Business // Compare and contrast robots and AI systems
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Compare and contrast robots and AI systems
With the increasing advancement of technology, data science consistently responds to the demand of global organizations where machine learning and artificial intelligence are important to shaping the future of enterprise. Artificial intelligence (AI) systems have four categories along two dimensions (Loureiro, Guerreiro, and Tussyadiah, 220): reasoning–behavior dimension and human performance–rationality dimension. These are: (1) systems that think like humans, (2) systems that act like humans, (3) systems that think rationally, and (4) systems that act rationally. Significantly, AI systems have the following capabilities: natural language processing to communicate in a natural language, knowledge representation to store information, automated reasoning – the use of the stored information to answer questions and to draw new conclusions, and machine learning to adapt to new circumstances and to detect and extrapolate patterns.
On one hand, the robot process automation (RPA) or robot system aims to replace people by automation done in an “outside-in’’ manner (Van der Aalst, Bichler, and Heinzl 270). This differs from the classical “inside-out” approach to improve information systems. Unlike the traditional workflow where people execute tasks according to designation, the use of robots varied in a way that it structured based on data input to information systems. It is typically combined with user interface interactions or connected to application programming interface (API). Thus, unlike human where managers can be given instructions that are out of designated task for the day, robots have structured data. An RPA tool operates by mapping a process in the RPA tool language for the software robot to follow, with runtime allocated to execute the script by a control dashboard. Nevertheless, this is important to business organization as it reduces the repetition of simple tasks on employees.
Burt explained in “How will the GDPR impact machine learning?” that machine learning lacks automated decision making. Robot system and artificial intelligence is similarly identified to replicate human being, but it needs further studies to apply most activities and ability of human persons. Note that as technology is trying to reduce human interventions in business by putting high investment machine learning, the general data protection regulation (GDPR) has somehow limited the potential impact of robot and artificial intelligence in the ways of human. Moreover, human beings’ capacity to decide on his own is how data science program tries to improve. For a man full of knowledge will not entertain inhibitions in making decisions for he can distinguish the false from factual and the right from wrong. Even if there is uncertainty, a human being knows how to distinguish various engagements, transactions, and development present in his environment for there is a borderline that separate the reality from fantasy.
No matter how enterprise improves its physical environment without rooting on standardized procedures and business value – collaboration, cooperation, and coordination – mostly likely business collapse. In addition, dealing with complex issues necessitates operations manager to deploy social learnings at work, expertise in discussions and documentations, and best training practices. Employees tend to easily collaborate and coordinate when management provides good training on improvement of communication, dealing with customers and IT updates. Hence, the complexity of managing an organization significantly involves a myriad of connections between its tangible resources (such as employees and machineries) and intangible resources (such as time values). All these require bringing quality and value in management and employee’s performance.
Works Cited
Burt, Andrew. “How will the GDPR impact machine learning?” May 16, 2018, https://www.oreilly.com/radar/how-will-the-gdpr-impact-machine-learnin g/
Loureiro, Sandra Maria Correia, João Guerreiro, and Iis Tussyadiah. “Artificial intelligence in business: State of the art and future research agenda.” Journal of business research 129 (2021): 911-926.
Van der Aalst, Wil MP, Martin Bichler, and Armin Heinzl. “Robotic process automation.” Business & information systems engineering 60 (2018): 269-272.
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