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Computer Science engineering deals with design, implementation, management of information system of both Software and Hardware processes. While Information Technology deals with the use of Computers and Computer Software to convert, store, protect process, transmit, and retrieve information, Computer Science is a scientific and practical Approach to computation and its applications. The difference between Computer Science and IT is that while Computer Science deals with the design and development of new software and hardware parts of computer, IT aims at designing, developing, implementing and managing computer-based Information Systems including software applications and computer hardware. When Computer and Communications technologies are combined, the result is Information Technology, or ‘InfoTech’. A Computer scientist specializes in the theory of computation and the design of computational systems.
Now, researchers from the Centre for Quantum Photonics (CQP) at the
University of Bristol together with collaborators from the University of
Queensland (UQ) and Imperial College London have increased the
likelihood of such a demonstration in the near term by discovering a new
way to run a quantum algorithm with much simpler methods than
previously thought.
The first definitive defeat for a classical computer could be achieved with a quantum device that runs an algorithm known as Boson Sampling, recently developed by researchers at MIT.
Boson Sampling uses single photons of light and optical circuits to take samples from an exponentially large probability distribution, which has been proven to be extremely difficult for classical computers.
Unlike other quantum algorithms, Boson Sampling has the benefit of being practical for near-term implementations, with the only experimental drawback being the difficulty of generating the dozens of single photons required for the important quantum victory.
However, the Bristol-UQ-Imperial researchers have found that the Boson Sampling algorithm can still be proven to be hard for classical computers when using standard probabilistic methods to generate single photons.
Dr Anthony Laing who led the CQP elements of the research said: "We realised we could chain together many standard two-photon sources in such a way as to give a dramatic boost to the number of photons generated."
Dr Austin Lund from UQ and currently on sabbatical in CQP added: "Once we had the idea for the boosted source, we needed to prove that it could solve a version of the Boson Sampling algorithm. We hope that the last major experimental hurdle has now been overcome.
"We developed this software for use in educational gaming, but it has
applications for all video game developers," says Dr. James Lester, a
professor of computer science at NC State and senior author of a paper
on the work. "This is a key step in developing player-adaptive games
that can respond to player actions to improve the gaming experience,
either for entertainment or -- in our case -- for education."
The researchers used "deep learning" to develop the AI software. Deep learning describes a family of machine learning techniques that can extrapolate patterns from large collections of data and make predictions. Deep learning has been actively investigated in various research domains such as computer vision and natural language processing in both academia and industry.
In this case, the large collection of data is the sum total of actions that players have made in a game. The predictive AI software can then draw on all of that data to determine what an individual player is trying to accomplish, based on his or her actions at any given point in the game. And the software is capable of improving its accuracy over time, because the more data the AI program has, the more accurate it becomes.
"At some point that improvement will level off, but we haven't reached that point yet," Lester says.
To test the AI program, the researchers turned to an educational game called "Crystal Island," which they developed years earlier. While testing Crystal Island, the researchers amassed logs of player behavior (tracking every action a player took in the game) for 137 different players. The researchers were able to test the predictive AI software against the Crystal Island player logs to determine its accuracy in goal recognition. In other words, they could tell the AI everything a player had done in Crystal Island up to a certain point and see what goal the AI thought the player was trying to accomplish. By checking the AI's response against the player log, the researchers could tell whether the AI was correct.
"For games, the current state-of-the-art AI program for goal recognition has an accuracy rate of 48.4 percent," says Wookhee Min, a Ph.D. student at NC State and lead author of the paper. "The accuracy rate for our new program is 62.3 percent. That's a big jump."
Computer Science engineering deals with design, implementation, management of information system of both Software and Hardware processes. While Information Technology deals with the use of Computers and Computer Software to convert, store, protect process, transmit, and retrieve information, Computer Science is a scientific and practical Approach to computation and its applications. The difference between Computer Science and IT is that while Computer Science deals with the design and development of new software and hardware parts of computer, IT aims at designing, developing, implementing and managing computer-based Information Systems including software applications and computer hardware. When Computer and Communications technologies are combined, the result is Information Technology, or ‘InfoTech’. A Computer scientist specializes in the theory of computation and the design of computational systems.
Making A Difference In The Developing World- Computer scientists and engineers describe the varied applications for
their computing knowledge [e.g., research science, fighting
forest fires, creating textbook graphics for the blind, rural community
connectivity]
for more detail click the link-www.cse.iitd.ernet.inThe quantum revolution is a step closer: New way to run a quantum algorithm
Theories show
how computing devices that operate according to quantum mechanics can
solve problems that conventional (classical) computers, including super
computers, can never solve. These theories have been experimentally
tested for small-scale quantum systems, but the world is waiting for the
first definitive demonstration of a quantum device that beats a
classical computer.
The first definitive defeat for a classical computer could be achieved with a quantum device that runs an algorithm known as Boson Sampling, recently developed by researchers at MIT.
Boson Sampling uses single photons of light and optical circuits to take samples from an exponentially large probability distribution, which has been proven to be extremely difficult for classical computers.
Unlike other quantum algorithms, Boson Sampling has the benefit of being practical for near-term implementations, with the only experimental drawback being the difficulty of generating the dozens of single photons required for the important quantum victory.
However, the Bristol-UQ-Imperial researchers have found that the Boson Sampling algorithm can still be proven to be hard for classical computers when using standard probabilistic methods to generate single photons.
Dr Anthony Laing who led the CQP elements of the research said: "We realised we could chain together many standard two-photon sources in such a way as to give a dramatic boost to the number of photons generated."
Dr Austin Lund from UQ and currently on sabbatical in CQP added: "Once we had the idea for the boosted source, we needed to prove that it could solve a version of the Boson Sampling algorithm. We hope that the last major experimental hurdle has now been overcome.
Researchers advance artificial intelligence for player goal prediction in gaming
Researchers
from North Carolina State University have developed artificial
intelligence (AI) software that is significantly better than any
previous technology at predicting what goal a player is trying to
achieve in a video game. The advance holds promise for helping game
developers design new ways of improving the gameplay experience for
players.
The researchers used "deep learning" to develop the AI software. Deep learning describes a family of machine learning techniques that can extrapolate patterns from large collections of data and make predictions. Deep learning has been actively investigated in various research domains such as computer vision and natural language processing in both academia and industry.
In this case, the large collection of data is the sum total of actions that players have made in a game. The predictive AI software can then draw on all of that data to determine what an individual player is trying to accomplish, based on his or her actions at any given point in the game. And the software is capable of improving its accuracy over time, because the more data the AI program has, the more accurate it becomes.
"At some point that improvement will level off, but we haven't reached that point yet," Lester says.
To test the AI program, the researchers turned to an educational game called "Crystal Island," which they developed years earlier. While testing Crystal Island, the researchers amassed logs of player behavior (tracking every action a player took in the game) for 137 different players. The researchers were able to test the predictive AI software against the Crystal Island player logs to determine its accuracy in goal recognition. In other words, they could tell the AI everything a player had done in Crystal Island up to a certain point and see what goal the AI thought the player was trying to accomplish. By checking the AI's response against the player log, the researchers could tell whether the AI was correct.
"For games, the current state-of-the-art AI program for goal recognition has an accuracy rate of 48.4 percent," says Wookhee Min, a Ph.D. student at NC State and lead author of the paper. "The accuracy rate for our new program is 62.3 percent. That's a big jump."
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