By Olivia Giles at January 13 2019 20:23:30
If the process is instantiated frequently and the instances are homegeneous, it is possible to create great process models that dramatically increase the efficiency of the process. The best way to ensure process improvement is to generate an environment in which people are motivated, enthusiastic and passionate about process management. Most of the time, knowledge processes are collaborative. By performing a process collaboratively it is possible that each task is carried out by the most specialised, experienced and knowledgeable worker in that specific area. Having a net of relations within the organization is a very important asset for people executing knowledge processes.
This method seems to be the inefficient method as the chances or the profits to rise at the higher level often degrades. The marketing costs and the net returns tend to decrease that prevents the consumers from earning accountable outputs in time. Further, the latest and modern rice milling units make use of more efficient technology that hep them yield a large net return and the calculated costs become better for the large quantity of paddy. Roughly, we can say that capacity utilization using the modern methods is approximately 70% in comparison to the conventional methods used (approx. 45%). Rice is the staple food and is one of the man source among all the food grains.
Define the starting point of the process of project. This is the first step that starts of the process. For example, the first step could be project planning or research. Write down the starting point and the end result. Both of these should be in boxes with some space in between them. Adjust this space according to the number of steps and sub-steps involved in the process. Draw an arrow from the starting point to the end result. Along this arrow, list the various steps in order that are needed to go from the starting point to the end result. Include any sub-steps as needed.
The possibility of developing some such artifact has intrigued human beings since ancient times. With the growth of modern science, the search for AI has taken two major directions: psychological and physiological research into the nature of human thought, and the technological development of increasingly sophisticated computing systems. In the latter sense, the term AI has been applied to computer systems and programs capable of performing tasks more complex than straightforward programming, although still far from the realm of actual thought. The most important fields of research in this area are information processing, pattern recognition, game-playing computers, and applied fields such as medical diagnosis.
Current research in information processing deals with programs that enable a computer to understand written or spoken information and to produce summaries, answer specific questions, or redistribute information to users interested in specific areas of this information. Essential to such programs is the ability of the system to generate grammatically correct sentences and to establish linkages between words, ideas, and associations with other ideas. Research has shown that whereas the logic of language structure-its syntax-submits to programming, the problem of meaning, or semantics, lies far deeper, in the direction of true AI.
Many scientists remain doubtful that true AI can ever be developed. The operation of the human mind is still little understood, and computer design may remain essentially incapable of analogously duplicating those unknown, complex processes. Various routes are being used in the effort to reach the goal of true AI. One approach is to apply the concept of parallel processing-interlinked and concurrent computer operations. Another is to create networks of experimental computer chips, called silicon neurons, that mimic data-processing functions of brain cells. Using analog technology, the transistors in these chips emulate nerve-cell membranes in order to operate at the speed of neurons.