Intelligence Without Reason

Computers and Thought are the two categories that together define Artificial Intelligence as a discipline. It is generally accepted that work in Artificial Intelligence over the last thirty years has had a strong influence on aspects of computer architectures. In this paper we also make the converse claim; that the state of computer architecture has been a strong influence on our models of thought. The Von Neumann model of computation has lead Artificial Intelligence in particular directions. Intelligence in biological systems is completely different. Recent work in behavior-based Artificial Intelligenge has produced new models of intelligence that are much closer in spirit to biological systems. The non-Von Neumann computational models they use share many characteristics with biological computation

BUSINESS INTELLIGENT AGENTS FOR ENTERPRISE APPLICATION

Fierce competition in a market increasingly crowded and frequent changes in consumer requirements are the main forces that will cause companies to change their current organization and management. One solution is to move to open architectures and virtual type, which requires addressing business methods and technologies using distributed multi-agent systems. Intelligent agents are one of the most important areas of artificial intelligence that deals with the development of hardware and software systems able to reason, learn to recognize natural language, speak, make decisions, to recognize objects in the working environment etc. Thus in this paper, we presented some aspects of smart business, intelligent agents, intelligent systems, intelligent systems models, and I especially emphasized their role in managing business processes, which have become highly complex systems that are in a permanent change to meet the requirements of timely decision making. The purpose of this paper is to prove that there is no business without using the integration Business Process Management, Web Services and intelligent agents.business intelligence, intelligent agents, intelligent systems, management, enterprise, web services

Intelligence Management: Learning to Manage at the Margins

No business or organisation can remain in ignorance of, or unresponsive to, what is going on its environment and hope to remain successful for very long. This is especially true with regard to the business planning process (Cooke and Slack 1991). Even though a significant amount of decision-making takes place across the boundaries of the organisation concern with the environment within which companies operate is a relatively new phenomenon. Without understanding this environment it is very difficult to be effective at gleaning business intelligence. For this reason, this paper examines the way that organizations manage intelligence from the environment

THE INTELLIGENCE OF THE JEWISH NATION

Understanding Judaism must be able to distinguish between two things, namely Judaism as a nation and Judaism as a religion. Jews as a nation known as a nation that is superior or superior to race or nation the other. This is not without reason and cause, the long journey of the Jews and the forgings of life that make them strong. The expulsion that occurs constantly being made the nation's Jews be spread in the whole world, the road to be able to still survive life is to have the skill and certainly intelligence to think. Intelligence that they have formed from the intake of food that has been observed since the age of the womb. The food that is consumed is always observed in many cases

Creating Business Intelligence through Machine Learning: An Effective Business Decision Making Tool

Growing technological progressions have given rise to many issues concerning the contemporary decision making in business, which is a difficult phenomenon without Business Intelligence/ Machine Learning. The linking of machine learning with business intelligence is not only pivotal for business decision making but also for the business intelligence in totality, owing to the reason that in absence of machine learning, decision making couldn’t take place efficaciously. Machines need to learn, re-learn, and then only they can help your learning process. The below paper seeks to make this concept simple/ easy by removing the ambiguities using a general framework. In order to prove the impact of machine learning on business intelligence, we need to forecast the trends, what is going around the world – business has to stay updated, then only it can be a successful endeavour.  The paper posits the basic theories and definitions of business intelligence and machine learning. To learn from the past and forecast the future trends, many companies are adopting business intelligence tools and systems. Companies have understood the brilliance of enforcing achievements of the goals defined by their business strategies through business intelligence concepts and with the help of machine learning. It describes the insights on the role and requirement of real time BI by examining the business needs. Keywords: Business Intelligence (BI); Machine Learning (ML); Artificial Neural Networks (ANN); Self-Organizing Maps (SOM); Data Mining (DM); Data Warehousing (DW)

Supporting Intelligence Analysts with a Trust-Based Question-Answering System

Intelligence analysts have to work in highly demanding circumstances. This causes mistakes with severe consequences, which is the reason that support systems for intelligence analysts have been developed. The support system proposed in this paper assists humans by offering support that improves their performance, without reducing them in their freedom. This is done with a trust-based question answering system (T-QAS). An important part of T-QAS are trust models which keep track of trust in each of the agents gathering information. Using these trust models, the system can support the intelligence analyst by: 1) helping to decide which agents are trusted enough to receive questions, 2) providing information about the reliability of each of the sources used, and 3) advising in making decisions based on information from possibly unreliable sources. An implementation of last two capabilities of T-QAS is evaluated in an experiment in which participants perform a decision making task with information from possibly unreliable sources. Results show that the proposed T-QAS support indeed helps participants to improve their performance. We therefore expect that future intelligence analyst support systems can benefit from the inclusion of T-QAS

Kegiatan Pelatihan Peningkatan Adversity Quotient (AQ) Bagi Usaha Kecil Dan Menengah (UKM) Kreatif Di Kota Bogor

Small and Medium Enterprises (SMEs) have been proven to make significant positive contributions to efforts to tackle economic and social problems in Bogor City. But in fact people with an entrepreneurial mindset alone are not enough to succeed because many are still unable to overcome the problem of adversity. Without a high advertisy quotient (AQ), it is feared that someone will experience frustration and setbacks in becoming an entrepreneur. For this reason, to solve the existing problems, the Adversity Quotient (AQ) Enhancement Training for Creative Small & Medium Enterprises (SMEs) is given. Adversity Quotient is intelligence to overcome difficulties

Cognition as Computation: From Swift to Turing

            If one is going to compile a catalogue of the central concerns of Gulliver’s Travels, it goes without question or hesitation that the concept of reason looms large, if not possessing the uppermost priority, in Jonathan Swift’s authorial agenda. Swift is not only interested in reason insofar as practical rationalities, rational practicalities, and moral mores are concerned but also in the nature and constitution of reason itself. Thus, the purpose of this paper is to look at Swift’s treatment of the nature and constitution of reason and rationality in two of the Gulliver’s voyages: viz. the journeys to Brobdingnag and Balnibarbi. What is intriguing is that Swift seems to anticipate in the former voyage Alan Turing’s Imitation Game and in the latter voyage Turing’s idea of computational mechanization of intelligence, where he relates the two tales with diametrically opposite sentiments. The discussion of Swift’s anticipations is then set against the backcloth of the vicissitudes of the idea of Logical Machine from William of Soissons in the twelfth century to Alonzo Church’s Theorem and David Hilbert’s broad-ranging Entscheidungsproblem in the twentieth century