Is AI/Robotics the Next Humanitarian Crisis?

I am not afraid of technology.  To make technology is to be human. Technology and humanity are inseparable. My concern rests on the agility of our leading global institutions to engage technology in a manner that does not understand other forms of human organization. Technology is human, but it is not innately social. 

Nearly everyday an apocalyptic statement graces the headlines, announcing the threat of robotics to the global economy and patterns of day-to-day living. Supposedly by 2025, one of every three people will lose his job and become replaced by a robot. Five years later, in 2029, robots will have intelligence equal to humans. Artificial intelligence is the “biggest existential threat” to humanity.

These pronouncements do more to demonstrate a poor comprehension of the technologies and a complete disregard for economic history. Thousands of years of evidence highlight technological change is a healthy thing for labor markets. The counter argument is that emerging developments in robotics concerning automation, computer vision, data analysis, and machine learning are giving rise to a new kind of technology that is different from previous developments concerning hardware or information processing. 

The debate on the integration of computation and human society is as old as computation itself, and contemporary arguments remain rooted in the 20th-century invention of cybernetics, an experimental epistemology concerning the effective organization and communication of integrated social and environmental systems. Norbert Wiener’s theories on systems feed back and interactions with technology were situated within concerns of human ecology and governance. Weiner also projected the eventual creation of the machine a’ governor, a robot government designed to solve any problems to emerge within the government-human economy. In consequence, humans would be free to spend time at leisure while the machines replicated themselves, taught themselves, and effectively solved all possible problems for humans. All hail the machine.

According to roboticist Illah Nourbakhsh, in his book Robot Ethics, the critical difference is that while the developments of the 20th century provided the ability to simultaneously engage and manage multiple streams of information, but the advancement of robotics provides the capability to now distribute multiple forms of action. More significantly, the ability for robots to network, sense, choose, and automate with other robots will eventually lead to strange unpredictable configurations of technology that will blur currently held conceptions of social identity and accountability. To render this probability in the context of advanced capitalism, established modes of socio-economic production are at risk of obsolesce. It will be necessary to invent new modes of value production in society or human beings will be useless.

Wiener’s visions were not unique but predicated John Maynard Keynes' Economic Possibilities for our Grandchildren. Economist John Maynard Keynes predicted a technologically advanced future relegating the necessity of human labor to a mere 15 hours a week. There was some plausibility to Keynes' vision, as more efficiency in labor does not replace the need for human labor, but rather elevates the value of human labor relative to the demand for production. Of course, if there is a change in demand, and production is not sufficiently responsive to that change, then human labor is devalued. In the case of robotics, it is imaginable that vast networks of robots producing goods in relation to the algorithmic demands of big data would be more responsive to market changes than humans. Already this is evidenced by the application of algorithmic trading in stock markets. Humans can’t compete with networked, data driven, robots on mere market efficiency. The need for human supplied labor is replaced. The externalities are severe. 

Lost human jobs equates to a reduced tax base, reduced social services, lower quality education, reduced social capital, deteriorating communities, crumbling infrastructure, and stunted generational access to social and personal mobility alongside increased depression and substance abuse. To let this possible future, arise anywhere in the world is unconscionable. Surely this can’t happen of course because humans are capable and responsive. New markets can be created. 

Keyne’s argued that the remedy to competition with technology relies on the ability for humans to acquire the sufficient skills to make use of the technology. Keyne’s highlighted this problem as a “temporary maladjustment:, which might require several years for a labor force to catch up to the technology”. But what if the pace of technological advancement is faster than the ability for humans to learn? To close the gap is an ineffective a pointless conquest. In some places – existing urban concentrations of wealth and opportunity like New York City, San Francisco, Tokyo, or Istanbul – this will be true and educated people who inherited rich social assets will make use of the technology. But no one else will ever have a chance to compete.

Clearly, there is a bigger concern that does not emerge in these conversations on robotic futures: Is a world in which day-to-day livelihoods remain under constant threat of technology a desirable world? As the systems of production, exchange, and valuation that drive the technology are the choices and actions of humans, why then could the long term future of the world be one outside of our own choosing? Obviously, systems magnify behaviors, and complex adaptive social systems are virulent landscapes to contain. The complementary mechanisms and conditions of the global economy, such as systems of governance and human security, have struggled to keep pace with socio-economic demand and it is clear that this trend will only get worse.

There have been tremendous strides in the global economy in the last 100 years. International institutions have been founded to advance regulatory measures for human interests. Less people have died of illness or war. More people in the world have accessed education and social mobility than any century in human history. I am confident in those strides and those institutions. My concern rests on the agility of those institutions to engage a technological threat. The problem of Keyne’s “temporary maladjustment” continues to repeat itself ad infinitum, and when the world places blind faith in the advancement of technology, the learning curve becomes painfully steep.

The international humanitarian and human rights regimes are not contemplating this future impact of robotics. Grass roots organizations and urban planning departments are busy replicating the status quo. In the meanwhile, cybernetics hasn’t died in the minds of military engineers and industrial capitalists remain quick to take advantage of grand visions of a perfectly automated society. We are sold conveniences but in exchange purchase longterm economic servitude.  There is a demand for another approach, other ways for thinking and acting are essential to guide the advancement of human living. Buried beneath the layers of ideology and intent, our globally distributed modes of production and exchange all contain a shared thread and this thread elicits a new possibility for design.

Design Comes First for High Tech Entrepreneurs: Quantum Computing, Robotics, Artificial Intelligence, BioComputing, Machine Learning

While the design is increasingly central to the operations of established technology companies, it remains overlooked within research teams pursuing initial research of those same technologies. This is due to the lack of understanding held by high tech entrepreneurs about the role of design. They think it is decoration - when in truth - it is the fundamental process to transform abstract ideas into new realities. Design is the cornerstone to successful entrepreneurship.

In the last few weeks, I have had conversations with dozens of high tech startups that have one foot in the lab and one foot on the path to a new market. Interplanetary robotics, quantum computing, and super intelligent machines are exciting new domains described at length in business and technology magazines alike, yet these ventures struggle to overcome the leap from research in unstructured domains to generating meaningful human-product experiences and viable companies. They struggle to think and work like designers.

How Design Serves Advanced Technology Companies

Computers are everywhere. As more of our lives are inundated with computers - cars, planes, banks, security, government - the software on those computers is getting very sophisticated and difficult to test. It is also difficult to build to ensure that it is testable. Think of the complexity to manage all air flights in a country, the testing of that software is critical to everyone's safety - but with so many airplanes, airports, satellites and so on - how do you test it?

In my engagement with machine learning companies throughout Silicon Valley and Pittsburgh, I found one that has solved this problem. They build tools to help other big companies build reliable software for complex systems.  They can even predict if your company is going to create a bug before it happens. It is incredible.

The company does amazing work and is profitable. Yet they have a terrible website. They know it. Their tools do not really have a user interface that users enjoy using or easily understand. Their software is very advanced and difficult to communicate.  It is challenging to hire for this company. They see every market as possible and yet are not sure how to access them. Their work is in such high demand they are doing well... but will this always be the case?  What are the limits of their current market? How do they know?

Upon offering to help with the website, I've since had multiple conversations with the founder of this company. I understand the technical details of their software. As a researcher, I am equipped to study and understand the problems they face. I also am a designer so I am able to communicate it to people who do not understand.  Consequently, as a designer, I also have methods to rapidly TEST & LEARN from the range of possible consumers on how to tailor the language, the product, and the transaction. We do not, consequently, have to worry about marketing or even business development. With design, we can KNOW and VALIDATE our language, our image, our transaction, and our team to transform machine learning into highly needed customer solutions. With Design, we can better engineer success, not just software.

This is Different than The Current Business Models and Operations

At each startup, the conversation unfolds the same way every time.  I ask about the product and the CEO demonstrates or describes the product. I ask about the business and they display a prepared document or slide deck on the business strategy and organizational shape.  I ask about financing and they tell me about early mistakes made impacting future financing for the negative. I ask about the distinction between the market they set out to pursue vs the market opportunity they have discovered - and they start to get depressed.  I ask about new market or growth opportunities, and they say "we hope to figure that out soon... " and perhaps "if only we could hire the right person for business development."

Hire the right person for business development?

Certainly, the most important aspect of building a company is the team. Yet to assume that the success of the business - to align internal operations to market demand - is the job of a solo individual is misguided.  MBA programs tout the ability to transform graduates into such beings - and there are many times this person can hold an instrumental role - but for highly sophisticated technologies, there is no evidence that a traditional business approach will always work.  To make the assumption is high risk.

In addition, when I ask "do you have a designer?" - the CEO confuses my question, thinking I asked: "Do you have someone to make this pretty?"  They say no or "that is important, but we aren't there yet" or "we know it needs to be attractive so we outsource that, we have someone make it look good." The worst ones point to their current success and say "we don't need a designer, we are doing just fine" and months later are panicking because they had all along been meeting the needs of only one or two clients and could not actually scale their business. By their definition, to have a designer on the team is expensive and the person would sit around most the time with nothing to do. If they learn their lesson - it is often too late.

Design Driven Business is an Optimized Transaction

Every company requires some basic components - they need the product, they need an efficient way to generate the product, they need a clear path to connect to the consumers for the product, they need a very simple mechanism to exchange the product for capital, and they need the ability to do this over and over again.  If this process is well tuned, the capital acquired will outweigh the capital exhausted and the company can flourish.

Nothing in this product demands marketing, or branding, or financial planning. There is no need to hire agile coaches or communications consultants. I also mentioned nothing about aesthetic design. These things - these tasks - are simply tools to help solve the core problem: the material transaction. The material transaction is made possible by the optimized movement of information. It is possible to invest in these things to make that transaction happen, but within an unknown market for an untested product, operating on the thresholds of possibility... it is difficult to measure the effectiveness of these tools. Marketing language, targetted advertising, and agile product development are all just attempts to optimize the movement of information. Yet with advanced technologies, early wins are just as likely events of luck. There is no way to know.

Design is the Process to Optimize Transactions

Designers are specialized in the art of communication. This communication may take place through graphic text, plastic form, or even through the process of a work itself (this is where the post-its come into use). Optimized communication within a team will increase efficiency, to transform a team from thinkers to doers but with less technical debt.  Optimized communication to external consumers will get the two groups together faster for the transaction to take place.

Optimized design of the transaction itself - a form of communication - will result in high satisfaction for everyone.  If transactions are fast and positive, and communication from the buyer can connect to the team (who internally is optimized to leverage it by communicating), the transaction will take place again.

You can call this stuff strategy, marketing, team building, communications, - whatever you want. But to get from lab to market - you are better off to optimize your business by starting with Design.

The Demand for Fuzzy Science and Explicit Design: Rationalism is Not Universal

The article was initially published on 5/24/17 at www.humanitarianspace.com. It has been relocated here for continuity. 

Whenever I explain Design to non-designers, I essentially describe the scientific method - observe, hypothesize, test, repeat. Many designers work this way, although they might think creativity is something more magical, and are less inclined to describe their work as sequenced trial and error via prototyping.  Also, let's face it, clients will pay for magic but not for experimentation. The differences between science and design are more cultural than procedural. Science is better tuned to the needs of validation and design facilitates more generative insight, but the largest difference is that the scientific method is often stuck in the culture of science - we tend to think of the scientific method within fields like biology or physics, and thus resort to reasoning and intuition for day-to-day matters. Thus we think we use the scientific method all the time, but actually, we rely mostly on the power to reason.  

At the Thresholds of Reason

Unfortunately, human reasoning is not universal. Rather, it is situated in disciplines, as each discipline enforces a particular kind of language and patterns of cognition. The lawyer does not interpret the world the same way as the doctor or the engineer. Yet if forced to work together on a worldly problem, they will each insist on using reason (common sense) to engage problems of magnitude. Yet as each person in the room is attuned to a different way to frame and engage a given problem, it seems that direct experimentation would be more valuable. So why the fear to experiment for results? 

There is a widespread predisposition to assign the scientific method to the profession of science, wherein science is only for science-y things, and likewise,  we assign design methods to the design professions like architecture. Why is the method of science only reserved for biology but not for daily action? By extension, we are prone to work through other disciplinary problems in discipline-centric methods- and those methods are all the same, but get diluted by disciplinary language and habit - so why don't we step outside of our discipline with our methods? Most MBAs for example, never do scientific experimentation, although it is the premise of all worldly knowledge. They might embrace a design workshop (as business has to embrace superficial elements of design), which is a good way to synthesize ideas and create opportunities, but it also is a high-risk endeavor because it lacks any direct role for validation - it is a synthesis of assumptions. In contrast to generating learning opportunities, we rely upon the idea of the "expert" who has knowledge based on previous experience and we expect that knowledge to cross over. 

In contrast, we resort to rationalism and rationalism relies heavily upon our subjective interpretation of previous experiences - and our memories are not the best way to record the world because memory is highly subjective. Design is an approach to understanding the world through observation and experimentation, yet it also considers and takes advantage of personal subjectivity, cultural patterns, and the emergent outcomes of group interaction.  It does not merely accept the implicit assumptions and detriments of subjective memory. Design attempts to leverage those things actively avoided within Science. We could all probably use more science in our lives.

Dangers of Expertise
Dropping expertise within approaching a problem is important because expertise is really the opposite of science and design.  Expertise makes the assumption that experimentation is no longer necessary because the expert has all the necessary information already or can quickly filter available information.  Experts supposedly already did all the hard work to understand a particular worldly pattern. Expertise can be reasonable - in any given problem, we all find a point in which additional experimentation is redundant, and an outlier result will have little statistical significance. Yet what happens when we combine this statistical argument with the psychology of a group? In this context, the value of expertise is diminished because of a necessity to all reach a common understanding. Without a process of experimentation, people will talk about things they understand as individuals with poor translation to others. They will impose known patterns upon new problems. They will fail to experiment.

Patterns of Cognition: Habits
Later, studying robotics at Carnegie Mellon University, this way of thinking was entirely beneficial to advance the state of human robot interaction.  How do you build stronger human relationships with technology? It takes more than mere manipulation of pixels or a human factors design assessment. Yet when I was a graduate student in economics and law, this way of thinking was not helpful.  In fact, economics never touches the same time of reasoning. Economists are looking regularly at the circulation and balance of inputs and outputs - a far more linear and sequential type of thinking. Law likewise was a headache to study, as legal reasoning relies more upon the accounting of evidence to identify conflicts of logic. The reasoning of pattern relations - whatever they teach in art school - has far less to do with sequencing or logic. These different paths to engage and interpret the world establish radically different understandings of a given problem.

The notion of "patterns of reason for knowledge construction" is perhaps a critical element in how we all approach problems. When I was an art student at the Art Academy of Cincinnati, I developed a way to see and understand the world that was not explicitly rational or empirical. It was more akin to pattern recognition at large, the ability to identify and symmetries/asymmetries amid abstract collections of information.  Studying historical and contemporary art, I became attuned to the relationship between cognition and body mechanics and to the relationship between physical materials and economic production. I learned to look at the associative relationship between materials and ideas.  For example, I recall seeing a photo with a dogwood tree, and by association bring up a legend about how the dogwood tree is cursed by God to grow crooked. This story implies personal meaning, and thus as an artist, I am inclined to use the tree to indirectly communicate something about religion. Compared to basic arithmetic, this way of thinking makes no sense, and yet, moving through the world by association is an important part of the human experience. 

Scientists attempt to identify and negate their values, yet there is sufficient evidence that all science is situated in the subjectivity of the scientist.  Many scientists accept this.  Good designers also take stronger responsibility for their own values and situated knowledge. Yet both can also discover other paths of inquiry and reason.  In the end, the only ones who fail to adapt to new problems are those who rely explicitly on their own situated reason.  The world is too complex for experts.

The Art Academy of Cincinnati - Education to be Radical, Relentless, & Radiant

The article was initially published on 5/19/17 at www.humanitarianspace.com. It has been relocated here for continuity. 

I was deeply honored to give the commencement speech to the graduating class of 2017 at the Art Academy of Cincinnati. These last few days, I am now continually reflecting upon the unique and powerful proposition this school makes to the world. There is no other school like it. 

The only other college to which I can compare it is the mythical Black Mountain College of the 1960s that produced revolutionary minds such as John Cage.  To plagiarize someone else’s story, the Art Academy (AAC) doesn’t merely graduate artists or designers, it graduates the critical but hard to find team member of every successful business: 

"there are three kinds of people you want to launch a business: the person with the idea, the person with the financial sense,  and the person who makes you say 'what the fuck?' The last is the person who can rip ideas apart, remix them, and flip everything upside down to generate breakthroughs that no one else can see."  

Blackmoutain College w/ Buckminster Fuller

The last kind of person is particularly hard to find. Many schools can teach people to become accountants or to be entreprenuers but no school teaches students to be intellectually rebellious and operationally radical.  Except for the Art Academy of Cincinnati. No joke. It is even in their mission statement.

Everyday books about Innovation, Design, and Economic Disruption churn through billions of dollars in annual publishing sales. Parallel to the publishing industry, countless institutions argue they offer an education that will transform students into innovators who will change our world.  But do these industries actually generate the change-makers we seek?

In the last ten years, I’ve been fortunate to spend time at the world’s best universities as a speaker, student, or instructor including Oxford University, MIT, Harvard, Cornell, and Carnegie Mellon University – and these are indeed great schools.  Their students are brilliant and the faculty are more than competent. The programs are well funded and the students are nearly guaranteed the security of a well-paying job upon graduation.  These schools also attract people who already have a history of success - when Elon Musk attended Stanford, he had already earned degrees in Physics and Economics. Yet I have never encountered another school that transforms unknown students into true innovators.  In fact, when I recently taught Design Thinking at an East Coast top-tier MBA program, my students complained the entire time about the lack of clear directions and the constantly shifting parameters within the course requirements.  I have since learned that this complaint is exceedingly common within MBA Design degrees. These programs are forcing square people through intellectual circles and many graduates come out very little changed. 

2017 Commencement Address

Do all art schools impact students to think so differently?  I'm not sure... there are many art schools in the world. My sister is a student at SCAD. I have friends as RISD. When I was a teenager, I lusted for the attention of the San Francisco Institute of Art (SFAI) and the School of the Chicago Institute of Art (SCIA).  Unfortunately, in 1999, I had so little money for college, I did not even have the 50 dollars to apply to any of those programs let alone all of them.  With little hope to attend any college, I drove my broken-down ‘91 Geo Prism to the Art Academy of Cincinnati for a Portfolio Review Day in mid-October, to present my high school artwork to various colleges.  San Francisco was there, as was Chicago, and at least a dozen others.  Chicago offered a partial scholarship on the spot, which was incredible… yet, as I did not have the money to apply, let alone to live in Chicago, it held more symbolic meaning than opportunity. I was nonetheless motivated at that moment to find a way to go to art school.

Weeks later I happened to cross paths with some artists, Aaron Butler and Christopher Daniel.  Aaron worked at the Art Academy of Cincinnati and pioneered the experimental music group, Dark Audio Project, while Chris was a metal sculptor who went on to found the extraordinary and thriving Blue Hell Studio. They both held Art Academy ties, and with their encouragement, I decided to do everything possible to earn a scholarship. I applied only minutes before the deadline, in person, submitting my application in a massive wooden box crafted from an old PA system pulled from a dumpster in Kentucky (at Aaron’s suggestion that I make the physical application somehow stand out). As a mediocre student in high school, I had only applied to one other school at the time – the globally exceptional design school of the University of Cincinnati, DAAP – and I was not accepted.  The Art Academy took a chance on me, offered a scholarship to cover more than half of tuition, and I will be forever grateful.  Notably, after later graduating from the Art Academy, I received a full scholarship to DAAP for graduate school.

Art Academy of Cincinnati

Visiting AAC this last weekend was not only nostalgic – it was inspirational.  The Art Academy is a weird place. It consistently takes chances on people like me. It is a community of outsiders. It pushes them to build expertise on the ability to make something new  – which is not typical, considering most degree programs demand students acquire knowledge on a longstanding subject or methodology. It pushes students to invent new models of production, new identities as artists, and to take life to the frontier of possibility.  Graduates of the Art Academy of Cincinnati do not need books on creative problem solving, they need wicked problems where all others have failed.  If the Art Academy has a flaw, it is a simple fact that they do little marketing or high-profile partnering, and consequently, the world knows little about this school amid an insatiable demand.  The Art Academy of Cincinnati is not a diamond in the rough – it is a silent A-bomb in the exosphere.

My life has changed much since I attended the Art Academy. I am writing this blog entry while on a flight to San Francisco. Tomorrow morning, I will run a series of design strategy workshops for a Venture Capital firm in Silicon Valley to explore new investment models for Artificial Intelligence. Since attending the Art Academy, I have lived in multiple countries, built companies, and am fortunate that my abilities to tackle entrenched problems in new ways are continually in demand. When I think of the year I started college, 2000, my life is now very different from the future that was most likely ahead.  Though I have my fair share of life challenges, I have a wonderfully creative and satisfying life. It has been a hard journey, but I credit the faculty and students of the Art Academy of Cincinnati. While most colleges chart a path for your future, the Art Academy provided a compass to guide me through the deep woods of the unknown.

Advancing New Economic Models by Design

The article was initially published on 5/24/17 at www.humanitarianspace.com. It has been relocated here for continuity. 

A few weeks ago I had to opportunity to spend a couple days at the Urban Planning Department of Cornell University. I was impressed by the graceful way this group was able to move fluidly between rigorous quantitive analytics and participatory public processes.  Yet for all the brilliance I found among faculty and students, it became clear to me how much urban planning education lacks sufficient focus on design methods.

I am not referring to design as urban design or architecture. I am referring to the ability to translate a series of complex social and technical processes into physical form.  I am referring to the ability to translate ambiguity into action and to oversee the transmission of that action to generate results. This is not the same as project management or the mere practice of the profession.  Rather, given the massive range of assets are available in urban planning for engagement and analysis, the discipline completely lacks a rigorous methodological framework for what actions to implement by consequence of the planning process.  If the discipline of urban planning stops with pitching the plan - then planners deserve to be disappointed when their work does not reach fruition.

This realization explains much about the disappointments of the planning profession - such as the constant repetition of "off the shelf" solutions such as green roofs, walkable streets, and historic main street development initiatives. These tactics are fine - but why such a small range of possible outputs in a world of more than 2.5 million cities, towns, and villages?  Basic statistical intuition suggests that a profession dedicated to building new futures and generating new economic development initiatives would capture a broader range of possible solutions.

To consider the urban planning process is to recognize that it remains rooted in a Waterfall design methodology - which has been proven to drive up costs and reduce stakeholder participation.  Most socio-technical systems have long since discovered that Waterfall methodologies fail to consider the variability of human actors, and thus tend to fail.  While organizations continue to search for replicable solutions utilizing scientific research designs and clinical trial models, the assets of localized place-based development go ignored or fail to scale.

Private sector technical sectors have shifted toward lean frameworks, agile methods, and other systems rooted in rapid feedback to avoid the high risk approach of waterfall planning. Unfortunately this understanding has yet to see the light in American politics where sweeping legislative action is the norm - not iterative improvement and variation. Urban planning, a field long aligned with design, has an opportunity to update to the 21st century - but it needs to start in education.  Design is more than architecture, it is the execution of ambiguity into meaningful consequences. 

Unlocking Machine Learning with Human Centered Design

Human Centered Design (HCD) is at its core, a process for eliciting the practice of human values and embedding those new values into a new artifact, process, or service.  It is perhaps challenging to consider how HCD may have a role within something as quantitative as Machine Learning, yet HCD can be an important component to the formation downstream benefit for machine learning services. The value to injecting HCD into the design and use of machine learning solutions is valuable to contribute to unknown opportunities and risk the threat of future-tense machine learning decisions.

Quick Intro to Machine Learning
Machine learning is best described as a methodology for a computer program to learn to write new programs without human assistance.  There are two primary domains of machine learning - supervised and unsupervised. Unsupervised machine learning utilizes various statistical functions to identify patterns in information and to extrapolate decisions from that information.  The reference to Big Data is often a use of unsupervised algorithms that are, at their core, a regression analysis. Supervised Machine Learning also leverages statistics, but to do so, relies upon a body of data curated by an individual.  The computer program builds an understanding of the data to replicate it or to use that understanding as a filter on other data.  For example, if 2000 images of a tree are shared with the program, it will use those 2000 images to build a concept of a tree, and will then be able to identify a tree from a suite of images which may or may not contain trees.

Of Machine Models and Human Cognition
To build an HCD approach to Machine Learning, it is important to first distinguish how machine learning is similar and different from Human Learning.  First, both the human brain and the ML program do have a core similarity - they are massive engines of statistical pattern recognition.  Our brains engage and understand the world through pattern recognition. To see an apple, know it is an apple, and understand what an apple does is in fact a massively complex undertaking.  The connection between sight and concept requires our brain to internalize and make sense of about 8,000 different points of information... how light bounces off the surface into our eyes to inform geometry, color, shape, texture, and subtle visual implications of weight, density, and so on that are difficult to articulate.  When our brain has sufficiently identified enough information bits to create a pattern - the apple pattern - it has built a conceptual model.  Our brains are massive repositories of conceptual models, and we use these models to discover new ones. "That is not an apple but it looks like an apple."

Machine learning programs are similar (as most are based on our brain's neural architecture). They use many different methods to analyze information and identify a pattern, like "tree."  Yet their methods are various different from ours, such as rapid quantitative measurements between corners, measurements of curvature, measurement of gaps - and thus generate a very different kind of model.  The ML model is not interpretable to the human - nor does it need to be. The result is the total information required by a computer to make inferences about other possible models.  A computer, also, has limits and it is difficult to make inferences. Thus a human can rapidly intuit a new situation "apple by a tree suggests apple tree," the computer will only see an apple and see a tree,  unless enough information is presented about apple trees to make the leap.

The Learning and Reproduction of Human Values
Machine Learning asserts a transactional value by  using found pattern within a set of information to predict how that pattern is extended against a new inflow of information.  We can use ML to facilitate market analysis, reduce risk in complex situations, and optimize organizations. Yet what does this ability inform... optimize how? Reduce risk to do what?  HCD is a process that requires the designer also conducts pattern analysis - but a key distinction is in the domain of qualitative patterns.  How does a user feel about a given situation over time?  How can that situation shift over time to elicit other feelings?  The intersection of opportunities between HCD and Machine Learning are vast when you consider the bigger context of the data.

Within supervised machine learning, the assembly of training data could be done in bulk or it can be curated.  A designer can pay close attention to the data... what kind of trees are presented? What health are they in?  Can multiple sets of training data be presented that contain healthy trees, sick trees, and variations in environmental conditions?  By curating the data, the human agent can tune the specific types of patterns to be determined, and thus generate greater value later.  Not all data is equal.

Likewise, as the outputs are generated from an ML program - such as new sets of data (often relayed in a dashboard or a customized suggestion), the human experience of that new data point can be catalogued and leveraged to tune the ML program.  While there is such a thing of "Human in the Loop," to aid the precision of the algorithm, another way to thin about this is "Value in the Loop," to reward the program for generating particular kinds of experiences. While abstract, the program does not need to understand the human value - it will build its own conceptual model to make sense of the behavior.  These values can tune the algorithm to new directions over time, wherein we can expect the algorithm to generate new data points very much unlike the original yet of great use.

Geographic Variance in Modelling
A critical aspect of being human is the variations that exists between humans for perception in the world.  Within small groups, language can take on a range of meanings, while across geographies, the variance of meaning ascribed to concrete things can be quite large. Machine learning generates one kind of solution for one kind of person and then generate a new solution for another kind of person... it can be granular, like Netflix, to suggest customized outputs.  Yet what about machine learning applications for broad data sets that are irregular?

Within the design community, I advocate close consideration of the role of place and data. There is a tendency for developers to look to large open data sets. Vast databases exist with training data (like this popular one at MIT).  Yet consistent with my above comments, what are the values which drive the labelling of this data?  For example, if you apply this training data to images of Mogadishu, it will generate results like "earthquake."  Not only is this inaccurate - but the correct label will vary depending on who and where the labeler resides.  A Somali in Nairobi will give a different answer than a design student in Boston to describe a set of data.  Yet most Somalis in Nairobi will supply more similar labels.

Consequently, it is not enough to parse data by meaning.  One must also ground the meaning, and to do this, I suggest the use of classic lat/long coordinates.  If we can build rich databases of place-based data and place-based identification, we can do more than build intelligent softwares, we can build softwares that are flexible to the global shifts in meaning and identity which are traditionally at odds with the demands of computation.  To fuse machine learning with the vast ocean of human value creation and reproduction is a great opportunity for the future.

The Execution Gap : Bottom Up and Top Down is a False Paradigm

Over the last 16 years I have spent a lot of time with people who have committed their lives toward social change.  Historically these people were architects in rust-belt cities and then aid workers in refugee camps.  Later I worked with lawyers and government officials in government offices. Today I continue the same work, but work with electrical engineers and data scientists. As I continue to engage the same kinds problems but across wildly different disciplines, it has become clear that many of the deeply entrenched beliefs of one group are completely foreign or irrelevant to another. 

For most of the 100+ year history of urban planning, the cannon of planning theory describes top-down vs bottom-up processes. Various terms are used to describe this such as "centralized vs decentralized" or "rationalist and grass roots." A few other sub-models of planning have emerged, such as Advocacy Planning and Participatory Action models. What has been less dominant is a recognition that our technologies are a big part of the planning process. Consideration of the technology as an equal force throughout the process opens many new directions for exploration. More importantly, technologies such as machine learning, when applied to dormant low-tech economic sectors, are far more powerful change agents than people. 

A critical question that has been consistently absent from planning theory concerns use of the appropriate technology relative to the complexity of the problem and idea. Why spend a million dollars, engage hundreds of people, conduct dozens of technical studies, and then document a robust vision for the future in only a book or a website? How do these technologies advance the implementation of the vision? These technologies increase risk because the translation of a complex vision from written text into built form or economic process is a massive leap.

There are other ways of course. Through small scale pilots, rapid trials, online Wikis, collaborative mapping sessions, partnerships with local technology companies (big and small), one can always find a way to create new experiences.  A classic example is from the Czech Republic; after the fall of communism a process was needed to rapidly educate the public on the history of their nation which had been suppressed under totalitarian rule. Rather than merely release a book, the city of Prague presented a living timeline of Czech history. Maps, photos, documents, and didactic panels lined the main streets so that citizens can walk through the history of their nation and learn how one era informed the next.  Woven into the architecture, the city itself functioned as the core technological platform to engage citizens and enhance thinking on the needs and futures of the nation-state.  As a robust experience, it also created stakeholder buy-in for the next logical step.

Design as a Process of Optimization

To develop a concept and not implement as part of the ideation process is high risk. Working at the EPA, for example, I find it very hard to convince leadership and scientists that a particular organizational or technical change is possible. For many decades, people have made suggestions and introduced new visions, but rarely have these been successful.  In contrast, I find a way to rapidly prototype every idea. Should we create a new division? Lets get some people to volunteer and test how this division might operate. Should we purchase an enterprise solution?  Lets get the 30 day free trial and see how we might need to also change if we buy it. Rapid prototyping any kind of solution will always give more information and reduce risk.

Planning and Design as Synthetic Futures

As Herbert Simon pointed out, the scientific method is an excellent way to understand the natural world that is around us, yet when faced with creating something new in the world - and thus artificial - we turn to design. The more we understand the design process, and how it is similar or different from the scientific method, the better we can design and introduce new things into the the world.

The role of design as an additional demand - to not only stretch and reveal what is possible in the future - but to help us get to what is preferable future. Empowered with the ability to make something new, design is a means to consider multiple future realities, and equipped with the ability to implement them, each idea on a  possible future exists in a state between idea and reality. Consequently, mindfully experimenting with a material process to test implementation measures and reduce the risk, one develop synthetic futures and transform them into new realities.

Impact Any Problem Like a Designer

This morning I was asked if I approach design management (the emergent term for the application of design to organizations to engage complex problems) as an architect or as a communications designer. It was a little hard to answer is because the answer squarely falls into the domain of neither and both. While I believe whole heartedly in a non-disciplinary approach to design, if it is necessary to specify a form of design practice and theory, it is important to recognize that these fields exist on a gradient. Over the last 15 years of research and practice in design and urban planning, I have developed a systematic approach to structure problems and interventions across this gradient and have developed a simplified conceptual model in response to demands.

Illustrated above, I look at all problems as fitting somewhere within the above structure - wherein a problem might be defined by thought and language (sign), by tangible products and interfaces (object), by spatial context (environment) or by large scale invisible systems such as formal law and culture (culture can be considered another expression of law).  So for example, if you are attempting to solve a big problem like poverty, it exists in all sections because poverty is contextual, has artifacts, and there are many existing specific words and images that are used to communicate the idea of poverty. Whereas a problem that is very well defined, like the design of a toaster, will most likely sit squarely in the domain of objects.

At Carnegie Mellon University, I was introduced to Richard Buchanon's theory on the Four Orders of Design, which was very similar to my own model, but we maintain very different objectives and I found his model is harder to operationalize.  Buchanon does have other variations,  and additional work on operationalization has been pursued by Golsby-Smith.  There are additional models out there and while I find it validating and interesting to look at their models, my own approach emerged from the field. It is not informed by these other works, I point them out merely because they exist, and I find these other frameworks are missing a critical component, the people.

Within my framework, the most important characteristic is the recognition of dispositions held by people who occupy each conceptual frame. Without people - there is no framework.  There are no objects or contexts without people - there is also no design or strategy - people are the scaffolding of everything.  Consequently, I do not consider this framework as universal, but is thus far, a model that has arisen organically through various design interactions with people, technologies, and spaces.

Yet people are highly complex. I cannot manage to engage all people in every project on every level, and therefore I have created over the years a simple heuristic to note critical attributes of people within a project which will determine the project outcome.  All stakeholders in a project have, want, or lack resources (for their interest or mine), they likewise all hold a unique vision for their lives and the project outcome, along with specific objectives, beliefs, expectations, and baggage from prior experiences. I cannot juggle all these balls for every person at one time, but I do attempt to establish an sense of organizational structure between different actors and their unique attributes.

The Difference of Design in Organizations
Lets imagine an international company hires me with a big problem phrased as a simple request, "how do we become the leader in our industry?"  When companies have approached me before, they have already conducted many of the preliminary SWOT assessments and strategy planning sessions. Perhaps they have utilized a more traditional business management strategy, but found the problem too sprawling to meet the discrete demands... for example, it is impossible to identify and validate appropriate benchmarks if the problem itself is poorly defined. Driven by market research, they believe they should offer the same technologies or assets as their competitors. Yet it makes no logical sense to mirror competitor if you want to be the industry leader. It is important to do something new - but what and how?

Using the Framework to Generate the Big Picture
As a complex problem, I will work at all levels of the framework. In the case of robotics, I will take this problem and build a detailed understanding of their robots (the object).  I will look at all documentation, branding, communications, and language used in relation to their robots (sign). I will go into the facilities where the robots are used and spend time understanding the relationship between the robots and the Environment. I will also look at sales trends, labor laws, social movements, international trade agreements, and latent technology trends (perhaps also concerning language, objects, environments) to capture a big picture understanding of the robots in relation to some invisible systems that shape the future of the company.

Digging Deep into the Social Terrain
In this process, however, I have left out the most important component: the people.  Who is talking about the robots? Who is listening? Where are they? When customers purchase the robots, what are they saying? How do they represent their needs?  In the environmental context, who works with the robots and how?  How do those people exchange information about the robot in that context?  More importantly, how does the robot relate (or not) to the resources, objectives, histories and so on, of every person at every level?  If I go to the capital and talk to the people shaping policies that inform the outcome of robotics markets - congressmen and lobbyists for example - what can I learn from them?

Insight by Emergence
Working through this framework to understand the problem is only the first step. Yet the more I can build knowledge at each level of interaction, the more flexibility I have to craft and test interventions. Perhaps the corporate strategy is something simple like a branding campaign or promoting a national policy - yet perhaps it also requires manipulation to the technology to better facilitate how other companies train their employees? If that is the case, what language should be used and by what device should it be communicated? By means of this approach, the key insights and opportunities will emerge and do not need to be invented - nor can they be predicted.

Impact by Design
The final outcome of such a problem will rarely consist of one single action.  Rather, it will require many small interventions choreographed across the system.  Some interventions are more important than others. To describe the processes on design for wicked problems deserves more attention than I can provide right, yet with this framework, one is equipped to better understand any kind of problem to get going in the right direction by doing the following:

1. Get away from the tunnel vision of a personal discipline or expertise
2. Build an integrated and fluid systems understanding of a problem 
3. Identify many points of intervention across scale/scope and points of view
4. Leverage the most powerful yet high-risk asset of any problem, the people.
5. Uncover new opportunities for exploration and testing

Beyond Ideological Innovation - Into the Methods, Concept and Experience

I just spent the last two days observing members of an entrenched government and business community learning about principals of lean startup, design thinking, and agile processes.  That was a good thing and I'm proud of this community for walking down these roads.  By taking these steps they will be better equipped to produce strong results and different kinds of outcomes. Yet we should not confuse this set of practices as innovation.  There is a distinction between innovation methods, innovation as a concept, and innovation as an experience.

Methods

Innovation, as a term, has come to describe a set of methods to drive new forms of socio-cultural and economic production.  This brand of innovation, like all other ideologies, was borne of necessity within particular economic conditions. The rise of the merchant class advanced Capitalism, the Industrial Revolution pushed forth Marxism, and Silicon Valley delivered The Lean Startup. Like any ideology - complexity, nudge, sustainability to name a few others - innovation has been appropriated by corporations to benefit their bottom lines and advance their missions.

It is easy to identify when the concept of innovation has been repackaged for consumption. If you participate in a workshop concerning Innovation as Design, you will likely have to do something with post-it notes and white boards, and maybe have to participate in low-fidelity rapid prototyping exercises.  If it is concerned with Lean Startup, you might take a standard idea shared with your team then quickly go call a few customers to ask if they like it or make a mockup for them to test.  This is also the general description of many UX Design education programs today.  These strategies at their core attempt to reconcile the simplicity of the scientific method with the irrationality of social behavior.

These procedures are all good things to do. I do these things and teach them to others. More people should try them.  Is this innovation? Sometimes, but not necessarily. These processes can provide pathways to innovation, but more acutely, do more to provide pathways to success according to an already existing - but perhaps unarticulated - definition of success that is situated within the minds of the participants.  To satisfy the demands of a  shared disposition is not the same thing as realized innovation.

Concept

We must consider innovation as something far more powerful, a force at work within a paradigm shift.  When Thomas Kuhn wrote on the structure of scientific revolutions, he described the paradigm shift as a social process, in which an accumulation of outlier evidence - over time - suddenly sways social beliefs to then become the new normative reality. When the earth was believed to be the center of the universe, attempts to research the universe often validated this belief or were built off of the assumption. It was held as fundamentally true, and to simplify history, Galileo was executed because his evidence contradicted the belief. Over time, enough evidence accumulated in favor of Galileo's argument and the community changed their belief.  In consequence, a new era for the intersection of science, religion, and society was borne.

There are things like religion in which one must work to hold a belief - to have faith - and there are things we just believe outright, such as the shared experience of a color or the weather. Within this distribution of beliefs, science is held as objective and the scientific research method is void of human error, but we fail to consider how science or faith is founded on a widespread predisposition.   In theory we eventually learn to identify our collective errors and we pivot or manage the constraints. When that happens, the school text books are rewritten as the sun becomes the center of the solar system, the universe grows in size, and space/time is a fluid dimensional fabric we believe in but struggle to understand, because the evidence is at odds with day-to-day human experience.

Innovation is not about design, science, or lean frameworks. It is the distribution of events that brushed up against each other so as to transform the entire normative experience of reality. These moments could be anything - scientific evidence, a new idea,  an observation, or a conversation. Some of these moments might be brilliant and profound, but many are just outlier fragments and glitches of daily experience.  We like to imagine innovation as a singular act, but singular acts have limited force, and thus the power of innovation relies upon loose configurations.

These individual events have limited power as a singular instances, but in coordination, can become a fulcrum of radical difference. A strange turn of phrase, uttered at the moment a butterfly lands on your arm, might unlock the gateway to a new personal ontology.  When this moment happens en masse, all possible roads into the future shift toward a new direction of possibility.  When Latour wrote Artemis, the failure of the high-tech transportation system was described as a network effect of many flickering and semi-related life moments. Latour wrote this to prove that social systems do not exist but are merely perceived... yet when these flickers do align into a system?  That is innovation.

Obviously this approach to innovation is too complex for a corporation to adapt because it cannot be packaged as a playbook or a method.  It cannot be entirely designed and it cannot be diffused or appropriated with ease. The Cult of Innovation fills much of the demand for change but in 10 years, our corporations and governments will look to another trend for answers because the more tightly packaged a concept for distribution, the less that concept can satisfy complex organizational needs. A truer path of innovation will not be appropriated because it is a plurality of outliers, and the core of its value is a contradiction to what we hold correct. Design or lean tactics may bring us to innovation - but I suspect this happens with less frequency than we believe.

Experience

Often if something is innovative, we do not like it. We dismiss it.  It pushes against our values, rubs us the wrong way, and introduces friction into our lives. We can adopt methods to reduce pain or mitigate risks, but ultimately, change has a cost, and that cost is at times the very foundation of whatever we believe to be real and true. Innovation is painful because it forces our brains to work differently.  If you witness a singular event and consider it brilliant, it is only a good according to subscribed preconditions.  By this definition, Elon Musk's Hyperloop is a good idea, not innovation. It might be an innovative act with in lattice of other acts, but we will need to stand back and observe.  

We usually only know if something was innovative in the past tense.  We believe in historical periods and future epochs such as the renaissance, the 20th century, and the information age.  We do not know how to experience time in other ways and yet we also have no ability to determine if something is ending an era or creating a new one.  When a radical disruption creates discomfort and only appears to be situated within the broader trends of the present tense - then we do not call it innovation.  We expect dramatic paradigm shifts to be immediate and identifiable - but this is misguided thinking.

As innovation is ambiguous in time, diffused in activity, and dissonant in experience, we would benefit to stop repeating our expectations of innovation as 'sudden, concrete, satisfying and specific.' Changing our perception of innovation might give us a better path to embrace it. And later, when the dust has settled, we can look back to say innovation has happened, though we may not be able to repeat it. 

Formulations of Post-Conflict Reconstruction Beyond and Within

Aerial Image of MIT during WWII from Lamelson Center for Invention and Innovation

The world will always have war and poverty. There is also no moral justification for the nature of war or poverty to be as severe and punishing as can be found throughout much of the world. Diseases can be reduced, incomes can be increased, and war can be less violent. It would seem that simple and practical solutions - common sense - could solve many these problems. But I've found over the last 15 years or so, that common sense is often the point of failure. True innovation is irrational. Systematic methods can be designed to facilitate innovation, but the starting point is an entrenched understanding of the problem.

Today's wars do not end, are rarely state-to-state engagements, and technology has shifted the capability of the non-state actor, giving individuals power on par with the state. Yet if technology can empower individuals to create chaos and fight the state, then it can equally empower an individual with state capacities to create peace and opportunity. Where terrorists destroy the present tense, an individual - not a government can likewise create a new future tense. Stability-minded, entrepreneurial individuals are the antithesis of terrorism, not government employees.

Inspired by organizations like Independent Diplomat, I went down this road as an urban planner, and built a private business for governance. In this capacity I aided governments in Afghanistan, Kenya, and Somalia for several years in addition to advising multilateral institutions. Unlike many of my peers in the humanitarian and development industry, I never once provided a report as a project deliverable (this is no easy thing, given that the entire industry is obsessed with reports). Rather, I focussed on building concrete mechanisms and leveraged technology to perform necessary change within entrenched problems.

The interesting consequence wasn't so much within the success or failure of those mechanisms, but the way other institutions responded. The best known example is how the UN restructured its Somalia efforts to compete and then later appropriate my municipal-level urban technology center in Mogadishu.  Years later, that effort has faded away, but a key lesson remains intact: if you want to change the operations of ]global institutions, a faster method than advocacy or protesting is to beat them at their own game because they fear competition.

For awhile, I believed that I had gone as far as I could personally take this work in postwar reconstruction. Over the years, marriage, fatherhood, and the brutal realities of active war zones left me believing that I had dug in as deep as possible, and that it was perhaps time to look into the future and shift gears.  I set new targets far from the front lines, leading to doctoral studies and a deeper immersion into the technology. Making an honest departure from the domain of reconstruction was valuable as it exposed me to new ways of thinking and working in addition to the acquisition of other skills.

Yet today, I find myself working in governance in a refreshed capacity. As an innovation specialist for the US federal government, I essentially work as an entrepreneur in residence. In this capacity I am approached by, or reach out to, federal agencies with deeply rooted and complex problems in search for new vision, strategies, and tools. Much of this work has been connected with Veterans Affairs, and thus my work within the domain of post-war reconstruction continues.

When rebuilding a wartorn city, or considering the future reconstruction of a city presently in war, I have always thought primarily about the actors there - in that space - and those who grew up there but left. Consideration of conflicted and secure space were constant to the extent that it gave a name to this blog. But in the way that I work, space is merely a container for relationships between people, and in the case of Mogadishu, for example, the stakeholders were the Somalis and the AMISOM soldiers (among others on site).  Throughout all my years of working, I never once thought about how the lives of those AMISOM soldiers will continue to influence the stability of Somalia upon return home.  Post-war reconstruction is not the rebuilding of a place - it is a web of flickering interactions between people, perceptions, objects, places, and wounds.

Mapping the Stakeholders in Post-Conflict Reconstruction of Future Wars as a Field of Lightbulbs

Working with Veteran Affairs, I have found myself looking into the eyes of the same soldiers who were in Afghanistan during my years there.  We were there for different reasons, to do different jobs, and possibly with different goals.  We also had different kinds of relationships with the local population and lived in entirely different ways.  We made and lost friends. We think about Afghanistan everyday, and also, think about it very differently, but it always in our minds. From this I know that the Afghanistan and Iraq wars will shape American politics for generations, just as the Vietnam war was discussed in every US political debate into the early 2000s.

To consider the demands of every node in the complex and time-warping web of global conflict is not feasible as a design approach. But consistent with the methods I have applied to other complex problems, to consider the thematic and territorial overlaps does prove effective. For example, in the domain of mental health, the impacts of war via PTSD are well documented within America and other NATO states. It is, however, less discussed among resident populations of war-torn regions and only marginally (if ever) discussed in reference to displaced populations.

The healing from trauma is complicated, and there are many who never fully recover or find effective remedies to move forward in their lives. Yet initiatives that have brought soldiers in contact with the places where they served, to build new memories and relationships with long harmful experiences, have been found to effective to some.  For others, there is a need to cut all ties, to relocate, and build new lives elsewhere. No matter how you approach it, healing becomes geographic as much as a psychological process.

To advance the state of postwar reconstruction, there is a necessity to go beyond security, architecture, and socio-economics. Like most design problems, there is an obvious need to factor such variables across time and space. But now I realize the necessity to reconsider our definitions of war in terms of how we conceive of stakeholders and stakeholder needs. This is not a static domain. New individuals and entities will emerge and disappear over time as will their contributions to the problems and the solutions. We cannot end war but we can formulate our present understanding of its ramifications so as to position a better tomorrow.