that makes us unique as a species.

Three signals that point towards this predicament, the end of entrepreneurial practice, are identified: Knock-Off Products, Feature Companies, and Product-Enhancing Products. In the 20th Century we saw an abundance of innovation – the Personal Computer, the Pocket Calculator, the Xerox Machine. I argue that, made visible by these signals, the current landscape of innovation is driven by enhancing that which has already been innovated, as opposed to creating that which is new.

Project Brief ~

These signals are a visible cry for help, a sign that the practice of entrepreneurship is on it’s last leg. My thesis offers a speculative alternative to human innovation by inventing the “Dehumanized Entrepreneur” (DE), a machine that aims to heroically aid mankind in entrepreneurial practice in order to raise dialogue around this predicament. The project is split into three parts: Creation (Dehumanized Entrepreneurship), Dissemination (Business PLANting), and Discovery (Deployment Strategies).

Part 01: Creation (Dehumanized Entrepreneurship) ~

DE is brought to “life” through the development of a Business Model Generator (BMG). The BMG is trained to create computer-generated executive summaries, the basis of any and all business. Why is this possible now? The three signals that point towards the end of innovation (highlighted in the above project conspiracy) show that entrepreneurship is beginning the journey towards it’s demise. The ability for humans to perceive problems is not currently distinct, but it is on the cusp of distinction, making right now the perfect moment to write this software – before it, too, becomes unperceivable.

The algorithm works by first creating a templated structure for the summary. This template is created by comparing a series of publicly available business plans, in order to create an “average” executive summary. While the algorithm itself is not true artificial intelligence, it creates the illusion of a complex AI system through the development of content creation strategies for the database the algorithm is pulling from. By designing and leveraging systematic strategies, the generated content becomes more removed from subjective human authorship. Also, the development of these systems for data collection aims to add to the project’s story as a whole by authoring the approaches in a matter that speaks to the components of my thesis (more on that in future posts to come).

As for the DE’s outputs – they are neglected upon export, and are to be immediately inserted into X amount of time capsules. The capsules will remain unopened in an attempt to preserve the content of the executive summary until the era of sameness, the time in which we run out of perceivable problems.

Part 02: Dissemination (Business PLANting) ~

X amount of these business plans will be planted in Silicon Valley, a space that serves as a metaphor around the world for innovation and entrepreneurship. I propose to create an additional system / routine (as implemented with the process of discovering words in Part 01) to objectively inform the placement and burial of each of these plans – a kind of extension to the algorithm itself.

The burial, and planning leading up to, will be treated as a hyper-documented performance of sorts. Each burial site will be marked with a plaque that will be designed specifically to welcome those who discover it in the future scenario (during the era of sameness).

Part 03: Discovery (Deployment Strategies) ~

The project will cultivate in a diegetic business meeting to be held the night of the final exhibition, April 18th. The meeting will be comprised of 3-5 entrepreneurs, and will be set in the time of the era of sameness. Live, for the duration of the show, the entrepreneurs will discuss the plan that was discovered under the surface of the earth, and will spend the evening conceptualizing the strategic means of developing and deploying the concept in order to bring it to market.

Rules: The company purpose will be unknown to the “actors” until the night of the event. The performance will not be rehearsed.

What is left behind: Artifacts of the brainstorm, as well as a visualization(s) of the lifespan of the plan itself (conception to birth to burial to discovery), will remain in the gallery after the night of the public performance.

Business PLANtings (test)

A test of using Google’s map maker to demonstrate where the business plans will be buried in Silicon Valley – more to come. Zoom out for the full effect 😉

Of course this is just an initial sketch – but the idea of creating a kind of mythology / “code” around the form of the placement pattern is pretty intriguing.

Database Content Creation Strategies

As mentioned in earlier posts, the business plan generator/algorithm works by pulling from a list of words that generate in the spaces allotted. It is stronger to use a process for authoring these lists that is objective (systematized / restricted) as opposed to subjective (authored by my own imagination) in order to provide the illusion of true artificial intelligence / automation. I have begun brainstorming strategies for discovering these words that matches the context of my thesis investigations.

The word-sets I need to create: “Problem”,”Opportunity”,”Market”, and “Genre.” Using twitter, a product invented in Silicon Valley (the target of the project), and the number 83 (the amount of cities in Silicon Valley), I have created a series of restrictions / guidelines for discovering each of these words. I have those posted here as well as some examples of what it produces.

Twitter (to find “problem”):

  1. search “annoying”
  2. “all” to reveal all related tweets in the search
  3. jump to the 83rd tweet
  4. record the “problem” evident in the tweet

Problems Generated: Shoulder injuries, Alcoholism, Bowlcut Hairstyle, too many drivers or over-population

Twitter (to find “opportunity”):

  1. search “awesome”
  2. “all” to reveal all related tweets in the search
  3. jump to the 83rd tweet
  4. record the “opportunity” evident in the tweet

Opportunities Generated: Talking animals, Bible Study

Twitter (to find “market”):

  1. search “these people”
  2. “all” to reveal all related tweets in the search
  3. jump to the 83rd tweet
  4. record the “market” evident in the tweet

Market Generated: People not from Seattle

It is interesting to compare this approach to word generation to the very first exercise I did in my thesis studies – the Serendipitous Business Model Generator (walking edition).

Business Generated: Park Benches for Dogs at Railroad Stations.

Moving forward, I will continue to collect these words as well as brainstorm and explore further strategies for objective content creation. I am interested in how the process of collecting can be one that is extremely extravagant and planned – almost an extension to the algorithm itself.

Algorithm Progress

I now have an initial working prototype of the business plan generator – this specific version is focusing specifically on the “company purpose” which is a short paragraph that encompasses an entire business plan. This is also the only piece of writing that the majority of investors require. The prototype is actually made entirely by using php. The algorithm, in this first stage, is essentially replicating the initial prototype I had, but is freed from the restrictions of using a third-party system. This allows me to house the algorithm on my own server, but also, obviously, is more flexible for further development and refinement.

I really enjoy looking at the source code behind such an algorithm – it is really interesting to see the “back end” of a generative business plan. Here are some screenshots:

The system works by creating an initial template ( a series of sentences) that have dynamic elements embedded within them. Each of these dynamic components (words) are then made generative by pulling from an archive of words related to that subject matter.

The system, in this stage, is not very robust – we only have 2 or 3 sets of words it is pulling from, but moving forward the following is being considered / tweaked:

  1. “Deep” crafting: instead of generating words, generate pairs of words / phrases. This will give a greater illusion of artificial intelligence, and will also form less of a noticeable relationship between the template and the outputs.
  2. Pondering different means of approaching plurals, consonants, vowels, etc…
  3. Should the company name be generated? Should that be left out, and be a part of what the human interprets from the machine’s output?
  4. I am currently compiling a list of openly available “company purpose” statements in order to analyze more the language used, and the overall sentence structures at hand. This will allow us to break the common elements / forms of these statements into chunks of data that can be grouped into the algorithm.
  5. “Flexible simplicity” is the method we are using – this essentially means an attempt to not have too much control, or too little control, but just in between the two.