Overdue for a post! General update.

Not going to lie, I have quite neglected the blog the past couple of weeks! My last review did not go quite as planned, but I have bounced back and am back on track with the project. Please take a look at my current working thesis abstract as well as a mock up for my final exhibition and descriptions of the 5 projects I will be displaying. More to come about each of the projects as they continue to develop in the near future.

View this document on Scribd

New Algorithm!

Hot off the keyboard…

I am planning on doing a lengthy post regarding to this entire experience of turning business plans into algorithms. Because the project has gone in a more narrative / conceptual terrain, the process of building the algorithm as opposed to the end output has become progressively more of interest.

System Map v4.0 + Business PLAN-ter sketch

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.

The “Company Purpose”

The argument of my thesis is that we (humans) will run out of ideas, and the ability to perceive problems, giving the illusion that all problems are solved, when indeed they are not, and thus making entrepreneurial practice obsolete. I am not arguing that, once these problems are laid out for us, we won’t be able to act upon them. We need an automated innovator, not a producer. Humans will always find a way to figure out how to make stuff work, that is not the issue I am concerned with. I’m interested in the lack of perception of these problems, the illusion that we are problem-less. Generating an entire business plan, then, would not be an appropriate approach to communicating this idea. Instead, I propose to build a robust Company Purpose generator.

The “Company Purpose,” or “Executive Summary,” provides a summary of all contents within a business plan. I am defining the Company Purpose as the basis of entrepreneurship as it represents the idea, or seed of an idea, as a whole. It is the only space in which the problem, opportunity, and solution are identified in one space, simultaneously. Therefore, every other component / section of a business plan exist solely to fill in the details of this all-encompassing statement – deeming them unnecessary components for this exploration. The following is an initial mock up of a potential structure for the more robust iteration of the “Company Purpose” algorithm:

This initial mock-up / prototype of the algorithm is an iteration on previous models, especially from the 1,000 Businesses project. This “template” is created by studying the patterns and trends within openly available business plans, on the internet, and averaging that content into this series of sentence structures. Check out some earlier iterations on this below:

These initial explorations were the seed of understanding how to discover the patterns in existing plans, but began to hold too much of a connection with mad libs. The reason I am not interested in that method is that it easily exposes the algorithm and, in doing so, eliminates the autonomous feel to the system. By adding more variations in grammar, etc. into this series of sentences, the template behind it’s creation gets lost – making it feel as though it were written by a human. I like that.

Business #00393 (FlyingSquirrel)

FlyingSquirrel is an aid organization comprised of a group of skydivers with a specialization in making dives into parallel worlds, made possible by patent-pending parallel-dive technology. With this technology, the organization is able to bypass the political and social structures that often hold back aid organizations from delivering help to their full potential. Breaking this barrier helps initiate rapid aid into societies that have experienced major collapse, without bureaucratic limitations.

FlyingSquirrel utilizes the skills and assistance of skydivers that are adventurous and interested in inter-world negotiation and rescuing. By transporting and “dropping off” skydivers in different worlds, the organization gains access to resources and information that can quickly be transported back to the mother-world in places seeking aid and knowledge. FlyingSquirrels’ skydivers function as hands to distribute resources to any world that displays a need for an extra hand.

The organization is currently working on developing ties with other universes in order to build a firm partnership to pave the way for providing aid in case of disaster, when the time comes. FlyingSquirrels is currently led by a board of directors: Ji Yong Park, Annalisa Swank, Hanlu C., Ellen Flaherty, Vina Rostomyan, Shana Torok, and Celeste Martin.

 

Business #00383 (uncut, LLC.)

uncut, LLC. uses green methods to publish printed books in their original, uncensored, form. The company focuses on designing systems that leverage alternative energy to publish, manufacture, and distribute formerly censored books in their initial form, with little to no detectable trace.

 

Business #00384 (Tentropy)

Tentropy is a 23rd century company that deploys modular teaching spaces, or “tents,” that bring order to chaos for educators that are leading classes in a context in which entropy has been reversed. Leveraging the opportunity of neuroscience, the tent material is made from a nanobot gel that can reform itself into any shape, including the exterior of the tent and the furniture inside. Using advanced neuroscience, the system records the brain of the teacher to reshape the physical environment to meet his or her needs, and to bring chaos to an ordered environment. The company was founded in 02012 by Joshua Fuller, Jaeson Kim, Zach Eastburg, Amy Wang, Jan-Michael Cariaga, and Kowoon.

Tentropy’s product, specifically, is the nano gel-coated tent which is currently designed to enhance the learning experience. The company currently has a working, 1:1 scale prototype of a classroom environment.

The space is made with their patented tent material, and has proven it’s capability to transform into any shape when the students and teachers react to them. By reading the teacher’s mind, the product activates the room, and changes accordingly. Upon launch, Tentropy will focus it’s Market Strategy towards private schools. The company is currently seeking angel investment and first-round seed capital to work towards a more accessible rollout in order to introduce the product to public institutions.