Worldwide Genetic Databases and Agent Based Modelling

The price of sequencing a complete human genome is now about $4-5,000 (with the price point on a steep downward trajectory expected to hit $1,000 and then $100).  $1 and cheaper DNA sequences are expected within the next ten years.  Humans shed thousands of skin cells every minute.  We are DNA transmitters.  So there can be no presumption of DNA privacy.   There is a synthetic biology revolution 30 years behind the computer revolution.  DNA can be printed like computer code.CDC researcher

Here is a random walk through the possibility space we live in.

The National Health Service in the UK is sequencing 100,000 citizens and wants to sequence the entire UK population. The NIH is doing a broad metadata study on DNA Environmental correlations on phenotype.   There seems to be a compelling case for the WHO to fund a global genetic database.  A complete genomic and associated health record of the human population, gives drug companies the ability to calculate the market value of treating each and every disease.  Some orphan diseases may cross the threshold of profitability.  New associations between genetic markers and health outcomes will be open to analysis.

With networked data on ancestry, a higher level study of phenotype and environment could identify similar correlations.  Large surveys of ancestry networks and  environmental impacts suggest a future where every grave in the world is mined for DNA data.  With a complete genomic record of ancestry, humans will be able to instantly know the family relationship between themselves and every other human.  The narcissism of small differences may lose value if people were able to see people from a neighboring tribe as being intimately related to themselves.  People are more likely to trust relatives and trust allows for greater economic fluency.  This is a compelling case for international organizations like the World Bank or United Nations to fund a global ancestry genomic database.

One reason that people are resistant to global genetic databases is the overuse and misuse of this data by security professionals.   Law enforcement officials In the southern United States keep hundreds of thousands of DNA samples from convicted criminals.  Crime labs in the USA spend billions on sequencing.  They even collect of DNA from victims.  Analytics driven smart policing using DNA augmented agent based models could offer a more scientific approach to the preemptive containment of criminals.  (The USA already incarcerates a large part of its population based on trivial victimless crimes.  One way of interpreting this policy is an effort to preemptively contain a high risk population based on a trivial but significant association.)  Modern law enforcement operates under a social contract where is promises to only attack those guilty of specific crimes.  If this contract is thrown away and replaced with a broader use of coercion it is a dramatic rebalance of power which has wide implications about political force, and legitimacy.

Your DNA is now part of your data exhaust (along with all the Google searches you ever made, your GPS record, every email you ever sent and etc).  It will be sucked up by marketing firms, insurance companies, banks and security firms.  The data will be used to make statistical predictions about your behavior and sold as widely as possible.  Future employers, landlords, and loan officers will likely bypass privacy protection laws by requiring your permission to use this data as part of use conditions.

With network analysis, social footprints are becoming an important part of analytics.  Your friends DNA and data exhaust will have an important impact on your possibility space in society.  This multi node data exhaust model is now set in motions –  the step beyond predictive analytics is agent based models.  This technology uses your data exhaust to make an agent (polymorph, ghost, replicant) that represents your expected behavior pattern.  Your agent is then run through a simulation millions of times to get a more accurate probability map of your decision space.


How can the cost benefit be balanced?  When analytics removes uncertainty it frees up resources.  How are these resources distributed?  Are there other ways to offset risk besides the over use of force and incarceration?  It would be extremely unfortunate to let negative externalities shape the debate when the up side is significant.

P.S.  A quick look at physical cloning.

Project Einstein is sequencing 400 math geniuses.  China is sequencing 1600 gifted children.  This trend to capture and compare the genotypes of great minds is compelling.   People buying eggs for invitro may want to increase the chances of having an above average baby.  Taking this idea further the name “Project EinStein” suggests the quest to sequence all the great minds of history.  Why wouldn’t every state power or infertile couple want to kickstart their own Einstein or Napoleon?   Or a cocktail containing significant qualities of both?   An attack on the ability to patent human DNA may impact the commercial yields of this area as study,  but a DIY peer-to-peer effort in this area could be compelling.  Some are already concerned about state efforts to engineer its populations genetics.

Google Glass as 3D scanner

Jailbreaking classic sculptures from museums using 3D scanning and 3D printing is a new trend in the art world, and Google Glass is the latest tech in the game.  Most people are not aware that 3D scanning has become cheap and easy, and can even be stitched together from photographs.

As this trend continues, designers can/weill save time by capturing and manipulating real  places and people in their work.  Are you ready to be scanned into a videogame?  Are you ready for your online presence to be aggregated into a data mashup?  When will we hear about a stalker scanning someone into lifelike doll?

What will home decoration look like when the ornamentation of great cathedrals and subatomic structures can become easily generated and incorporated into domestic space?

doll face

The Future of Premium Content and IP Monetization: Film as gamification storification maker toolkit

filmpres_5-thI just read an interview with 1970’s film gurus George Lucas and Steven Spielberg predict that VOD is the future.  This is something that they could have safely predicted in the 1970s.  So I thought I would add a few more obvious observations on tech trends for the film industry.  Where to begin?  I think I will start with games before moving to piracy and maker culture.

Games:  The above interview includes an old school chauvinist film bias that video games cannot provide an emotional experience.  Some might flip that around and say that film action can be less engaging than game action and that Hollywood should retrofit every classic movie with game play levels.  Alternately, there are a wide range of ways to increase emotional engagement in games.  One way to bring life to video games is to hire live actors from around the world to perform within video game worlds.  Live haunted houses make use of live actors and actresses, and are forced to pay them first world prices.  There are obvious wage asymmetries around the world that can bring a live cast into a video game world for pennies on the dollar.   Another obvious way to bring human emotion into a video game world is to populate games with friends and families.  Warcraft Guilds and Modern Warfare networked game play already demonstrates the emotional power of this type of engagement.  With dramatic game design that allows for multiple overlapping objectives this type of play can be broadened into a wider emotional range.  If you put this style of game onto an AR LARP platform that includes fantastical graphical layers on video chat feeds or real world environments we could have types of cinematic costume parties with stylized emotional games.  If you put this type of game play onto a social graph and interest graph you could drive social collision in fantastical dramatic emotional cinematic ways.  Film directors and dramatic improv directors have an advantage for this type of game design.  They are expert at motivation and think in terms of drama.  Film industry refugees should keep this mind.

Piracy:  There are no closed systems in nature.  Data attack outpaces data defense.  As the information age progresses the free flow of data becomes cheaper than restricting the flow of data.

If you went to the “What’s Next?” event at the Academy of Motion Picture Arts and Sciences a few years ago, you would have heard the former head of technology for the NSA warn the industry.  20th century premium content IP monetization based on creating a huge demand (marketing) and limited supply (limited release windows) is obsolete.  Using coercion to defend an obsolete IP monetization model is expensive and has limited range.  Attacking attackers (even false targets like distribution rival Megaupload) has limited range.  Premium display like 3D IMAX and dome projection cannot compete with emerging domestic consumer display.  A head mounted display like Vizux goggles can emulate a 1000 foot tall movie screen.   Movie studios are stultified by the inertia of tradition and guild contracts.  Meanwhile super agile mafia capitalists and bored hackers can and will hijack any networked IP and will iterate new ways of slicing dicing and mashing up the data into get rich quick schemes.  Tom Cruise doesn’t want his likeness in herpes medication or machine gun ads?  Guess what?  The Russian mafia does not care, Nigerian teenagers with advanced coding skills don’t care.   Contracts prevent movie studios from chopping their film library into 1 million animated .Gifs with embedded bootleg viagra commercials?  Some one else will get that money.

VITATNMaker culture:  The future is a blend of 1 part premium content and 1 billion parts maker culture.

If the film industry wants to stay in the game they need to provide the best possible platform for their product.  They need to aggressively compete within the possibility space on convenience, price, and utility.  Anything less is death by a thousand cuts.  Price and convenience for pirated content is free and instant.  So that leaves utility.

Here is the big paradigm shift.  Everyone in the modern world has a networked video camera in the pocket.  Right now everyone wants to be able to create premium content with their camera.  Everyone wants to be a movie star.  Right now the toolset that bridges the gap between Youtube garbage and slick TV and Cinematic productions does not exist.  However it will exist.  Templates and software to bump up the quality of smart phone videos are an emerging trend.  One way for premium content creators to increase the footprint of their productions is to feed maker culture.  Use their premium product as a magnet for makers.  Fuel their fan fiction.  Give them templates.  Host their home made videos.  Use premium product as a social collision machine to inspire more makers.  Offer 3d files for all the films elements online.  Offer chatbots of all the main characters.  There are a wide range of apps that can bridge the gap between amatuer maker productions and pro content.  Trends across all digital media are directed toward ease of use and universality.  A feature film or reality TV program can inspire thousands and thousands of hours of free content and user engagement.   See the above examples of dramatic game play to increase the engagement and flow of maker drama.

So far these ad hoc self assembling maker-culture initiatives have been chaotically flowing around premium content.  Studios have been forced to ignore them.  Perhaps they will be ignored to the end.  Or perhaps they will become a more active part of the monetization.

If the collapse of the music industry and the print industry is any indication of the future….

The real path forward is  — premium Film and TV content as gamification storification maker toolkit.

Face recognition – What is it good for?

Google has announced a ban on face recognition apps for it’s Glass product.  This is a dramatic step because many people have been expecting face recognition to be one of the most important features of Glass.

What good is face recognition?

  • Face recognition along with other biometric information can be used for authentication.  Passwords suck.  They are  a bad user experience for many reasons: they choke fluidity, they are by definition hard to remember and are thus written down, they are ugly, they are alienating.  Face recognition  is a pathway around passwords.
  • Face recognition allows for customization.  If a machine recognizes you and knows who you are, they can balance resources in ways that are more useful and pleasing to you.  This can ease your cognitive load (gibberish repetitive tasks stumbling around a one size fits all pre-rendered world) and it can increase your productivity.
  • Face recognition allows for pervasive abilities.  Do you want to have to go through a multiple-stage, deliberate look-up for people you meet, or can you get a more relaxed flow if some important information is cued up?  Social information is extremely important for humans.
  • Face recognition can function as medical augmentation for people who have alzheimer’s disease, face blindness, or are just bad with names.
  • Face recognition is enterprise software for sales people.  Sure, it’s creepy if Wal Mart greeters start talking to you by name, but if they offer you personalized discounts or can cue up half your shopping basket based on your buying patterns, they can save you time and money.  High end sales people at trade shows selling multi million dollar products may have a strong need for identifying buyers they have not met yet.
  • Face recognition is a social rocket booster.  It opens up the usefulness of on-line social graphs like Facebook, Linkedin, Ancestry, Classmates, and dating websites.  If you meet a stranger at a party you would know what interests you have in common, what people you know in common, and what fun or value you can offer each other.  The easy flow of this information makes in person social collision more fun and rewarding.

Face recognition does have some grey area uses.  As a security device it could warn you of people on sex offender registries, or people who own guns, or have criminal records.  People can use face recognition to avoid each other based on arbitrary categories or even target people for bullying.   Misguided advertisers can use face recognition to target and (if the targeting algorithm or data tech is wrong) harass and annoy customers.  I would also expect 1001 new misuses to be discovered once face recognition is ubiquitous.  However, these problems will need to be addresses as needed in the same way any new tech has potential pitfalls.  Traffic lights didn’t invent themselves.

Why do I expect this tech to be common, regardless of Google’s immediate decision?  Google and all major software companies are already using this tech.  Google announced a major machine learning algorithm to recognize cat faces.  It’s easy to imagine how this work can be applied to less fuzzy faces.  It seems that Google is merely preventing immediate face recognition.  Perhaps they are trying to sabotage competing social media companies.  Perhaps they are defending against an aggressive smear campaign against them.  Regardless, face recognition is a major tech and can and will be applied to every smart phone and webcam.


In Praise of Survaillence

Anticipatory computing is a type of user experience design where the machine knows what you are doing and is actively anticipating what you may do next in order to cue up abilities and allow for more fluidity in your user interaction.  This type of design will become more and more important as the internet of things begins to materialize.

The ideal user experience of anticipatory design is something like interacting with your best friend or spouse that can almost complete your sentences, or in a professional situation working with a highly trained assistant (valet or caddy) that knows you very well.

This is obviously a VERY hard problem.  Human human interaction is hard.  Humans confound each other all the time.  If a human watches another human working on an unknown task it can be extremely difficult to guess what they are doing or what goals they have without asking them.

It’s also an very IMPORTANT problem to solve because this is a pathway to enormous economic growth (risk reduction, resource balancing, domestic and professional automation) and mental well being.  Off loading boring repetitious complicated tasks can free up your mind, lowering your cognitive load and your stress load.


Anticipatory computing needs as much data about you as possible: dirty data, weak data, social data, sensor data, infinite data, open data.  Again, humans have trouble anticipating other humans with multiple senses, a long memory, a deep background of heuristics, and powerful biological social cognition.

Bad automation and bad anticipation is a negative value – it will wrestle with you and fight you – it’s the worst.  So expect your machines and apps (both individually and as part of an ambient intelligence network) to listen to you, to stare at you, and think about you constantly, and ask you a lot of questions.  What are you doing?  What do you want to do later?  Do you want to do this?  What about that?  It’s learning your patterns, how to cluster your activities, goals, contingencies, insertions, priorities, task selection hierarchies, the secret nuances of what you do.  Sure it sounds a bit creepy at first, but have you ever wished that you had eight arms and two brains?  What good are eight arms and two brains if they don’t know what you are doing or what you are trying to do?  You don’t want your machines and apps getting in your way.

Sensor data and anticipatory computing is also useful for personal health.  Presymptomatic warnings about disease or environmental stressors can save lives.

Concerns about hostile 3rd parties abusing anticipatory systems are worth consideration. Data attack outpaces data defense, and there are no closed systems in nature.  However, there are immune systems in nature.  Dynamic evolving barriers to environmental threats are an important part of the technology tool kit.

Data asymmetry is a dead end.  App companies, communications companies or other strategies based on collecting your data and hiding it behind walls are wasting time on incomplete inferior data sets.   The value of open data utility will find paths around them.  A better strategy is to open the data as a valuable infrastructure and concentrate resources on developing valuable competitive abilities.


Software Robotics and Enterprise App Opportunities

Check this ComputerWorld article “Immigration reform may spur software robotics.”

There are several software based paradigm shifts happening at the same time.

  • Most of the business world has yet to transition to social and mobile media.  Some businesses have yet to completely transition their paper records to electronic records and utilize their aggregate value (dirty data, weak data, data mashups, data visualization tools for utility/value discovery, task automation).
  • Business desktop tools are not anticipatory.  Automation tools for routine bureaucratic business tasks aren’t here yet. Use of pervasive desktop aggregation and big data (dirty data and weak data) has yet to come online.  Automated multi-task scheduling capabilities (such as the Gantt charts used by project managers) have yet to be adapted for personal use and diffused to the greater working population.  Aggregated bottom up multi-tasking traffic optimisation across social networks has yet to be developed.
  • Pervasive and ubiquitous anticipatory AR OS systems are an opportunity ready to be exploited.  Most cameras are dumb right now, and there is low fluidity in video network capability and data aggregation.    RFID and the internet of things is just sitting in a box waiting to be unpacked.  Every commercial vehicle can be automated from trucking to mining equipment,  construction with quadcopter swarms to warehouse drones.

Most tech giants are aware that there just aren’t enough software engineers worldwide, or in the pipeline.  Even with Viet Nam including a sophisticated software engineering curriculum in their secondary school education.

So what are the big tech giants doing about it?  What should they be doing about it?  The most immediate thing they are doing is outsourcing to the existing worldwide workforce.  This is a bit turbulent in a global recession, and brings us back to the ComputerWorld story above.  Software Robotics

The most obvious solution to the software engineer shortage is to develop a new software engineering tool set.  IMO this is the biggest challenge/opportunity in software engineering right now.  Software development needs to become easy enough that it can be done by semi-skilled labor.

Unless software is written in machine language, code is meant to be read by other humans.  Many of the new scripting languages brag about “semantic sugar.”  It’s the idea is that when a programming language is closer to human spoken language, there is increased fluency and increased productivity.  These upper level scripting languages are a move in the right direction, but instead of semantic sugar there needs to be a semantic happy meal.  Think of the command line OS tools from the 80s (like DOS) compared with SIRI and GUI touch screen that can be used by toddlers or the illiterate.

Intuitive human friendly tools for software engineering will increase not only the worker base  it will increase the entire user base.  Putting DRY (Do-not-Repeat-Yourself) automation tools in the hands of individual workers will explode productivity and grow the economy in ways that cannot yet be foreseen.  It will be a paradigm shift as powerful as popular literacy and numeracy.

As an example, here is an MIT coding toolset for kids.  What is the adult version for the workplace?


The New Search Engine: Pervasive Socially-Mediated Search Feed Mash-ups

Command line search is being replaced with NLU (Natural Language Understanding).  The static search page is being replaced with a search feed.

The Samsung Intel backed app called MindMeld can listen to a conversation of up to eight people at once and creates a dynamic search engine results feed.

Mindmeld is being targeted at networked chat.  However the same search feed technology is well suited to a pervasive ubiquitous paradigm, as user experience flows between glass, phone, tablet, laptop, browser, and etc.    A pervasive record of device use (desktop, browser, phone, text, geo) over hours, days and years allows search engines to find and use hyper personalized (and valuable) preference patterns in deep time.  Again, it’s worth emphasizing that the search results would be a dynamic feed rather than a static results page.  So the context of the users situation and (forward/backward) patterns in time would constantly be aggregated into the feed.  What is the user browsing now?  What email conversations are they having?  Where are they?

Socially mediated search – social dynamics and information patterns from parallel (pervasive) records are also important.  One major paradigm change in networked data is the mash-up.  When feeds collide amazing things happen.  Humans are social animals.  When rich social pathways are used to direct data collision, value is created.  Exposing parallel search feeds across the network (and exposing contexts in search feed information pathways) allows for social search grooming and re-versioning.

Data visualization tools are a good place to look for search feed mash-up gamification possibilities and using search for social collision.   

This  presentation on MindMeld technology and mapping techniques can be applied to visualization tools and dynamic data.


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