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This paper contends that the real understanding of natural language and the fulfillment of cloud computing cannot be reached without dealing with the significant sentimental factor. This paper points out that the achievement and enjoyment of cloud computing is highly reliant on break throughs in advanced intelligence. In this paper, advanced intelligence refers to the high level of interaction between natural intelligence and artificial intelligence. We introduce intelligent computing language in the software so that machines can take decisions autonomously and in real time. By applying artificial intelligence to the cloud, we are hoping to develop a system through which computers can manage themselves. For example, computer scientists are looking to develop software that follows computers’ power consumption and regulates their operation according to the specific needs at any given time, thus reducing energy expenditure. Implanting artificial intelligence into codes that will run in the cloud to improve efficiency is one of the strong research lines. Its part of a drive to create applications, executed in the cloud that goes beyond basic automation to anticipate situations and take decisions in real time over the Internet. We introduce intelligent computing language in the software so that machines can take decisions autonomously and in real time.
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PRESENTED BY
RAHUL ABHISHEK & SUJATA KUMARI
RESEARCH STUDENT OF CSE & IT BRANCH
email- [email protected] MAJHIGHAR IANI INSTITUTE OF TECNOLOGY AND SCIENCE, RAYAGADA
Intelligent Computing Relating to
Cloud Computing
Artificial Intelligence
Cloud computing
Affective computing
Implementation of advance intelligence
Conclusion.
References
Topics To Be Covered :-
Artificial Intelligence
•Artificial Intelligence is coined by John McCarthy in 1955. He defines it as “the science and engineering of making intelligent machines.”•They can neither improve gradually with experience nor learn domain knowledge by experimentation. They cannot automatically generate their algorithm, formulate new abstractions, or develop new solutions by drawing
analogies to old ones.
Artificial Intelligence and natural intelligence are mortal like humans.Natural intelligence can forget and lose information while Artificial Intelligence could do this if it was program to do so, but this would be counter-productive. In artificial intelligence the same information can be exact, every time with speed while in natural intelligence it is given the same information it cannot be as exact and is slower.
Comparison between Artificial Intelligence & Natural Intelligence :-
Cloud computingCloud computing is a general term for anything that involves delivering hosted services over the Internet.
The name cloud computing was inspired by the cloud symbol that's often used to represent the Internet in flowcharts and diagrams.
Affective computing is the study and development of systems and devices that can recognize, interpret, process, and simulate human affects. Now we can see with the help of eq. How affective computing is related with advanced intelligence.
The value of cloud computing v can be described asv = d s ∗ ∗ (1 + n) ∗ (1 + ai) 2 (1)Where d signifies the value of resource pools, s the value of service, n the value of Language understanding and AI is the value of advanced intelligence.
Thus machine interpreting the state of appropriate response through cloud computing in accordance with AI .
Affective computing
The current research in cloud computing is focused on the attempts to construct resource pools, including data, platform, software & so on. The challenge for cloud computing now is for business to take advantage of it .For the above implementation cloud computing must be built upon the basis of natural language understanding.Hence the attainment of cloud computing can be relied upon light weight devices (cell phones) to access services. Customers can access the cheap services delivered by cloud computing at any place (as long as they can see the “sky”) and any time (unless a solar eclipse and power outage occur simultaneously).
IMPLEMENTATION OF INTELLIGENCE SYSTEM
At each step, it uses a Random walk() procedure to change the current state s to a neighboring state line (9). The Random Walk() procedure tries a number (cn) of paths , where each path is a random sequence of a number (cl) of actions. Random Walk() uses a heuristic function to evaluate the ending state of each path and returns the best ending state out of the cn paths. MRW search fails to find a solution when the minimum heuristic value is not improved in cm iterations, or s ends up as a dead-end state (Line 5). In this case the MRW search simply restarts from the initial state sI . The iteration stops when a state satisfies goal state sG (Line 4), which means a SOLUTION is found.
Algorithm is that exhibit intelligent behavior by MRW :
Computer programs that exhibit intelligent behavior : . Algorithm 1: MRW (_)Input: a classical planning problem _Output: a solution plan1 s ← sI ;2 hmin ← h(sI ) ;3 counter ← 0 ;4 while s does not satisfy sG do5 if counter > cm or dead-end(s) then6 s ← sI ;7 hmin ← h(sI ) ;8 counter ← 0 ;9 s ← Random Walk(s,_) ;10 if h(s) < hmin then11 hmin ← h(s);12 counter ← 0;13 else14 counter ← counter + 1;15 return plan;
AI's scientific goal is to understand intelligence by building computer programs that exhibit intelligent behavior.It is concerned with the concepts and methods of symbolic inference, or reasoning, by a computer, and how the knowledge used to make those inferences will be represented inside the machine.But most progress to date in AI has been made in the area of problem solving, concepts and methods for building programs that reason about problems rather than calculate a solution.
Conclusion:
•Pratik, Rahul Abhishek, Payal Sinha. “Future Aspect Of Artificial Intelligence”. ICWET-2012, pp336.•Pratik, Rahul Abhishek “The Relationship Between Artificial Intelligence and Psychological Theories”, Proceeding ICETM – Sept 2012•Rich, Elaine, and Kevin Knight, (2006), “Artificial Intelligence”, McGraw Hills Inc.•Hyde, Andrew Dean (Sept 28, 2010), “The future of Artificial Intelligence”.•George F. Lunger, William A. Stubblefield (1993) Artificial Intelligence – Structuresand Strategiew for Complex Problem Solving, Benjamin-Cummings, Albuquerqe, ISBN ) 0-8053-4780-1.
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