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.

1. PRESENTED
BY
RAHUL ABHISHEK
&
SUJATA KUMARI
RESEARCH STUDENT OF CSE & IT BRANCH
email- rahulmithu.abhishek@gmail.com
MAJHIGHAR IANI INSTITUTE OF TECNOLOGY AND SCIENCE, RAYAGADA

2. Topics To Be Covered :-
Artificial Intelligence
Cloud computing
Affective computing
Implementation of advance intelligence
 Conclusion.
References

3. 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.

4. Comparison between Artificial Intelligence
& Natural Intelligence :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.

5. Cloud computing
Cloud 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.

6. Affective computing
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 as
v = 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 .

7. IMPLEMENTATION OF INTELLIGENCE SYSTEM
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).

8. Algorithm is that exhibit intelligent behavior by MRW :
 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.

9. Computer programs that exhibit intelligent behavior : .
Algorithm 1: MRW (_)
Input: a classical planning problem _
Output: a solution plan
1 s ← sI ;
2 hmin ← h(sI ) ;
3 counter ← 0 ;
4 while s does not satisfy sG do
5 if counter > cm or dead-end(s) then
6 s ← sI ;
7 hmin ← h(sI ) ;
8 counter ← 0 ;
9 s ← Random Walk(s,_) ;
10 if h(s) < hmin then
11 hmin ← h(s);
12 counter ← 0;
13 else
14 counter ← counter + 1;
15 return plan;

10. Conclusion:
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.

11. Reference:
•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 ) 0-80534780-1.