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Generic alternatives accutane artificial intelligence that can make predictions of the future based on its past experiences. The work has been published on the preprint server arXiv.
In humans, what makes it possible for some to predict the future, is an ability to make predictions based on past experience that have worked well for them in the past without any external input. In computer code called evolutionary algorithms, a piece of code that can make future predictions, evolutionary algorithms require human input or, better, prior behavior. This work, by a team led Professor of Statistics Michael W. Snyder at Stanford University, is an attempt to use computational evolutionary approaches along with statistical tools to create intelligent systems.
"When considering the future, it is often said that an intelligent system must be able to make a prediction without relying on any prior or external input – a capability that is often lacking with evolved algorithms," said Professor Snyder. "Our work, which we performed in the lab of Yudong Song, demonstrates that artificial neural networks (ANNs) can do this in the presence of prior knowledge. A key contribution is to provide a method for performing back-propagation and through time to build update a posterior probability distribution of the data over time."
When humans think about the future, they tend to think about what they have done in the past that was helpful in predicting what will happen the future. For example, while people are thinking about the upcoming election, they may decide that would never vote for any candidate whom they believe does not have a good chance at winning. Their prediction is based on the past. This process of back-propagining, or making predictions based on their past behavior, is called predictive inference. But how does a computer make predictions using the same kind of methods? Artificial neural networks (ANNs) work well for recognizing objects, patterns in an image, and recognizing speech. Proscar 5mg $58.37 - $1.95 Per pill But they do not seem to work well at predicting what might happen in the future.
The answer to this problem lies in evolutionary algorithms. These systems use DNA sequences and natural selection to learn how survive in the environment by adapting to survive under various environmental changes. The first step is to learn how organisms in the population have performed past times where they lived, so that the system can predict how they may perform in future times the absence of any changes that need to be made.
In the study, published on arXiv preprint server, the team, which included Yudong Song and David H. D. Smith of Princeton University and Richard M. Smith of Princeton University, used ANNs to model the behavior of a neural network that predicted the probability of future events based on a history of past events. They learned how to predict the probability of certain events, like the occurrence of a rainstorm.
The first part of experiment focused on learning how to build an ANN for predicting rain-storms under various conditions. They first trained the ANN with data where network had made two or more incorrect predictions for a specific forecast. Next, the program was tested on data of a new forecast that had been generated without any prior knowledge about how the data of old forecast did or not match the predictions.
To build an ANN with more flexibility, they then combined their two previous programs into one program and started using it to predict new data while they had not yet learned how correctly it had performed in the one prior test of predicting the next event.
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