Monday, August 18, 2014

Intracortical Recording Devices

A key future use of neural electrode technology envisioned for nanomedicine and cognitive enhancement is intracortical recording devices that would capture the output signals of multiple neurons that are related to a given activity, for example signals associated with movement, or the intent of movement. Intracortical recording devices will require the next-generation of more robust and sophisticated neural interfaces combined with advanced signal processing, and algorithms to properly translate spontaneous neural action potentials into command signals [1]. Capturing, recording, and outputting neural signals would be a precursor to intervention and augmentation.

Toward the next-generation functionality necessary for intracortical recording devices, using organic rather than inorganic transistors, Bink et al. demonstrated flexible organic thin film transistors with sufficient performance for neural signal recording that can be directly interfaced with neural electrode arrays [2].

Since important brain network activity exists at temporal and spatial scales beyond the resolution of existing implantable devices, high-density active electrode arrays may be one way to provide a higher-resolution interface with the brain to access and influence this network activity. Integrating flexible electronic devices directly at the neural interface might possibly enable thousands of multiplexed electrodes to be connected with far fewer wires. Active electrode arrays have been demonstrated using traditional inorganic silicon transistors, but may not be cost-effective for scaling to large array sizes (8 × 8 cm).

Also, toward neural signal recording, Keefer et al. developed carbon nanotube coated electrodes, which increased the functional resolution, and thus the localized selectivity and potential influence of implanted neural electrodes. The team electrochemically populated conventional stainless steel and tungsten electrodes with carbon nanotubes which amplified both the recording of neural signals and the electronic stimulation of neurons (in vitro, and in rat and monkey models). The clinical electrical excitation of neuronal circuitry could be of significant benefit for epilepsy, Parkinson’s disease, persistent pain, hearing deficits, and depression. The team thus demonstrated an important advance for brain-machine communication: increasing the quality of electrode-neuronal interfaces by lowering the impedance and elevating the charge transfer of electrodes [3].

Full Article: Nanomedical Cognitive Enhancement

[1] Donoghue, J.P., Connecting cortex to machines: Recent advances in brain interfaces. Nat. Neurosci. 5 (Suppl), 1085–1088, 2002.
[2] Bink, H., Lai, Y., Saudari, S.R., Helfer, B., Viventi, J., Van der Spiegel, J., Litt, B., and Kagan, C., Flexible organic electronics for use in neural sensing. Conf. Proc. IEEE Eng. Med. Biol. Soc. 2011, 5400–5403, 2011.
[3] Keefer, E.W., Botterman, B.R., Romero, M.I., Rossi, A.F., and Gross, G.W., Carbon nanotube coating improves neuronal recordings. Nat. Nanotechnol. 3(7), 434–439, 2008.

Sunday, August 10, 2014

Escaping the Totalization of my own Thinking

One of the highest-order things that we can do for ourselves and others is try to escape our own thinking style. Each of us has a way of thinking, a default of which we may not even be aware. Even if we are aware that we each have a personal thinking style, we may not think to identify it and contrast it with other thinking styles, consider changing our own style, and even what it might mean to be portable between thinking styles.

This is a form of the totalization problem, that being completely within something, it is hard to see outside of the totality of that thing. If we are thinking through our own mind, how can we possibly think or see anything that is not within this realm? By definition, this seems an impossible conundrum; how are we to see what is beyond what we can see? How can we become aware of what we are not aware?

The totalization problem has been an area of considerable philosophical focus, whether there is an exteriority (an outside) to concepts like world and reality, and if so, whether it is reachable. Philosophers like Jacques Derrida thought that yes, escaping totalization (any system that totalizes) would indeed be possible. One way is though literature, which offers its own universe (totalization) but also inevitably a hook to the outside (our world). Another way is through the concept of yes, assent, which has a hearing-party affirming and a talking-party asserting in a dynamic process that cannot be totalized.

In a less complicated way for our own lives, there can be other ways of escaping from the totalization of our thought into an exteriority, an outside where we can see things differently. Explicitly, we can try different ways of experiencing the world by learning other of how people apprehend reality, and noticing that more joy may come from experiencing the journey rather than attaining any endpoint. Perhaps most important is being attuned to new ideas and new ways of thinking and being, especially those that don’t automatically make sense.

Sunday, August 03, 2014

Machine Ethics Interfaces

Machine ethics is a term used in different ways. The basic use is in the sense of people attempting to instill some sort of human-centric ethics or morality in the machines we build like robots, self-driving vehicles, and artificial intelligence (Wallach 2010) so that machines do not harm humans either maliciously or unintentionally. This trend may have begun with Asimov’s Three Laws of Robotics. However, there are many different philosophical and other issues with this definition of machine ethics, including the lack of grounds for anthropomorphically assuming that a human ethics would be appropriate for a machine ethics, beyond the context of human-machine interaction.

There is another broader sense of the term machine ethics which means any issue pertaining to machines and ethics, including how a machine ethics could be articulated by observing machine behavior, and (in a Simondonian sense (French philosopher Gilbert Simondon)) how different machine classes might evolve their own ethics as they themselves develop over time.

There is yet a third sense of the term machine ethics - to contemplate human-machine hybrids, specifically how humans augmented with nanocognition machines might trigger the development of new human ethical paradigms, for example an ethics of immanence that is completely unlike traditional ethical paradigms and allows for a greater realization of human capacity.

Machine ethics interfaces then, are interfaces (software modules for communication between users and technologies (machines, devices, software, nanorobots)) with ethical aspects deliberately designed into them. This could mean communication about ethical issues, user selection of ethically-related parameters, ethical issues regarding machine behavior, and ethical dimensions transparently built into the technology (like a kill switch in the case of malfunction). Machine ethics interfaces are the modules within machines that interact with living beings regarding ethical issues, pertaining to the ethics of machine behavior or the ethics of human behavior

Machine Ethics: 1) (conventional) technology designers attempting to incorporate models of human-centric morality into machines like robots, self-driving vehicles, and artificial intelligence to prevent humans from being harmed either maliciously or unintentionally, 2) any issue pertaining to machines and ethics, 3) the possibility of new ethical paradigms arising from human augmentation and human-machine hybrids.

Machine Ethics Interfaces: Interfaces (software modules for communication between users and technologies (machines, devices, software, nanorobots)) with ethical aspects deliberately designed into them. This could mean communication about ethical issues, user selection of ethically-related parameters, and ethical dimensions transparently built into the technology (like a kill switch in the case of malfunction).

Wallach, W. (2010). Moral Machines: Teaching Robots Right from Wrong. Oxford, UK: Oxford University Press.

Sunday, July 20, 2014

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.

Monday, July 14, 2014

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.

Sunday, July 06, 2014

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  

[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: รก-adrenergic receptor involvement in the late phase. Learn. Mem. 6, 88–96, 1999.

Sunday, June 29, 2014

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.

Sunday, June 22, 2014

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, 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"

Sunday, June 15, 2014

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). 

Monday, June 09, 2014

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.