By Elizabeth Spain at September 14 2018 06:17:53
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.
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.
It is extremely important to continuously improve knowledge processes, by creating an environment through which they can evolve. This can only be achieved through coordination of diverse disciplines such as knowledge management, change management, expectations management, etc... It is crucial to establish an adequate process context (the combination of technologies, procedures, people, etc... that support the processes). The process context must incorporate feedback mechanisms, change evaluation procedures, process improvement methods and techniques and must be flexible, in order to be able to incorporate enhancements in an agile but controlled way.
Tables : When your data doesn't clearly show a trend, use a table. You may have this data in Excel, and can even link to the Excel file. Quotations : Quotes are very powerful when they come from authorities or well-known individuals. In a persuasive presentation, you can use testimonials from other customers, for example. Stories : Stories are powerful when they support your message. They can be personal, related to current events, examples from other customers, and so on. They can be full-blown situations, or simple examples. Collect stories as you hear them and keep them in a file for use later.
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.
Knowledge workers carry out these processes by taking into account multiple inputs (generally a wide set of unstructured data and information) to perform difficult tasks and make complex decisions among multiple possible ways of doing the work, each one implying different levels of risk and possible benefits. They are dependent on individuals and it is not possible to automate them. One example of a knowledge process is "Marketing a new product". The same steps are followed each time a new product is launched (benchmarking competitors, deciding pricing strategy, planning promotion, etc...), but it is the experience, knowledge and intuition of the people that drive the process to success.