Innovation and Technology Management: The Perspective of the Defintion and Managment of Knowledge (Essay Sample)

INNOVATION AND TECHNOLOGY MANAGEMENT
By Students name
Business
Tutor: …………………...
University of…………….
Department of…………..
20th April 2016
Introduction
Recently, Stephen Hawking warned of humanities impending doom in a century or so as we rapidly progress in innovation and technology. He posited that although progress is good, this progress can create a way of new things going wrong. He emphasized how nuclear war, genetically engineered viruses and global warming are but some of our own doomsday creations. (O'Callaghan, 2016). The use of AI in knowledge management has raised fundamental questions. One common one is that the AI system has not yet reached a point where it can converse with a human. Nevertheless, most modern knowledge management systems have incorporated Artificial intelligence capabilities. These capabilities have also raised certain ethical and moral issues with industrial and knowledge experts. Some have come to believe, including Stephen Hawking and Elon Musk that the continuous improvement of this AI technology would spell doom for the human race. They ponder that certain restrictions need to be put in place such that the growth of AI would not pose a big threat in the future.
Artificial intelligence also provides a strong tool for the expansion of knowledge in the present day. There is consensus that today’s businesses are viewed from the knowledge perspective rather than industrial (Maier, 2007). Knowledge has become one of their biggest resources. For an organization to succeed in the current market place they have to create, evaluate and advance their knowledge resources (Metaxiotis et al., 2003). Information and Communication technologies assist in the management of knowledge. These modern ICT systems are coupled with artificial intelligence potential that would improve on the information and knowledge management lifecycle which in turn will improve the value of data that is provided to individuals and organizations. These AI technologies are applied in areas of mathematical logic, pattern recognition and self-programming computers. These intelligent tools are used for data mining, profiling of users, semantic text analysis and matching of patterns. These tools improve how knowledge is handled and processed within machines. These machines would then be able to interpret the knowledge to draw conclusions.
This paper will try to evaluate the growth of AI technology and how it has the potential to affect how humans and organizations handle knowledge. It will also highlight the recent trends of AI in knowledge management systems and how they have come to be used in core business processes i.e. profiling users, the management of content and the personalization of human and computer interactions. The paper will also review certain ethical issues raised by AI detractors in the context of human interactions and the advancement of AI technology.
Literature Review
Artificial Intelligence
AI, previously known as machine intelligence, has its roots from a 1956 summer project at the Dartmouth Campus. Artificial intelligence can be chronicled in a couple of manners. Firstly, it is scientific area that endeavours to uncover the essence of intelligence and help create machines that are smart. Secondly, it tries to study complex problems which cannot be deciphered without the application of intelligence (Dilek, Cakır and Aydın, 2015).
For a system to be defined as intelligent it must exhibit certain characteristics such as, machine learning, ontologies, deduction using statistical approaches, perception, empathy simulation, creativity and information retrieval etc. There are two forms of Artificial intelligence approaches i.e. the classical approach and distributed approach which focus on individual human behaviour and social behaviour respectively. For distributed approach, the solution to problems is arrived at by sharing knowledge and cooperating among knowledge agents whereas the classical approach relies on the inference capabilities and knowledge portrayals of individuals.
The fears of AI are widespread even in popular culture with depictions of machines taking over the world e.g. Stanley Kubrick’s Space Odyssey in 2001 and the character of Arnold Schwarzenegger in the Terminator Sequels. Author and inventor Ray Kurzweil defined the term singularity as the moment in time when the intelligence of machines would surpass that of humans. He gave an estimation of such an occurrence to be in 2045. Hawking et al., (2014) wrote an article in the Huffington Post which illustrated that the creation of an AI would be the greatest event in human history and it may prove to be the last. Another supporter for such views is billionaire Elon Musk who referred to Artificial Intelligence as the biggest existential threat. He called for an international oversight over the field of AI and compared the threat to be greater than that of a nuclear disaster (Kramer, 2014). Other experts have faulted this assertion and claim that this day is far of in the future
Mark Zuckerberg and Ashton Kutcher are notably personalities that openly support AI. They recently invested in a company that is in the process of creating an artificial brain. Other experts in the field of AI including Charlie Ortiz, insist that the fears are overblown. He illustrated that this fears are rooted in the belief that when entities become smarter they tend to be more violent and suppressive. He described the human race, which has grown in intelligent, as being more caring and peaceful with time and that the growth of AI is still far from registering any real breakthroughs (Lewis, 2014).
With the increased talk of intelligent machines, they have become the most important and disconcerting concept the human race has encountered. Bostrom (n.d) in his study of existential risk insists that AI can be the biggest doom-laden technology of all time. With the growth of self-programming computers that have an exponential growth in intelligence beyond that of human comprehension, enslavement by this self-improving AIs is not a far off notion. Nevertheless, he posits that if humans are able to program human-friendly values into these intelligent machines then this values would remain regardless the intelligence growth in machines. He explains the concept of superintelligence which implies a level of performance in virtually all aspects that is far beyond the cognitive realm of humans. This definition is premised on the same assumptions that the innovators of AI thought process used. It is worth noting that these assumptions were discredited in later years.
Herbert Simon in 1957 declared that the dawn of AI was here. He posited that the world was in the verge of seeing machines that can learn, think and create. The ability of this machines would increase exponentially and would be comparable to human mind problem solving skills. This development needs the human species to engage in a discussion and soul-searching where man would question their duty in this world, in which their only advantage i.e. intelligence is surpassed by that of the machines. Simon collaborated with RAND researcher Allen Newell to implement the General Problem Solver. This program had the potential of initializing the age of superhuman machine intelligence. Simon was using Aristotle’s idea of means-ends algorithm analysis which aimed at reducing the distance from an inceptive state to the desired objective as determined by the rules set out by a user. The discovering of inference operations would have been their breakthrough which would have propelled the creation of intelligent machines.
Artificial Intelligence Applications
There are many definitions of information, data and knowledge that exist. Data is said to be raw facts. When these raw facts are organized then they are information. This information is then analysed and interpreted into what is knowledge. The enthusiasm of knowledge management has been felt since the 1990s but was cut short in later years after it emerged how the knowledge management tools and software were highly limited (Hoffman et al., 2008). For knowledge management to be effective the organization and distribution of information must be systematic and efficient. The integration and circulation of meaningful information is one of the key issues of knowledge management.
Many organizations view the loss of expertise in form of experienced employees as major problem for knowledge management. These loss of knowledge is coupled by the fact the new workers would not be able to access this knowledge that was possessed by the lost workers (Hoffman and Hanes, 2003). This has led to the development of Expert Systems (ES). This ES have improved the management of knowledge in organizations. Expert systems can be defined as a programs that apply inferential reasoning that enables these systems to execute duties that a human specialist can do. This ES also possesses a wide array of information in specific domains. Expert systems are based on the assumption of certainty and gravitate towards the clearly defined alternatives. This is in contrast to Knowledge management systems that are based on uncertainty. ES have some drawbacks which include not being able to respond to vague questions like a human can and the difficulty in updating the system and learning from experience.
Another application of AI is the use of Artificial Neural Networks (ANNs) in knowledge management systems. ANNs are capable of functioning with incomplete data. They can profile users which will improve targeting of persons with specific preferences. The application of such a feat is that the knowledge ma...