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The terms "revolution" and "disruption" inthe context of technological innovation are probably bandied abouta bit more liberally than they should. Technological revolution anddisruption imply upheaval and systemic reevaluations of the waythat humans interact with industry and even each other. Actualtechnological advancement, however, moves at a much slower pace andtends to augment our current processes rather than to outrightdisplace them. Oftentimes, we fail to realize the ubiquity oflegacy systems in our everyday lives sometimes to our owndetriment.

Consider the keyboard. The QWERTY layout of keys is standard forEnglish keyboards across the world. Even though the layout remainsa mainstay of modern office setups, its origins trace back to themass popularization of a typewriter manufactured and sold by E.Remington & Sons in 1874.1 Urban legend has itthat the layout was designed to slow down typists from jammingtyping mechanisms, yet the reality reveals otherwise thelayout was actually designed to assist those transcribing messagesfrom Morse code.2 Once typists took to the format, thekeyboard, as we know it today, was embraced as a global standard even as the use of Morse code declined.3 LikeQWERTY, our familiarity and comfort with legacy systems hascontributed to their rise. These systems are varied in their scope,and they touch everything: healthcare, supply chains, our financialsystems and even the way we interact at a human level. However,their use and value may be tested sooner than we realize.

Artificial intelligence (AI) and blockchain technology(blockchain) are two novel innovations that offer the opportunityfor us to move beyond our legacy systems and streamline enterprisemanagement and compliance in ways previously unimaginable. However,their potential is often clouded by their "buzzword"status, with bad actors taking advantage of the hype. When one cutsthrough the haze, it becomes clear that these two technologies holdsignificant transformative potential. While these new innovationscan certainly function on their own, AI and blockchain alsocomplement one another in such ways that their combination offersbusiness solutions, not only the ability to build upon legacyenterprise systems but also the power to eventually upend them infavor of next level solutions. Getting to that point, however,takes time and is not without cost. While humans are generallyquick to embrace technological change, our regulatory frameworkstake longer to adapt. The need to address this constraint ispressing real market solutions for these technologies havestarted to come online, while regulatory opaqueness hurdles abound.As innovators seek to exploit the convergence of AI and blockchaininnovations, they must pay careful attention to overcome bothtechnical and regulatory hurdles that accompany them. Do sosuccessfully, and the rewards promise to be bountiful.

First, a bit of taxonomy is in order.

AI in a Nutshell:

Artificial Intelligence is "the capability of machine toimitate intelligent human behavior," such as learning,understanding language, solving problems, planning and identifyingobjects.4 More practically speaking, however,today's AI is actually mostly limited to if X, then Yvarieties of simple tasks. It is through supervised learning thatAI is "trained," and this process requires an enormousamount of data. For example, IBM's question-answeringsupercomputer Watson was able to beat Jeopardy! championsBrad Rutter and Ken Jennings in 2011, because Watson had been codedto understand simple questions by being fed countless iterationsand had access to vast knowledge in the form of digital dataLikewise, Google DeepMind's AlphaGo defeated the Go championLee Sedol in 2016, since AlphaGo had undergone countless instancesof Go scenarios and collected them as data. As such, mostimplementations of AI involve simple tasks, assuming that relevantinformation is readily accessible. In light of this, Andrew Ng, theStanford roboticist, noted that, "[i]f a typical person can doa mental task with less than one second of thought, we can probablyautomate it using AI either now or in the near future."5

Moreover, a significant portion of AI currently in use or beingdeveloped is based on "machine learning." Machinelearning is a method by which AI adapts its algorithms and modelsbased on exposure to new data thereby allowing AI to"learn" without being programmed to perform specifictasks. Developing high performance machine learning-based AI,therefore, requires substantial amounts of data. Data high in bothquality and quantity will lead to better AI, since an AI instancecan indiscriminately accept all data provided to it, and can refineand improve its algorithms to the extent of the provided data. Forexample, AI that visually distinguishes Labradors from other breedsof dogs will become better at its job the more it is exposed toclear and accurate pictures of Labradors.

It is in these data amalgamations that AI does its job best.Scanning and analyzing vast subsets of data is something that acomputer can do very rapidly as compared to a human. However, AI isnot perfect, and many of the pitfalls that AI is prone to are oftenthe result of the difficulty in conveying how humans processinformation in contrast to machines. One example of this phenomenonthat has dogged the technology has been AI's penchant for"hallucinations." An AI algorithm"hallucinates" when the input is interpreted by themachine into something that seems implausible to a human looking atthe same thing.6 Case in point, AI has interpreted animage of a turtle as that of a gun or a rifle as a helicopter.7 This occurs because machines arehypersensitive to, and interpret, the tiniest of pixel patternsthat we humans do not process. Because of the complexity of thisanalysis, developers are only now beginning to understand such AIphenomena.

When one moves beyond pictures of guns and turtles, however,AI's shortfalls can become much less innocuous. AI learning isbased on inputted data, yet much of this data reflects the inherentshortfalls and behaviors of everyday individuals. As such, withoutproper correction for bias and other human assumptions, AI can, forexample, perpetuate racial stereotypes and racial profiling.8 Therefore, proper care for what goesinto the system and who gets access to the outputs must be employedfor the ethical employment of AI, but therein lies an additionalproblem who has access to enough data to really take fulladvantage of and develop robust AI?

Not surprisingly, because large companies are better able tocollect and manage increasingly larger amounts of data thanindividuals or smaller entities, such companies have remainedbetter positioned in developing complex AI. In response to thistilted landscape, various private and public organizations,including the U.S. Department of Justice's Bureau of Justice,Google Scholar and the International Monetary Fund, have launchedopen source initiatives to make publicly available vast amounts ofdata that such organizations have collected over many years.

Blockchain in a Nutshell:

Blockchain technology as we know it today came onto the scene inlate 2009 with the rise of Bitcoin, perhaps the most famousapplication of the technology. Fundamentally, blockchain is a datastructure that makes it possible to create a tamper-proof,distributed, peer-to-peer system of ledgers containing immutable,time-stamped and cryptographically connected blocks of data. Inpractice, this means that data can be written only once onto aledger, which is then read-only for every user. However, many ofthe most utilized blockchain protocols, for example, the Bitcoin orEthereum networks, maintain and update their distributed ledgers ina decentralized manner, which stands in contrast to traditionalnetworks reliant on a trusted, centralized data repository.9 In structuring the network in thisway, these blockchain mechanisms function to remove the need for atrusted third party to handle and store transaction data. Instead,data are distributed so that every user has access to the sameinformation at the same time. In order to update a ledger'sdistributed information, the network employs pre-defined consensusmechanisms and military grade cryptography to prevent maliciousactors from going back and retroactively editing or tampering withpreviously recorded information. In most cases, networks are opensource, maintained by a dedicated community and made accessible toany connected device that can validate transactions on a ledger,which is referred to as a node.

Nevertheless, the decentralizing feature of blockchain comeswith significant resource and processing drawbacks. Manyblockchain-enabled platforms run very slowly and haveinteroperability and scalability problems. Moreover, these networksuse massive amounts of energy. For example, the Bitcoin networkrequires the expenditure of about 50 terawatt hours per year equivalent to the energy needs of the entire country ofSingapore.10 To ameliorate these problems,several market participants have developed enterprise blockchainswith permissioned networks. While many of them may be open source,the networks are led by known entities that determine who mayverify transactions on that blockchain, and, therefore, therequired consensus mechanisms are much more energy efficient.

Not unlike AI, a blockchain can also be coded with certainautomated processes to augment its recordkeeping abilities, and,arguably, it is these types of processes that contributed toblockchain's rise. That rise, some may say, began with theintroduction of the Ethereum network and its engineering around"smart contracts" a term used to describecomputer code that automatically executes all or part of anagreement and is stored on a blockchain-enabled platform. Smartcontracts are neither "contracts" in the sense of legallybinding agreements nor "smart" in employing applicationsof AI. Rather, they consist of coded automated parametersresponsive to what is recorded on a blockchain. For example, if theparties in a blockchain network have indicated, by initiating atransaction, that certain parameters have been met, the code willexecute the step or steps triggered by those coded parameters. Theinput parameters and the execution steps for smart contracts needto be specific the digital equivalent of if X, thenY statements. In other words, when required conditions havebeen met, a particular specified outcome occurs; in the same waythat a vending machine sells a can of soda once change has beendeposited, smart contracts allow title to digital assets to betransferred upon the occurrence of certain events. Nevertheless,the tasks that smart contracts are currently capable of performingare fairly rudimentary. As developers figure out how to expandtheir networks, integrate them with enterprise-level technologiesand develop more responsive smart contracts, there is every reasonto believe that smart contracts and their decentralizedapplications (d'Apps) will see increased adoption.

AI and blockchain technology may appear to be diametricopposites. AI is an active technologyitanalyzes what is around and formulates solutions based on thehistory of what it has been exposed to. By contrast, blockchain isdata agnostic with respect to what is written into it thetechnology bundle is largely passive. It is primarily inthat distinction that we find synergy, for each technology augmentsthe strengths and tempers the weaknesses of the other. For example,AI technology requires access to big data sets in order to learnand improve, yet many of the sources of these data sets are hiddenin proprietary silos. With blockchain, stakeholders are empoweredto contribute data to an openly available and distributed networkwith immutability of data as a core feature. With a potentiallylarger pool of data to work from, the machine learning mechanismsof a widely distributed, blockchain-enabled and AI-powered solutioncould improve far faster than that of a private data AIcounterpart. These technologies on their own are more limited.Blockchain technology, in and of itself, is not capable ofevaluating the accuracy of the data written into its immutablenetwork garbage in, garbage out. AI can, however, act as alearned gatekeeper for what information may come on and off thenetwork and from whom. Indeed, the interplay between these diversecapabilities will likely lead to improvements across a broad arrayof industries, each with unique challenges that the twotechnologies together may overcome.

Footnotes

1 See Rachel Metz, Why WeCan't Quit the QWERTY Keyboard, MIT Technology Review(Oct. 13, 2018), available at: https://www.technologyreview.com/s/611620/why-we-cant-quit-the-qwerty-keyboard/.

2 Alexis Madrigal, The Lies You'veBeen Told About the Origin of the QWERTY Keyboard, TheAtlantic (May 3, 2013), available at: https://www.theatlantic.com/technology/archive/2013/05/the-lies-youve-been-told-about-the-origin-of-the-qwerty-keyboard/275537/.

3 See Metz, supra note1.

4 See Artificial Intelligence,Merriam-Webster's Online Dictionary, Merriam-Webster (lastaccessed Mar. 27, 2019), available at: https://www.merriam-webster.com/dictionary/artificial%20intelligence.

5 See Andrew Ng, What ArtificialIntelligence Can and Can't Do Right Now, Harvard BusinessReview (Nov. 9, 2016), available at: https://hbr.org/2016/11/what-artificial-intelligence-can-and-cant-do-right-now.

6 Louise Matsakis, Artificial IntelligenceMay Not Hallucinate After All, Wired (May 8, 2019),available at: https://www.wired.com/story/adversarial-examples-ai-may-not-hallucinate/.

7 Id.

8 Jerry Kaplan, Opinion: Why Your AI MightBe Racist, Washington Post (Dec. 17, 2018), availableat: https://www.washingtonpost.com/opinions/2018/12/17/why-your-ai-might-be-racist/?noredirect=on&utm_term=.568983d5e3ec.

9 See Shanaan Cohsey, David A.Hoffman, Jeremy Sklaroff and David A. Wishnick, Coin-OperatedCapitalism, Penn. Inst. for L. & Econ. (No. 18-37) (Jul.17, 2018) at 12, available at: https://papers.ssrn.com/sol3/papers.cfm?abstract_id=3215345##.

10 See Bitcoin Energy ConsumptionIndex (last accessed May 13, 2019), available at: https://digiconomist.net/bitcoin-energy-consumption.

Keywords:Artificial Intelligence + Robotics,Blockchain, Fintech

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