Applying AI for addressing chaos and indiscipline

M Rokonuzzaman | Published: December 28, 2018 21:31:18 | Updated: January 02, 2019 20:47:15


"There is no doubt that if self-driving vehicles powered by artificial intelligence (AI) can safely traverse through a chaos of cities and highways of developing countries, for those smart vehicles, driving in structured, disciplined environment of advanced countries would be far easier. Does it mean that self-driving innovations should target developing countries to be smart enough for deployment in advanced countries?"

Traffic in cities like Delhi or Dhaka in developing countries is highly chaotic, as opposed to the disciplined movement of vehicles in cities of advanced countries. In expressways in advanced countries, vehicles travel adhering to predefined speed limits through designated lanes following a set of rules. On the other hand, rampant traffic rule violations are the 'norms' in highways and cities of developing countries. There is no doubt that if self-driving vehicles powered by artificial intelligence (AI) can safely traverse through a chaos of cities and highways of developing countries, for those smart vehicles, driving in structured, disciplined environment of advanced countries would be far easier. Does it mean that self-driving innovations should target developing countries to be smart enough for deployment in advanced countries?

Like streets and highways, all forms of operations in developing countries are infested with chaos and indiscipline making them most complex for AI innovations to encounter. Does it mean that India and other developing countries should be the innovation laboratory that leads the world into the artificially intelligent machines marking the Fourth Industrial Revolution (FIR)? Unfortunately, the likely answer is NO. Rather, the inherent diversity, complexity, and chaos are running the risk of leaving developing countries farther behind in the race of benefiting from AI and the fourth industrial revolutions.

As research suggests, AI could potentially deliver an additional economic output of around $13 trillion by 2030, boosting global GDP by about 1.2 percent a year. But, this benefit will not be uniformly distributed across the countries. One of the precursors to benefit from the artificially intelligent machines is about the state of maturity of rules guiding existing operations. For example, self-driving vehicles can follow predefined rules but fail to handle chaos. Upon maturing in self-driving vehicle innovations, advanced countries will start reducing the economic loss caused by road accidents, improving efficiency in logistics and mobility, while developing countries will likely be late in benefiting from those innovations due to the chaos on their streets and highways.

Similarly, another FIR case is the Blockchain or Distributed Ledger Technology. The theoretical promise of this technology for immutable record keeping, decentralised decision-making, transparency, fractionalisation of asset ownership, and the elimination of intermediaries faces barriers in delivering the potential benefits in developing countries. For various reasons, management practice, both in public and private sectors, in developing countries is centralised and authoritarian as opposed to decentralised decision making guided by a set of rules. Similarly, as opposed to transparency and immutable record keeping the practice to manage information in developing countries rather often takes place in a clandestine manner. To impose subjective judgment and information sharing, the unwritten policy is to keep intermediaries. As a result, unless and until developing countries migrate from subjective and corrupt practices, FIR technologies like Blockchain have very limited scope to deliver intended benefit. On the other hand, advanced countries will take the advantage of it to automate their rule-guided operations in increasing speed, accuracy, and efficiency.    

AI innovations are not magical outcomes as they are shown in science fiction movies or books. They will likely follow a typical s-curve -- a slow start given the investment associated with learning and deploying the technology, and then acceleration driven by competition and improvements in complementary capabilities. Irrespective of the greatness of the ideas, or the strength of the underlying technologies, AI innovations will start emerging as rather primitive solutions. In the beginning, the effectiveness of those innovations will only be found in rule-based, disciplined operations. Countries and firms having high-level maturity of rule-based, disciplined operations will benefit from those initial emergences. Along the way, capabilities of those AI machines will grow to deal with increasing variations, but not to make them capable to deal with man-made chaos. For example, automobiles have been improved to traverse through rural roads, but automobile makers are no longer focusing on improving them further for unpaved rural roads of India, Bangladesh and other developing countries. Rather, to benefit from automobiles, investments are being made to pave roads and bring an acceptable degree of disciplines in guiding operations.

From the historical lessons, it has been found that innovations do not succeed by focusing on addressing the most complex problems at the beginning. Rather, success begins by finding and addressing simple problems, around which revenue could be generated with initial primitive solutions. Upon succeeding with the generation of revenue with rather primitive solutions, the long journey of incremental innovations begins. Innovators keep advancing underlying technologies, adding new features and improving existing ones for improving the quality and reducing the cost, so that more demanding requirement could be addressed in a profitable manner. To benefit from the progression of such innovations, customers should also keep progressing in improving their capability. For example, in order to benefit from enterprise resource planning (ERP) software applications, developed by global companies primarily targeting the automation of disciplined business functions of the western companies, governments as well as companies of developing countries had to streamline their operations.

Due to the chaotic situation, developing countries lag far behind in the adoption of AI innovations, and more importantly, the chaos, lack of rules and indiscipline stand in the way of the growth of start-ups to pursue AI innovations. In the absence of rules in operations, AI start-ups in developing countries need to mature their innovations quite further, in comparison to their counterparts in advanced countries. Weak R&D capacity, limited risk capital finance and the long journey of generating revenue from AI innovations are among major barriers for start-ups in developing countries to leverage innovation opportunities from the underlying FIR technology potentials.   At the end of the day, the objective of technology and innovation is profitable exploitation through addressing complexities-creating both consumer and producer surpluses. 

Starting from the streets to the factory floors to the operation of the government, it's time for developing countries to focus on bringing disciplines and set the rule of law in guiding their operations. Otherwise, the competitiveness gap between advanced and developing countries will keep widening in the globally connected market economy.

M Rokonuzzaman Ph.D is academic, and researcher on technology,

innovation and policy.  zaman.rokon.bd@gmail.com

 

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