Enterprise Bitcoin and the Brain as a CryptoCurrency Network

If Dell, New Egg, and TigerDirect now accept Bitcoin, and Paypal's CEO contemplates the same, eBay and Amazon might also accept Bitcoin in the not too distant future, and this would start to really push cryptocurrency into the mainstream. Faster still if Google Wallet were to join. Bitcoin seems to be 'going enterprise' (= key step to mainstream) as fast as the Internet-of-things (Enterprise IOT: Microsoft, Ernst & Young, etc. offering connected POS (point of sale) networks and all 'devices' as an IOT service to businesses). However, even though Bitcoin in its entirety is a radically new concept, from a vendor standpoint, accepting Bitcoin is not a big deal - it is analogous to accepting any other kind of payment mechanism. Anyone (individual or enterprise) receiving, or wanting to pay out in Bitcoin can easily convert national currencies via Coinbase, bitpay, or other sites, or now the purported (as of July 2014) 33 worldwide Robocoin Bitcoin ATMs. Conceptually, Bitcoin is a payment mechanism for vendors, but for money businesses like banks, it is much more critical to develop explicit Bitcoin strategies and policies.

However, there is still much risk in Bitcoin and cryptocurrencies. Bitcoin as a currency is still new and volatile, and it is not clear if it is a faddish or persistent transformation, although the concept may have considerable resiliency even if specific cryptocurrencies do not (i.e.; Baconcoin). Also, there is only about $8 billion USD in Bitcoin now, and it would need to be on the order of $50-100 billion USD to receive more serious financial consideration. The currency does have a number of important features that could propel acceptance including architecture (psuedo-anonymous and trustless), openness, low-cost (eliminates currency exchange costs), and fungible worldwide availability. As Kevin Kelly points out, Bitcoin is not just a payment mechanism, it is a revolutionary way to enable collaboration at an unprecedented scale. Bitcoin is the reinvention of the institution of capital. Further, in the automation economy, Bitcoin is automated and open accounting; a transparent ledger. The concept of Bitcoin and its architecture and operation is a new model which is not unlike the brain, where (at minimum) many functions are handled automatically, and there is a certain modular aspect to function. Bitcoin might be a universal mathematical model of nature that human intelligence is just now discovering.

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.

Cognitive Enhancement Memory Management: Retrieval and Blocking

One familiar notion of cognitive enhancement is prescription drugs that boost focus and concentration: ADHD (attention-deficit hyperactivity disorder) medications like Modafinil, Ritalin, Concerta, Metadate, and Methylin [1], and amphetamines like Adderall, Dexedrine, Benzedrine, Methedrine, Preludin, and Dexamyl [1-3]. These drugs are controversial as while there is some documented benefit, there is also a recovery period (implying that sustained use is not possible), and they are often obtained illegally or for nonmedical use.

What is new in memory enhancement drug development is the possibility of targeting specific neural pathways, like long-term potentiation induction and late-phase memory consolidation [4]. A cholinesterase inhibitor, donepezil, which has shown modest benefits in cognition and behavior in the case of Alzheimer’s disease [5], was also seen to enhance the retention performance of healthy middle-aged pilots following training in a flight simulator [6]. Ampakines are benzamide compounds that augment alertness, sustain attention span, and assist in learning and memory (by depolarizing AMPA receptors to enhance rapid excitatory transmission) [7, 8]. The drug molecule MEM 1414 activates an increase in the production of CREB (the cAMP response element-binding protein) by inhibiting the PDE-4 enzyme, which typically breaks it down. Higher CREB production is good for neural enhancement because it generates other synapse-fortifying proteins [4, 9].

Memory management in cognitive enhancement could also include blocking or erasing unwanted memories such as traumatic memories brought on by PTSD (post-traumatic stress disorder). Since even well-established memories require reconsolidation following retrieval, the memory reconsolidation process could be targeted by pharmaceuticals to disrupt or even erase aberrant memories [10]. Critical to memory reconsolidation are the glutamate and b-adrenergic neurotransmitter receptors. These neurotransmitter receptors could be targeted by drug antagonists like scopolamine and propranolol, which bind with these receptors, to induce amnestic effects so that unwanted memories are destabilized on retrieval [11-14].

Summarized from: Boehm, F. Nanomedical Device and Systems Design: Challenges, Possibilities, Visions. CRC Press, 2013. Ch17.
Full article: Nanomedical Cognitive Enhancement  

References:
[1] Weyandt, L.L., Janusis, G., Wilson, K.G., Verdi, G., Paquin, G., Lopes, J., Varejao, M., and Dussault, C., Nonmedical prescription stimulant use among a sample of college students: Relationship with psychological variables. J. Atten. Disord. 13(3), 284–296, 2009.
[2] Varga, M.D., Adderall abuse on college campuses: A comprehensive literature review. J. Evid. Based Soc. Work 9(3), 293–313, 2012.
[3] Teter, C.J., McCabe, S.E., LaGrange, K., Cranford, J.A., and Boyd, C.J., Illicit use of specific prescription stimulants among college students: Prevalence, motives, and routes of administration. Pharmacotherapy 26(10), 1501–1510, 2006.
[4] Farah, M.J., Illes, J., Cook-Deegan, R., Gardner, H., Kandel, E., King, P., Parens, E., Sahakian, B., and Wolpe, P.R., Neurocognitive enhancement: What can we do and what should we do? Nat. Rev. Neurosci. 5(5), 421–425, 2004.
[5] Steele LS, Glazier RH (April 1999). "Is donepezil effective for treating Alzheimer's disease?". Can Fam Physician 45: 917–9. PMC 2328349. PMID 10216789.
[6] Yesavage, J.A., Mumenthaler, M.S., Taylor, J.L., Friedman, L., O’Hara, R., Sheikh, J., Tinklenberg, J., and Whitehouse, P.J., Donepezil and flight simulator performance: Effects on retention of complex skills. Neurology 59(1), 123–125, 2002.
[7] Chang, P.K., Verbich, D., and McKinney, R.A., AMPA receptors as drug targets in neurological disease—Advantages, caveats, and future outlook. Eur. J. Neurosci. 35(12), 1908–1916, 2012.
[8] Arai, A.C. and Kessler, M., Pharmacology of ampakine modulators: From AMPA receptors to synapses and behavior. Curr. Drug Targets 8(5), 583–602, 2007.
[9] Solomon, L.D., The Quest for Human Longevity: Science, Business, and Public Policy. Transaction Publishers, New Brunswick, NJ, 2006, 197pp.
[10] Milton, A.L. and Everitt, B.J., The psychological and neurochemical mechanisms of drug memory reconsolidation: Implications for the treatment of addiction. Eur. J. Neurosci. 31(12), 2308–2319, 2010.
[11] Debiec, J. and LeDoux, J.E., Disruption of reconsolidation but not consolidation of auditory fear conditioning by noradrenergic blockade in the amygdala. Neuroscience 129, 267–272, 2004.
[12] Lee, J.L.C., Milton, A.L., and Everitt, B.J., Reconsolidation and extinction of conditioned fear: Inhibition and potentiation. J. Neurosci. 26, 10051–10056, 2006.
[13] Ferry, B., Roozendaal, B., and McGaugh, J.L., Role of norepinephrine in mediating stress hormone regulation of long-term memory storage: A critical involvement of the amygdala. Biol. Psychiatry 46, 1140–1152, 1999.
[14] Sara, S.J., Roullet, P., and Przybyslawski, J., Consolidation of memory for odor-reward association: á-adrenergic receptor involvement in the late phase. Learn. Mem. 6, 88–96, 1999.

Google I/O: Seamless Integration: Watch, Tablet, PC, Glass, Smart Home, Smart Car

Google I/O, the company’s annual developer conference this week had many interesting announcements. The key point is the concept of the multi-device ecosystem, with the smart watch at the center for notifications, and seamless communication and content-sharing between all platforms: watch, PC, tablet, Glass, TV, smart home, and smart car (eCar).

The statistics are impressive, and have long surpassed Apple: Google Android has 1 billion active monthly users. One company initiative is Android One, a sub-$100 platform for roll-out to the world’s 5 billion currently without smartphones. The major new change with Android is the next version of the operating system, now having progressed up to the letter ‘L’ but whose candy-name like Kit-Kat for ‘K’ has not yet been announced (Lollipop? Licorice? Laffy-taffy?). L’s look and feel, and “material design” concept is different. It is much more like Windows with moving, self-resizing squares per priority and current activity, and 3D layers so some on-screen objects persist.

Some of the most innovative announcements pertained to Android Wear, wearable computing platforms like the smart watch and Glass. Android Wear feature notifications from the phone and tablet directly bridged to watch, and novel glanceable contextual apps developed specifically for wearables, for example being able to tap your phone to order a pizza or a Lyft ride. Android Auto is another expected announcement, with 40 partners in the Open Auto Alliance, and 5 car manufacturers planning to launch vehicles with Android Auto in 2015.

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"

Over 70 Google Glass Apps Available

As of June 2014, there were just over 70 Glass Apps available in a wide range of areas (Table 1). Some of the current applications for Glass include picture-taking, video, maps, directions, search, and hangouts; also points-of-interest ‘near me’ like parking, hotels, and restaurants, gestures, notifications, news, cooking (SousChef for Glass), and sports (scores and also augmented reality apps that overlay information to live events like baseball pitch speed and player statistics).

As one sign of the times, the first market ticker app for Glass is bitcoin quotes not stock market data. So far there are 13 gaming apps for Glass, including Ping (an analog to Pong, one of the first video games ever developed), MineSweeper, Space Invaders, Blackjack, Spelling, augmented reality gaming, and others.

Table 1. Google Glass App Categories (crosslisted) (Source). 

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.

EmergingTechs Nanotechnology, Synthetic Biology, and Geoengineering in the Governance Eye

The second annual Governance of Emerging Technologies conference held in Phoenix AZ May 27-29, 2014 discussed a variety of governance (regulation), legal, and ethical aspects of three areas of emerging technology: nanotechnology, synthetic biology, and geoengineering (climate management).

The prevailing attitude in nanotechnology is much like that in artificial intelligence, “no new news” and some degree of weariness after having experienced a few hype-bust cycles, coupled with the invisibility frontier. The invisibility frontier is when an exciting emerging technology becomes so pervasive and widely-deployed that it becomes invisible. There are numerous nanotechnology implementations in a range of fields including materials, computing, structures, nanoparticles, and new methods, similar to the way artificial intelligence deployments are also widely in use but ‘invisible’ in fraud detection, ATM machine operation, data management algorithms, and traffic coordination.

Perhaps the biggest moment of clarity was that different groups of people with different value systems, cultures, and ideals are coming together with more frequency than historically to solve problems. The locus of international interaction is no longer primarily geopolitics, but shifting to be much more one of collaboration between smaller groups in specific contexts who are inventing models for sharing knowledge that simultaneously reconfigure and extend it to different perspectives and value systems.

Futurist Ethics of Immanence

The ethics of the future could likely shift to one of immanence. In philosophy, immanence means situations where everything comes from within a system, world, or person, as opposed to transcendence, where there are externally-determined specifications. The traditional models of ethics have generally been transcendent in the sense that there are pre-specified ideals posed from some point outside of an individual’s own true sense of being. The best anyone can ever hope to achieve is regaining the baseline of the pre-specified ideal (Figure 1). Measuring whether someone has reached the ideal is also problematic tends to be imposed externally. (This is also an issue in artificial intelligence projects; judgments of intelligence are imposed externally).

 Figure 1: Rethinking Ethics from 1.0 Traditional to 2.0 Immanence.

There has been progression in ethics models, moving from act-based to agent-based to now situation-based. Act-based models are based on actions (the Kantian categorical imperative vs utilitarianism (the good of the many) or consequentialism (the end justifies the means). Agent-based models hold that the character of the agent should be predictive of behavior (dispositionist). Now social science experimentation has validated a situation-based model (the actor performs according to the situation (i.e., and could behave in different ways depending on the situation)). However all of these models are still transcendent; they are in the form of externally pre-specified ideals.

Moving to a true futurist ethics that supports freedom, empowerment, inspiration, and creative expression, it is necessary to espouse ethics models of immanence (Figure 1). In an ethics of immanence, the focus is the agent, where an important first step is tuning in to true desires (Deleuze) and one’s own sense of subjective experience (Bergson). Expanding the range of possible perceptions, interpretations, and courses of action is critical. This could be achieved by improved mechanisms for eliciting, optimizing, and managing values, desires, and biases.

As social models progress, a futurist ethics should move from what can be a limiting ethics 1.0 of judging behavior against pre-set principles to the ethics 2.0 of creating a life that is affirmatory and expansive.

Slideshare presentation: Machine Ethics: An Ethics of Perception in Nanocognition

Wearables-Mobile-IOT Tech creates Fourth Person Perspective

So far the individual has almost always existed in the context of a society of others. This could change in the farther future as individuals might be in the form of a variety of digital and physical copies in different stages of augmentation. It could become more difficult to find ‘like-others.’ My claim is that the function of alterity (an awareness of others that triggers subjectivation) would need to persist for individuals to fully become themselves, but it would not need to come from others that are like us.

All that is needed is some sort of external otherness that can show us ourselves in a new way to facilitate a moment of development. There is nothing in the function of alterity to suggest that it must be an ‘other’ that is like us. It is just that it has been this way historically, because other humans have been the ready form of ‘the other.’ It has been easiest and most noticeable when another human serves as a device like a mirror allowing us to see ourselves in a new way.

However, it is quite possible that the alterity function could be fulfilled in many other ways that do not involve a self-similar subject. One mechanism that is already allowing us to see ourselves in new ways is quantified self-tracking gadgetry. The ensemble of QS gadgets creates a fourth-person perspective, an objective means of seeing ourselves via exteriority and alterity that can trigger a moment of subjectivation. Now understanding the alterity function as such, there could be many alternative means of fulfilling it. 

Longer video on the topic: Posthuman Interpretation of Simondon's Individuation