Cognitive Easing: Human Identity Crisis in a World of Technology

Cognitive Easing is the aim of much of our endeavor, whether explicit or implicit. We have never wavered from trying to create a life of ease, enjoyment, and fulfillment. The definition of Cognitive Easing is spending less mental effort to achieve a result.

A contemporary problem seems to be technology’s controlling presence in the world. Jobs are disappearing due to technological unemployment. News is fed to us that does not correspond to reality. Mysterious big data algorithms direct from the background. We no longer seem able to think for ourselves with “the cloud” automatically piloting our lives. What happened to caprice and serendipity, to our very humanness?

However, I argue the opposite. It is not the infantilization of humans by technology that is happening, but rather the opportunity for cognitive easing. We are not always accustomed to using our brains in the most creative and productive ways. Therefore we feel dumbed-down by technology when cognitive easing is actually freeing us from mental drudgery. Consider the amount of effort spent on “last-mile cognition problems” such as planning and coordination. Instead, cognitive load could be increasingly outsourced to algorithms. This has been the promise of technology from the beginning, easier lives.

A pushback is that lower-level cognitive tasks might seem like part of the definition of what it is to be human. However, while we have had to occupy our time this way, it does not have to be who we are. We need to challenge the false and nostalgic notion of defining our humanness by the tasks we do, and this might not be easy. Even scarier than how we will spend our time after technological automation is the question of who we are – our very identity.

Technology is forcing us to question what it is to be human. We have defined ourselves by physical labor and lower-level mental tasks, and it is abrupt to have to change this, especially because we do not know who we are. Worse, there is a timing lag with technology replacing what we think our humanness consists of before we have had a chance to redefine what it could be. We feel out of step with technology, and that we are regressing instead of greatly progressing. We think paradoxically that technology robs us of our humanity when in fact it is doing what we wanted all along, providing physical and cognitive easing.

Technology, automation, and cognitive easing are requiring us to redefine what it is to be human based on the higher-level capacities we have. These higher-level faculties include creative problem solving, artistic expression, storytelling, and quirky ingenuity. Only humans have the ability to perceive the world and react with unique and inventive solutions. We can now contemplate a new class of problems that we did not have the luxury of addressing before, deploying our creative problem-solving capability to a greater extent. The vision for the future is engaging with more of our unique humanness, increasingly freed from both physical and mental drudgery, to be more of who we really are, creative, serendipitous, problem-solving beings exploring and enacting our world in new and ingenious ways.

Philosophy of Big Data

Big data is growing as an area of information technology, service, and science, and so too is the need for its intellectual understanding and interpretation from a theoretical, philosophical, and societal perspective.

The ways that we conceptualize and act in the world are shifting now due to increasingly integrated big data flows from the continuously-connected multi-device computing layer that is covering the world. This connected computing layer includes wearables, Internet-of-Things (IOT) sensors, smartphones, tablets, laptops, Quantified Self-Tracking devices like the Fitbit, connected car, smarthome, and smartcity.

Through the connected computing world, big data services are facilitating the development of more efficient organizing mechanisms for the conduct and coordination of our interaction with reality.

One effect is that our stance is moving from being constrained to reactive response to now being able to engage in much more predictive action-taking in many areas of activity.

Another effect is that a more efficient world is being created, automating not just mechanical tasks, but also cognitive tasks. This paper discusses how a philosophy of big data might help in conceiving, creating, and transitioning to data-rich futures.

 More Information: Presentation, Video, Paper

Dynamic Group Cognitive Coordination through Wearable Tech

A surprising ‘new functionality’ enabler of smartwatches and wearable tech is not just getting real-time alerts and notifications to a single user as the front-end of the seamless connected computing world, but group coordination. Real-time group coordination could foster a whole new class of wearable applications, for a wide range of ‘serious’ and ‘fun’ uses in both large and small groups.

35 teams presented at the Apple WatchKit Hackathon at Silicon Valley’s Hacker Dojo on Sunday November 23, 2014. Many interesting apps were shown, mostly for only a single smartwatch like MoodyBaby, NowCash, MedAlert, ItsRaining, LoveTap, and ScrollforSushi.

Best Tech was won by this author’s own team project for WatchSet: a multi-player social gaming app for smartwatch wearers in proximity to self-discover and play interactive games (Figure 1).

Figure 1: WatchSet Multi-player Social Gaming App for Smartwatch.

Large-scale Dynamic Cognitive Coordination
The level of where we are starting to operate now with technology is automating lower-level cognitive tasks. Linking any and all data streams on-demand in the connected computing world is allowing us to conceive of automation in new ways - as both mechanical task relief and cognitive processing offload. This suggests that we may be able to shift the whole way we interact with the world, and organize human activity in new and dynamically coordinated ways, that are potentially at a much larger scale than has been possible previously.

iSchools: Contemporary Information Technology Theory Studies

The perfect merger of academic rigor and contemporary thinking has come together in the concept of iSchools, which give practical consideration and interesting learning opportunities to the most relevant issue of our time: information. So far there are over 50 worldwide iSchools; a global pool, like bitcoin for academia. The March 2014 conference was held in Berlin and the March 2015 conference will be at UC Irvine. With higher education under reinvention pressure from all directions, the possibility of making institutional learning relevant again cannot be underscored enough.

iSchools are the perfect venue to take up not just the practical agenda within the information technology field but also the theoretical, philosophical, and societal dimensions of the impact of information technology. There have started to be some conferences regarding ‘big data theory’ (Theory of Big Data, University College London, Jan 2015), and a calling out of the need for ‘big data theory’ (Big Data Needs a Big Theory to Go with It, Scientific American, Rise of Big Data underscores need for theory, Science News). These efforts are good, but mostly concern having theory to explain the internal operations of the field, not its greater societal and philosophical effect. In addition to how ‘big data theory’ is currently being conceptualized, an explicit consideration of the general theoretical and social impact of information technology is needed. Floridi’s distinction re: philosophy of information is apt; the main focus is how the field changes society, not the internecine methods of the field.

Research Agenda:
Contemporary Information Technology Theory Studies 
Here is a thumbnail sketch of a research agenda for Contemporary Information Technology Theory Studies. Early examples of topics taken up at institutes and think tanks (like Data&Society) are a good start and should be expanded and included in the academic setting. A more appropriately robust agenda will consider the broad theoretical, social, and philosophical impact of the classes of information technology below that are dramatically reshaping the world, including specifically how our ideas of self and world, and future possibilities are changing.

• Big Data (big data, smart data, personal data, group data)  
• Blockchain Technology (Institute for Blockchain Studies) 
• The Brain as an Information Technology (computational neuroscience, connectome, in-silico simulation, cognitive enhancement) 
• Smartcity, Connected World, Self-driving Vehicles 

Prediction Markets Round-Up

Prediction Markets are a tool for collecting group opinion using market principles. The price is usually based on a conversion of an opinion of the percent likely an event is to happen (i.e., the probability), for example there is a 40% change that Candidate X will win the election. The premise is that there is a lot of hidden information that can be sharable but there are not mechanisms to share it because information-holders either cannot or do not wish to share it (for example that a current work team project may not finish on time). Some research has found that prediction markets may beat polls or experts in terms of forecast accuracy [1].

Figure 1. Prediction Market Example

To aggregate hidden organizational opinion and expertise, Prediction Markets are in use at 100-200 large US organizations as of June 2014: Paypal, HP, BestBuy, Electronic Arts, Boeing, Amazon, Harvard, GM, Hallmark, P&G, Ford, Microsoft, Chevron, Lockheed Martin, CNN, Adobe, American Express, and Bosch. There are several enterprise Prediction Market vendors for enterprise idea management: Consensus Point, Inkling, Spigit/Crowdcast, Bright Idea, and Qmarkets. The main applications of Enterprise Prediction Markets are revenue forecasting, demand planning, and capital budgeting; innovation life cycle management (rate, filter, and prioritize ideas), and project management and risk management.

There are Enterprise Prediction Markets and also Consumer Prediction Markets for event prediction such as politics: election results; economics: box office receipts, product sales; and health: pandemic prediction. Some of the leading markets are Iowa Electronic Markets (and Iowa Electronic Health Markets), the Hollywood Stock Exchange (film box office, TV shows, celebrities), simExchange (gaming: video game consoles, video game launches), CROWDPARK (general), and LongBets (futurist). A new market, SciCast, has recently launched for detailed science and technology predictions.

Markets are typically real-money, reputation-based, or anonymous. In the wake of Intrade’s regulation-forced closure, Bitcoin Prediction Markets are enjoying a surge of trading activity; markets like Predictious, Fairlay, and Bitcoin Bull Bear.

More Information: Prediction Markets @ Singularity University

[1] Trepte, K. et al. Forecasting consumer products using prediction markets. MIT. 2009.

Neural Data Privacy Rights

A worry that is not yet on the scientific or cultural agenda is neural data privacy rights. Not even biometric data privacy rights (beyond genomics) are in purview yet which is surprising given the personal data streams that are amassing from wearable computing, Internet-of-Things biosensors, and quantified self-tracking activities. Neural data privacy rights is the notion of considering the privacy and security issues regarding personalized data flows that arise from the brain.

There are several reasons why neural data privacy rights could become an important concern. First, personalized health data is already a contentious personal data issue, and anything regarding the mind, and mental performance and potential pathology has even more sensitivity and taboo attached to it.

Second, neural data privacy rights could be an issue because it is not difficult to measure some level of the electrical and other activity of the brain, and ever-ratcheting price-performance technology improvements could make it possible to capture and process the neural activity of vast numbers of people simultaneously in real-time. There are already many consumer-available devices that measure neural activity such as EEGs, PPGs, and tMS systems, augmented headsets like Google Glass, Oculus Rift, and foc.us, and other emotion and cognitive state analysis applications using eye-tracking, mental state identification, and affect analysis. 

Does Google Glass come with a Faraday cage?

Third, at some point, big data machine learning algorithms may be able to establish the validity and utility of neural data with correlation to a variety of human health and physical and mental performance states.

Fourth, despite the sensitivity of neural data streams, like any other form of personal data (where two data elements start to constitute an identification), privacy, security, and anonymity may be practically impossible. At worst, there could be malicious hacking, viruses, and spam targeting neural data streams.

Detailed Essay: "Neural Data Privacy Rights: An Invitation For Progress In The Guise Of An Approaching Worry"

What is Big Data and when will it be Smart Data?

Big data is cell phone users having an average of 100 interactions with their phone per day, all of which generate computerized records (100s of trillions of records). Big data is every financial market transaction, every passenger on every airplane flight, every shipped container, every transportation conveyance, every tweet, and every Internet post (all in the 100s of billions or trillions of records). Every transaction for all time.

One area of long-standing data interest is mortgage statistics since mis-estimating prepayments can cost investors billions of dollars. This raises the question of how prepayment risk is still being mis-estimated. Irrespective, mortgage data is one of the fastest growing kinds of data, both by row and column of tracked data, growing at more than 2x Moore’s law on a log chart (Moore’s law reflects the hardware on which the data is stored and manipulated (algorithms somewhat fill the gap)). This begs the question of smart data rather than big data.

There is much talk about all types of data growing (and data scientists being the biggest category of job growth), but the size of big data should surely be one of its most basic attributes. What is much more relevant is the value that big data provides through its use. For example, how has having more rows and columns in mortgage-tracking spreadsheets improved (if at all) prepayment prediction?

Like genomics, many big data problems are in the early stages of ‘the diffs,’ not knowing which part of the data is salient to keep out of the 99% that may be useless. ‘The diffs’ are the differences, the differences between a sample data set and the reference/normal data set that constitute salience and allow the rest of the data to be discarded.

Interactive Media Environment to Drive Next-Generation Collaboration

A key feature of the contemporary media environment is interactivity. From clicking big data into information visualizations to personal digital assistants to MMORPGs to crowd-produced digital art to on-demand video content integrated with real-time social networking, interactivity is the underlying expectation of any contemporary media experience.

So far, the interactive media environment and its deployments have been realized mostly in the areas of entertainment and information, and at the level of the individual. While it can be argued that ‘interactivity as a feature’ is an obvious progression in the evolution of technology, something much more profound is happening. At a higher level, the interactive media environment is facilitating the fuller development of the individual, and also of groups.

As Clay Shirky heralded, online interactions are progressing from social networking to content sharing to action-taking. The expectation of interactivity and sociality as a feature of web properties means that an interesting next level of human collaboration can be envisioned. Some of the examples of this include eLabor marketplaces, software communities like Wikipedia and Linux, and problem-solving groups like Foldit and EteRNA.

Big Data: Reconfiguring and Empowering the Human-Data Relation

A strong new presence in contemporary life is big data (the collection and use of personal data by large institutions). As individuals, we can feel powerless in our relation with data.

At present, the human-data relation is one of fear, distance, powerlessness, lack of recourse, and diminished agency. There is an asymmetry of touch in the human-data relation where data can see and touch us without our noticing or being able to touch back. What is missing from the human-data relation is the capacity for humans to touch data in a meaningful way. The asymmetry of touch leads to an incomplete subjectivation of both the human and the data: big data creates a false composite in trying to model and understand the whole individual from just a few electronically-traceable activities, while humans have almost no sight or conceptualization of the entity that is big data.

There are at least two ways to humanize and improve the human-data relation. One is reconceptualizing subjectivation and personal identity as a malleable and dynamic association of elements and capacities, and the other is reconfiguring the power relation between humans and data. To balance the power relation so that humans are more empowered, non-profit institutions, watchdog organizations, and community groups could be created for the defense of personal data, and privacy could be overhauled as a practical impossibility and recast into a system of access rights and responsibilities conferred upon the data.

Presentation: The Philosophy of Big Data
Video (in French): La reconfiguration de la relation humaine-données par le toucher

Big Data becomes Personal: Knowledge into Meaning

One of the most significant shifts in the contemporary world is the trend towards obtaining and analyzing ‘big data’ in nearly every venue of life.

However, one of the biggest outstanding challenges is turning these large volumes of impersonal quantitative data into qualitative information that can impact the quality of life of the individual in a multiplicity of areas such as happiness, well-being, goal achievement, stress reduction, and overall life satisfaction.

For this reason, I have helped to organize the AAAI Spring Symposium this week (Big data becomes personal: knowledge into meaning) at Stanford March 24-26 to explore exactly this question of turning personal data into meaning as related in Figure 1.

 

Figure 1: Turning big data into personal meaning.

The Post-Human Biocitizen

We find ourselves in a world with a frenetic pace of life sciences bio-innovation emanating from institutional science, startups, and community biolabs. New possibilities abound in a wide range of areas including  personal genomics, regenerative medicine, cellular therapies, anti-aging, microfluidic chips, quantified self tracking devices and apps, Google Glass, Google diabetes monitoring contacts, brain fitness training, wearable computing, IoT, and cognitive enhancement techniques.

At a higher level, two main themes emerging from this bio-innovation are:
1) what is happening with ourselves as human subjects
2) what is happening regarding data

The human subject is in the process of evolving into a biocitzen, being at the center of health optimization action-taking with a layer of quantified self-tracking gadgetry as a first line of defense, then a layer of preventive medicine health intermediaries (like genomic counselors) and peer collaborators in health social networks and community biolabs, and finally traditional public health services as final line of defense. 

Data's role is transforming even more quickly than the emerging biocitizen where the possibility of collecting, integrating, and sharing huge volumes of health data streams is now possible and required for the destigmatization of health issues and realization of preventive medicine. There are four main data streams to integrate: all of the omics (e.g.; genomics, metaboliomics, etc.), traditional health, quantified self-tracking gadgetry (wearables), and personal internet-of-things (e.g.; smart car, smart home). There is an important need to extend the concept of privacy and rethink the attendant rights and responsibilities of data regimes, quick likely with the advent of protective data intermediary services.

YouTube Video: The Post-Human Biocitizen

Presentation (en français): The Post-Human Biocitizen
Video (en français): Les personnes futures comme biocitoyens

Turning Big Data into Smart Data

A key contemporary trend is big data - the creation and manipulation of large complex data sets that must be stored and managed in the cloud as they are too unwieldy for local computers. Big data creation is currently on the order of zettabytes (10007 bytes) per year, in roughly equal amounts by four segments: individuals (photos, video), companies (transaction monitoring), governments (surveillance (e.g.; the new Utah Data Center)), and scientific research (astronomical observations).

Big data fanfare abounds, we continuously hear announcements like more data was created last year than in the entire history of humanity, and that data creation is on a two year-doubling cycle. Better cheap fast storage has been the historical answer to supporting the ever-growing capacity to generate data, however this is not necessarily the best solution. Already much collected data is thrown away (e.g.; CCTV footage, real-time surgery video, and genome sequencing data) without saving anything. Much of stored data remains unused, and not cleaned up into a form that is human-usable since this is costly and challenging (de-duplication a primary example).

Turning big data into smart data means moving away from data fundamentalism, the idea that data must be collected, and that data collection in itself is an ends rather than a means. Advancement comes from smart data, not more data; being able to cleanly extract and use salient aspects of data (e.g.; the ‘diffs,’ for example identifying relevant genomic polymorphisms from the whole genome sequence), not just generate and discard or mindlessly store.

2014 Top 10 Technology Trends

2014 promises to be another exciting year for technology development! Technology more than any other endeavor has the potential to most quickly improve people's lives.  

Some prominent multi-year trends currently in development include:

1. Worldwide Internet-connected (2 billion in 2013 growing to 5 billion in 2020) and growing Southern hemisphere megacities
2. Big data (doubling to 8 zettabytes 2013-2015)
3. Smartphone (>1 billion)
4. Smartwatch 
5. Wearables, Glass 
6. Quantified self-tracking (QS) gadgetry
7. Self-driving vehicles
8. eLearning and MOOCs 
9. 3D printing 
10. New economic models: bitcoin/cryptocurrencies, crowdfunding, crowdsourced labor marketplaces 

On the Horizon:

• Lab-produced synthetically-engineered food 
• Smart home 
• 3D bioprinting / DNA computing / synbio

Predictions for 2013, 2012, 2011, 2010, 2009 

Supercomputing Processing Speed Nearly Doubles in One Year

The Top500 November 2013 biannual list of the world’s fastest supercomputers shows China's Tianhe-2 still at nearly twice the capacity of the second fastest, with virtually no change since the machine vaulted onto the list in June 2013.

Tech Specs: Tianhe-2 (MilkyWay-2) - TH-IVB-FEP Cluster, Intel Xeon E5-2692 12C 2.200GHz, TH Express-2, Intel Xeon Phi 31S1P. The machine was constructed by the National University of Defense Technology (NUDT).

Tianhe-2's maximum processing power is 33.8 petaflops per second (and peak processing speed 54.9 petaflops per second). The nearest competitor is the US DOE's Titan, a Cray XK7 Opteron 6274 16C 2.200GHz with a maximum processing power of 17.6 petaflops per second. Processing power is firmly on a steep growth curve, accelerating since the 5 petaflops per second mark was surpassed in June 2011 by RIKEN Japan (Figure 1).

Figure 1: Supercomputing Processing Capacity (Source: Top500)

Supercomputers, although processing faster than the human brain for some time now, process in a massively linear parallel manner which is not at all how the brain functions. However, while not mimicking the human brain, trying to understand it is a key use of supercomputers, along with other traditional prediction problems like weather forecasting, and physics phenomena and energy modeling. Now firmly in the big data era, processing astronomical data too is a key use for supercomputing. Maybe Tianhe-2 will compute findings from lunar data processed through China's recently landed Jade Rabbit. In other astronomical applications, astronomers expect to be processing 10 petabytes of data every hour from the Square Kilometer Array telescope under development in Australia and South Africa with a total collecting area of one square kilometer.

State of Tech: Image Corpus Corralling and API Integration

Two of the biggest current tech trends are image corpus ‘corralling’ and API integration. 1 billion images are taken per day and 6 billion photos are shared monthly.

Uploaded photo databases are the new 'data' corpora.

The first moment this shift became clear in Google’s announcement in June 2012 of the ability to recognize images of cats (the most frequently appearing entity in YouTube videos), and in the big data industry’s continual innovation to manage unstructured data like photos. Now, the sheer volume of image-related activity and opportunity for different consumer and commercial applications is making image classification a focal area for the tech industry.

Functionality is being developed for classifying and accessing content – both images and all web content - with tools such as Imagga as a cloud-based image classification program, and OpenCalais, a standard for text-based semantic content analysis and organization. What we might now start to call the corpus characterization ecosystem is expanding into related tools like DocumentCloud that runs uploaded documents through OpenCalais as a service to extract information about the nouns (e.g.; people, places, and organizations) mentioned.

API integration remains an ongoing trend of the year, with integrated API and developer management platforms like Mashery continuing to grow. API integration platforms give companies a means of facilitating and encouraging external developers to access their content to make apps, and give developers a standardized means of accessing large varieties of data content from different sources to integrate in creating a new generation of sophisticated apps and web services. 

Big Data and the Quantified Self

Big data is in a moment that is pre-Coprenican, (original) world-is-flat, and ‘sail west from Europe for the Orient.’ That is to say that big data is in need of not just descriptive tools, but maps, cartographic representations that help to define, concretize, and conceptualize what exactly big data is and how to think about it.

A conceptual mapping of big data is necessary which would most obviously draw upon basic mapping principles (metaphorically and literally) and epistemological models. For example, one dimension of a big data map is numeracy. In the conceptualization of big data, so far the context has been individuated information, information about individual units, like people (e.g.; personal data), and the tensions between the subject of the data and the user of the data, namely institutions. There is now an emergent category (e.g.; group data), the sense of data arising from and belonging to a group.

The quantified self is a vanguard paradigm for understanding personal data and urban data a similar vanguard paradigm for understanding group data. The quantified self is inherently a big data problem, as manually-tracked ‘small data’ is now being replaced by automatically-collected ‘big data,’ and cloud-based methods are required for data processing, analysis, and storage.

More information: Slides, Blog coverage, Paper

Extreme Data shapes Future Cities

With over 50% of the planet living in cities as of 2008 growing to an expected 75% by 2050 (when the population is estimated to be 9 billion), seamlessly transitioning to cities-of-the-future should be a key planning goal for every urban area. In some countries like the UK, there are strategic initiatives underway to create Future Cities and Smart Cities that include sponsoring hackathons for citizens to work with open urban data, and in other cases research centers are leading efforts such as the MIT Senseable City Lab using the wireless Internet-of-Things (IOT) to sense the real-time city.

Some of the more familiar recent innovations that are starting to pop-up include smart electricity meters, electric car charging stations, on-demand bicycle transport depots, aspirations for vertical farms, and in public transportation: mobile apps with on-demand schedules, journey-planning, and real-time transport information. As another sign of the times, the Oxford English dictionary added the term Internet-of-things in August 2013.

Extreme Urban Data 
The biggest trend reshaping all aspects of our lives, the Big Data Era, is driving a whole new tier of Future Cities and Smart Cities apps connecting big data, open data, statistical processing, and machine learning to user-friendly apps, web services, and other consumable front-ends. Killer Apps could focus on practical improvements to daily life and resource-use: adaptive lighting, smart waste, pest control, hygiene management, eTolls, transport and traffic management, smart grid, asset tracking, and parking. Killer Apps can also be political – using crowdsourced data and social media scrapings to create tools that are the bottom-up sousveillance antidote to top-down surveillance as envisioned in David Brin’s Transparent Society, for example, companies using social media-sourced data to predict country instability in real-time like Cytora.

Digital Literacy: Learning Newtech for its Own Sake

Digital literacy is a new capability and feature of our modern world, where consciously or unconsciously, there is a category in our lives called ‘learning newtech.’

There are two levels: first the basic skill acquisition and conceptual understanding required to learn a newtech, and second, the psychology of the digital learning curve which includes evaluating and justifying the time investment and utility of learning au courrant digital literacy tools with the appreciation that they will be almost immediately obsolescent.

We might complain about the effort required to master contemporary areas of digital literacy like learning mobile app development, the big data statistical manipulation language R, and scripting frameworks like node.js and jQuery. At the same time as we forget our many digital proficiencies, and the time invested to acquire them; previous generations of digital tools like file sharing, photo-uploading, Excel macros, Microsoft Word, PREZI presentations, file archival, and system restoration.

It is arguable that we should devote explicit effort to digital literacy, and further that digital literacy for its own sake could also be an objective. Taking Stanford University as an example, all incoming students must take a software programming class; pedagogically the language requirement is still in place, but it has shifted from French, Spanish, or German to C++, Java, or Python.

Artworld's Reaction to Citizen Art: not like Science and DIYscience

Considering the tradition of the highly-regarded place of science in society and the venerated scientific method, it is surprising that the ScienceWorld has deigned to notice Citizen Science and DIYscience efforts. Initially, the reaction of science may have generally been to dismiss citizen science, however, in many cases, perspectives shifted to wondering how to collaborate with citizen science efforts and how low-cost world-wide accessible Internet models could help to crowdsource study participants, data analysis, and other aspects of studies. The ecosystem became a continuum ranging from institutional science ('high science') to the individual n=1 quantified self experimenter ('citizen science as the venture capital arm (e.g.; starter, feeder, interest-surfacer) of science').(More)

Now the advent of new media has democratized the tools for art production. It is much easier for individuals to express themselves creatively in many different digital art venues and genres. Some of the tools that facilitate individual and collaborative creativity include Garage Band, SoundCloud, Pinterest, the Spore creature creator, blogs, Twitter, and virtual worlds. It is therefore timely to ask about the ArtWorld's (e.g.; insiders: artists, museums, critics) potential reaction to Citizen Art. As opposed the ScienceWorld's reaction to Citizen Science, the ArtWorld's reaction to Citizen Art could be much more complex. This is because art has been, and may always be something contentious. Key questions remain unsettled and even more pronounced with new media and digital art:

• what is art? 
• who can do art? 
• who can determine what is art? 
• what is the consecration process for art? 
• what is the societal and political role of art? what is the role of art as critique (of art, society, politics, etc.)? 
• is art autonomous from society? 
• what does the commercialization of art mean? 

Precisely because it is art, and not science, the acceptance of Citizen Art by the ArtWorld is much more nuanced than the tangible and quantitative nature of science, including Citizen Science, that makes results demonstrable. What is at stake is also more nebulous, although some new genres of digital art like SciArt, itself a mix of science and art, may be earlier to be acknowledged by the ArtWorld. (More)

Web Analytics: a Precursor to Cognitive Enhancement

Any sufficiently complex, rapidly-responding, and self-adjusting computer system is sometimes heralded as the place that “AI could wake up,” but could be seen more broadly as the venue of algorithm development that might have extensive applicability, including for cognitive enhancement.

Some exemplar complex modern computing systems include big data analytics, high-frequency trading operations, multiplayer video game networks, weather-modeling systems, and now…mobile and web analytics. These latter platforms are becoming increasingly sophisticated, continually updating actual and predicted user behavior data and delivering this information in real-time. Web property owners can watch live engagement with their websites in a heads-up display (HUD) overlay on web pages, as Chartbeat demonstrated at an API-related mini-hackathon at their NYC offices on June 29, 2013.

Some of the most prominent mobile and web analytics companies currently include Google Analytics, Chartbeat, MixPanel, KISSmetrics, Omniture, and Visual Revenue.

The 800 pound gorilla of online metrics is the eCommerce use case, using metrics in endless iterations of A/B testing (e.g.; does version A or B of the website produce more click-throughs and product purchases?) However, it is now increasingly important to measure other kinds of web experiences, for example news consumption where the key metric is engagement time as opposed to a simple link click-through. The type of interaction is also important, where the user spends time on the page, and the type of engagement activity such as reading, commenting, and sharing/referring.