Top 5 Immediate Money-Making Applications of Blockchain Technology

The right question is not whether Bitcoin is over or under-valued, or over or under-hyped, but what the biggest potential money-making applications might be. While we wait for consumer-ready cryptocurrency applications to be presented to us by the financial services industry and other trusted providers, in the progression of ATMs, online billpay, eStatements, and Apple Pay, there are many other opportunities to be explored.

Blockchains could be the last piece of core infrastructural technology needed to facilitate the machine learning revolution in the same progression as the industrial revolution, only quicker.

1. Banking 2.0: The first and most obvious application of blockchain technology is the opportunity to reinvent the banking and financial services industry. The current monetary system is far too slow - it takes days and weeks to transfer funds, where cryptocurrency transactions are received immediately anywhere in the world. Sending a payment to a software development team in India means people receiving money instantly, and at a fraction of the traditional transaction cost. This could mean a tremendous speed-up in the velocity of money, and a way to allow legacy banking systems to interoperate, reorganize themselves, and make the whole way they do business more efficient. 
2. Financial Markets. In financial markets, one clear application of blockchain technology is algorithmic trading and back office operations. High-frequency trading could be taken to the next level implemented in smart contract DACs (decentralized autonomous corporations) and executed by semiautonomous agents with the ability to act more quickly and better crawl information sources for price, news, and sentiment changes. Similarly, whole tiers of back-office operations like clearing currently handled by people-agents could be handled by blockchain-agents. The automation economy is well under way, and blockchains provide the final required checks-and-balances feature of accountability. Blockchains instantiate a robust technology record of all transactions in a universal ledger system that is available for future lookup on-demand at any moment. Financial services businesses like the mortgage industry could implement smart property via blockchains as a universal asset registry and transfer system, and smart contracts for payment automation, interest rate resets, and securitization packaging. 
3. RTB and BI Automation. In the same vein as smart contract-based algorithmic trading, blockchains are also well-suited for implementation in other automated high-frequency markets like real-time bidding for advertising and business intelligence. These are already heavily machine algorithm-based markets that could be easily facilitated by being instantiated in blockchains. Again there is quicker, better, more-trackable, more-efficient, permanent, universal worldwide execution, together with record-keeping, monitoring, and tracking. The RTB market is billions of dollars at present and estimated to grow to $42 billion in the next three years. Likewise automated business intelligence is big business, and big data, for example, domain name hosting services use machine learning algorithms to continuously obtain competitive data points for standard services such as the cost of 1-year hosting with 2 GB of storage.
4. Blockchain IOT and M2M. We think cryptocurrencies might make our human lives easier, and they do, but even more so, they are for the machine economy. Cryptocurrencies are the economic layer the web never had, and can facilitate not just remuneration, but also the communication, coordination, and tracking of all machine-to-machine and machine-to-human interactions. While two thirds of people are estimated to be online in five years (from the current one-third), 25 billion things are forecast to be online by 2020. A corresponding Internet-of-Money is needed to organize this Internet-of-People and Internet-of-Things, for example seamlessly facilitating a connected car’s progression from smarthome IOT network to smartcity highway to automated city center parking. IBM's Adept announcement at CES is an early example of the idea of smartnetwork IOT coordination using blockchain technology.
5. Blockchain Thinkers. Not only is blockchain technology a potential means of enacting Friendly AI, it is more broadly a new concept and tool for instantiating intelligent computing operations in a blockchain architecture. This could have implications for both the development of artificial intelligence and human cognitive enhancement. DeepMind's Neural Turning Machines as an external memory for machine learning algorithms is an example of this kind of instantiation structure in artificial intelligence. For cognitive enhancement too, a blockchain could be the tool that makes lifelogging useful, recording every thought as a transaction in a blockchain memory, for search and recovery later, for example in the cases of Alzheimer's disease and stroke rehabilitation. One implication of Blockchain Thinkers is Blockchain Advocates. Blockchain Advocates are blockchain-based smart contracts as a new form of independent third-party advocate that can act on your behalf in future time frames. Right now you can set up smart contracts to monitor your smarthome IOT network, for example pinging you if the security system goes offline. In the future, your smart contract advocates could confirm that your digital mindfile is still running and being backed up appropriately, doing future real-time oracle lookups. “You’re still running on the current standard, Windows 36” your smart contract butler informs you. (More information: Blockchain Thinkers: The Brain as a DAC and Cognitive Applications of Blockchain Technology).

Bitcoin and blockchain technology could be just the first application of decentralization as a new form of information technology. 

Overall, blockchains are a new class of decentralized information technology for the potential execution of any kind of administrative task more efficiently, from all applications of money and finance, to government to health. Blockchains are a global-scale coordination mechanism - quicker, more transparent, more participative, and more accessible. Blockchains are a supercomputer for reality in the sense that they are a management tool for any system that can be quantized or divided into discrete elements or constituent parts. Bitcoin as the current ‘legacy’ cryptocurrency with more entrenched network effect adoption than other cryptocurrencies might not be the final or enduring cryptocurrency. Likewise the blockchain architecture as currently instantiated with questionably expensive and wasteful proof-of-work mining operations might not be the final architecture. However, it is harder to argue that decentralization as a new concept and class of information technology is not here to stay given the liquidity and penetration reach of the Internet. Focusing on end-user applications could help Bitcoin shift from its nacency into a more mature phase of cryptocurrency industry development, becoming a value currency, not just a development currency or speculative currency.

More Information: Swan, M. (2015). Blockchain: Blueprint for a New Economy. O'Reilly Media.

Blockchains as a Granular Universal Transaction System

Blockchain technology is a new concept in large-scale coordination due to a number of key features. First, a blockchain is an open universal transaction system. Every transaction worldwide is processed the same way and posted and made available for viewing on the blockchain. The transaction ledger is publicly-inspectable on-demand at any future moment.

Second, blockchains are trustless in the sense of not having to find or trust any of the other parties in the transaction; it is just necessary to trust the system. This suggests that orders-of-magnitude more transactions may be possible in trustless systems since the architecture is a mechanism allowing anyone to transact with anyone anywhere; geographical proximity, personal knowledge, and the search problem are all reduced or resolved. This is conceptually a next step in the progression of how Amazon (a global system) opens up trading in a way that Craigslist (geographically-local) does not.

Third, blockchains are a universal tracking system that might be able to accommodate infinitely more granularity than has been feasible and cost-effective to monitor previously. The optimality of what level of transaction detail is best to post directly to the blockchain (thus invoking the expensive mining operation for their recording) is being sorted out in different ecosystem tiers. The overall blockchain ecosystem is developing to avoid bloating the blockchain with too many micro-transactions by making use of special-purpose sidechains, decentralized off-chain storage (for example with MaidSafe, batched transactions (like batched notary sidechains to register large groups of legal documents), and Merkle trees (confirming and storing a whole corpus of data with one meta-hash).

Blockchains are a Supercomputer for Reality, a Mechanism for Orchestrating Quanta

The key idea of blockchains as a universal transaction system is that they are an automated computational mode, a seamless universal infrastructural element for the coordinated activity of granularity. Blockchains could be a universal transaction system on an order never before imagined that could possibly be used to coordinate the whole of human and machine activity. In this sense, blockchain technology is a supercomputer for reality. Any and all phenomena that can be quantized (defined in discrete units or packages) can be denoted this way and encoded and transacted in an automated fashion on the blockchain. As big data seeks to perhaps eventually model and predict all phenomena, natural and otherwise, so too might blockchains accompany big data for the tracking and administration of all phenomena.

One summary and prognostication of this dynamic and the potential universal applicability of blockchains is that anything that can be decentralized will be. This has an implied assumption about the inherent efficiency, benefit, and potential superiority in certain situations of the blockchain model. Decentralization is ‘where water goes’ (where water flows naturally, along the path of least resistance and least effort). The blockchain is an Occam’s razor, a natural efficiency process.

Blockchains are thus an intriguing model for coordinating the full transactional load of any large-scale system, whether the whole of different forms of human activity (social systems) or any other system too like a brain. In a brain there are quadrillions of transactions that could perhaps be handled in the universal transactional system architecture of a blockchain, like with Blockchain Thinking models.

Further, it is not just the transaction-handling capability of the blockchain as a universal coordination system but other properties that can also be applied through-out such as demurrage incitory stimulation for dynamic resource redistribution across the system. In Blockchain Thinking, this could be redistributing brain currencies like ideas and potentiation. Thus, it is not the mere orchestration features of universal blockchain systems but their enhancement possibilities that is perhaps the more interesting point. Not only can we better organize larger-scale existing activity with blockchains, but we can also possibly open up new classes of as-yet unimagined functionality and potentiality.

More information: Swan, M. (2015). Blockchain: Blueprint for a New Economy. O'Reilly Media.

Technology is ‘The Other’ with whom Humans Engage the most

The Contemporary Media Environment (CME) is the current situation of the widespread connected world of computing, which features the pervasive presence of technology in an increasingly rich information environment between and amongst human and machine entities.

One aspect of the CME is the increasing emergence of technology as ‘the other’ in the human-technology relation. Humans are now in a wholly new conceptualization and interaction with technology, and also information, where non-human entities are the primary other party in the majority of interactions (Floridi 2014). Technology is ‘the other’ with whom humans are engaging the most.

The theme of the ‘technology other’ has often been explored in film, with the increasing trend of humans and technology being portrayed in full partnership, for example in Big Hero 6 (2014), Her (2013), and Robot & Frank (2012).

Another way that the CME is manifesting the technology other is through embodiment, and in an escalation in the forms and types of human interaction. The technology other is no longer conceived narrowly as Amazon and Netflix recommendations, but instead as a fully-embodied agent. An example of this is robotic personal assistants for home and work like Robotbase’s Personal Robot, MIT’s JIBO, and Amazon’s Echo. Likewise artificial companions, for a variety of functional interaction with humans, may be the next innovation.

A sense of embodiment might also be perceived with advanced voice assistants like Apple’s Siri, Google Now, and Microsoft’s Cortana; they are a new kind of object-person.

Even beyond technology-as-other is technology-as-partner: the best ‘worker’ for many contemporary jobs in the automation economy, perhaps soon to be the machine economy, is a human and a machine in collaboration (Cowen 2013, Carr 2014).

Machine Cognition and AI Ethics Percolate at AAAI 2015

The AAAI’s Twenty-Ninth Conference on Artificial Intelligence was held January 25-30, 2015 in Austin, Texas. Machine cognition was an important focal area covered in two workshops on AI and Ethics, and Beyond the Turing Test, and in a special track on Cognitive Systems. Some of the most interesting emergent themes are discussed below.

Computational Ethics Systems
One main research activity in machine ethics is developing computational ethics systems. The status is that there are several such systems, however, a paucity of overall standards bodies, general ethics modules, and an articulation of universal principles that might be included like human dignity, informed consent, privacy, and benefit-harm analysis. Some standards bodies that are starting to address these ideas include the IEEE’ s Technical Committee on Robot Ethics and European committees involved in RoboLaw and Roboethics. 

One required feature of computational ethics systems could be the ability to flexibly apply different systems of ethics to more accurately reflect the ways that human intelligent agents approach real-life situations. For example, it is known from early programming efforts that simple models like Bentham and Mill’s utilitarianism are not robust enough ethics models. They do not incorporate comprehensive human notions of justice that extend beyond the immediate situation in decision-making. What is helpful is that machine systems on their own have evolved more expansive models than utilitarianism such as a prima facie duty approach. In the prima facie duty approach, there is a more complex conceptualization of intuitive duties, reputation, and the goal of increasing benefit and decreasing harm in the world. This is more analogous to real-life situations where there are multiple ethical obligations competing to determine the right action. GenEth is a machine ethics sandbox that is available to explore these kinds of systems for Mac OS, with details discussed in this conference paper.

There could be the flexible application of different ethics systems, and also integrated ethics systems. As in philosophy, computational ethics modules connote the idea of metaethics, a means of evaluating and integrating multiple ethical frameworks. These computational frameworks differ by ethical parameters and machine type; for example an integrated system is needed to enable a connected car to interface with a smart highway. The French ETHICAA (Ethics and Autonomous Agents) project seeks to develop embedded and integrated metaethics systems.

An ongoing debate is whether machine ethics should be separate modules or part of regular decision-making. Even though ultimately ethics might be best as a feature of any kind of decision-making, ethics are easiest to implement now in the early stages of development as a standalone module. Another point is that ethics models may vary significantly by culture; consider for example collectivist versus individualist societies, and how these ideals might be captured in code-based computational ethics modules. Happily for implementation, however, the initial tier of required functionality might be easy to achieve: obtaining ethicist consensus on overall how we want robots to treat us as humans. QA’ing computational ethics modules and machine behavior might be accomplished through some sort of ‘Ethical Turing Test;’ metaphorically, not literally, evaluating the degree to which machine responses match human ethicist responses.

Computational Ethics Systems: 
Enumerated, Evolved, or Corrigible
There are different approaches to computational ethics systems. Some involve the attempted enumeration of all involved principles and processes, reminiscent of Cyc. Others attempt to evolve ethical behavioral systems like the prima facie duty approach, possibly using methods like running machine learning algorithms over large data corpora. Others attempt to instill values-based thinking in ways like corrigibility. Corrigibility is the idea of building AI agents that reason as if they are incomplete and potentially flawed in dangerous ways. Since the AI agent apprehends that it is incomplete, it is encouraged to maintain a collaborative and not deceptive relationship with its programmers since the programmers may be able to help provide more complete information, even while both parties maintain different ethics systems. Thus a highly-advanced AI agent might be built that is open to online value learning, modification, correction, and ongoing interaction with humans. Corrigibility is proposed as a reasoning-based alternative to enumerated and evolved computational ethics systems, and also as an important ‘escape velocity’ project. Escape velocity refers to being able to bridge the competence gap between the current situation of not yet having human moral concepts reliably instantiated in AI systems, and the potential future of true moral superintelligences indispensably orchestrating many complex societal activities.

Lethal Autonomous Weapons
Machine cognition features prominently in lethal autonomous weapons where weapon systems are increasingly autonomous, making their own decisions in target selection and engagement without human input. The banning of autonomous weapons systems is currently under debate. On one side, detractors argue that full autonomy is too much, and that these weapons no longer have ‘meaningful human control’ as a positive obligation, and do not comply with the Geneva Convention’s Martens Clause requiring that fully autonomous weapons comply with principles of humanity and conscience. On the other side, supporters argue that machine morality might exceed human morality, and be more accurately and precisely applied. Ethically, it is not clear if weapons systems should be considered differently than other machine systems. For example, the Nationwide Kidney Exchange automatically allocates two transplant kidneys per week, where the lack of human involvement has been seen positively as a response to the agency problem.

Future of Work and Leisure
The automation economy is one of the great promises of machine cognition, where humans are able to offload more and more physical tasks, and also cognitive activities to AI systems. The Keynesian prediction of the leisure society by 2030 is becoming more imminent. This is the idea that leisure time, rather than work, will characterize national lifestyles. However, several thinkers are raising the need to redefine what is meant by work. The automation economy, possibly coupled with Guaranteed Basic Income initiatives, and an anti-scarcity mindset, could render obligation-based labor a thing of the past. There is ample room for redefining ‘work’ as productive activity that is meaningful to one’s sense of identity and self-worth for fulfillment, self-actualization, social-belonging, status-garnering, mate-seeking, cooperation, collaboration, and meeting other needs. The ‘end of work’ might just mean the ‘end of obligated work.’ 

Persuasion and Multispecies Sensibility
As humans, we still mostly conceive and employ the three modes of persuasion outlined centuries ago by Aristotle. These are ethos, relying on the speaker’s qualities like charisma; pathos, using emotion or passion to cast the audience into a certain frame of mind; and logos, employing the words of the oration as the argument. However, the human-machine interaction might cause these modes of human-related persuasion to be rethought and expanded, in both the human and machine context. Given that machine value systems and character may be different, so too might the most effective persuasion systems; both those employed on and deployed by machines. The ethics of human-machine persuasion is an area of open debate. For example, researchers are undecided on questions such as “Is it morally acceptable for a system to lie to persuade a human?” There is a rising necessity to consider ethics and reality issues from a thinking machine’s point-of-view in an overall future world system that might comprise multiple post-biological and other intelligent entities interacting together in digital societies.