Saturday, November 27, 2010
"Thriving in an Era of Rabid Collaboration"
This lecture was given at UC Berkley. Brad Wheeler, CIO and VP of Information Technology at Indiana University, talks about some of the efforts that are made to help enable collaboration, and what collaboration is. Collaboration is "an unnatural act." Given the choice, people would rather work on their own than to be dependent on other people.
"Collaboration means co-laboring." Co-laboring means working together with each other. It is important to note that collaboration is different from cooperation:
"Collaboration is not the same as cooperation. Collaboration requires alignment around a common goal. Collaboration is about doing something together. Collaboration only lasts as long as the alignment around common purpose exists."
-James Hilton, U. of Virginia
There are barriers to collaboration, primarily in the factors of cost. As the value or cost of a project increases a phenomenon called "slope of retreat" occurs. This means that when high value is placed on a project, failure can become very devastating. When faced with greater possible failure, benefactors would rather retreat. This is akin to the concept of "risk" in Finance.
Why should companies and collaborate then? What about the universities that wish to fund research projects? The answer to these questions is simple: The payoff can be greater than the risk involved. Even if there are setbacks, you will still end up better off than you were to begin with.
The increasing use of open source software plays a key role in collaboration. When companies and universities use open source software they can cut their costs dramatically. In the video, Brad Wheeler said, "This is not anti-commercial." It's a more cost effective way of accomplishing objectives.
Collaboration gives a competitive edge to companies that seek it out and use it efficiently. IT governance plays a key role in that aspect. Organizations must decide how to run their IT departments, as it will ultimately determine how "well-oiled" their "machine" is.
Cloud computing can help universities with certain Information Technology needs. Topics discussed in the video at [1:08:43] include: Commercial Sourcing, Institutional Sourcing, and Consortium Sourcing.
Thursday, November 11, 2010
Applications of Neural Networks
Neural networks can be used in many different fields to solve complex problems beyond the scope of one person's thinking.
As stated in this website:
"Neural networks have been successfully applied to broad spectrum of data-intensive applications, such as:
* Process Modeling and Control - Creating a neural network model for a physical plant then using that model to determine the best control settings for the plant.
* Machine Diagnostics - Detect when a machine has failed so that the system can automatically shut down the machine when this occurs.
* Portfolio Management - Allocate the assets in a portfolio in a way that maximizes return and minimizes risk.
* Target Recognition - Military application which uses video and/or infrared image data to determine if an enemy target is present.
* Medical Diagnosis - Assisting doctors with their diagnosis by analyzing the reported symptoms and/or image data such as MRIs or X-rays.
* Credit Rating - Automatically assigning a company's or individuals credit rating based on their financial condition.
* Targeted Marketing - Finding the set of demographics which have the highest response rate for a particular marketing campaign.
* Voice Recognition - Transcribing spoken words into ASCII text.
* Financial Forecasting - Using the historical data of a security to predict the future movement of that security.
* Quality Control - Attaching a camera or sensor to the end of a production process to automatically inspect for defects.
* Intelligent Searching - An internet search engine that provides the most relevant content and banner ads based on the users' past behavior.
* Fraud Detection - Detect fraudulent credit card transactions and automatically decline the charge."
As you can see, there are many examples of how neural networks can be applied to different "fuzzy logic" questions. In my research of this topic, I have not been able to find many company-specific examples of neural networks in specific fields. Instead, there are many neural network software packages that provide suggestions on how to harness the power of neural networking.
One specific example on the neural networking software website says they used the technology to predict stock market trends, such as the next day's closing price and sensitivity analysis.
As stated in this website:
"Neural networks have been successfully applied to broad spectrum of data-intensive applications, such as:
* Process Modeling and Control - Creating a neural network model for a physical plant then using that model to determine the best control settings for the plant.
* Machine Diagnostics - Detect when a machine has failed so that the system can automatically shut down the machine when this occurs.
* Portfolio Management - Allocate the assets in a portfolio in a way that maximizes return and minimizes risk.
* Target Recognition - Military application which uses video and/or infrared image data to determine if an enemy target is present.
* Medical Diagnosis - Assisting doctors with their diagnosis by analyzing the reported symptoms and/or image data such as MRIs or X-rays.
* Credit Rating - Automatically assigning a company's or individuals credit rating based on their financial condition.
* Targeted Marketing - Finding the set of demographics which have the highest response rate for a particular marketing campaign.
* Voice Recognition - Transcribing spoken words into ASCII text.
* Financial Forecasting - Using the historical data of a security to predict the future movement of that security.
* Quality Control - Attaching a camera or sensor to the end of a production process to automatically inspect for defects.
* Intelligent Searching - An internet search engine that provides the most relevant content and banner ads based on the users' past behavior.
* Fraud Detection - Detect fraudulent credit card transactions and automatically decline the charge."
As you can see, there are many examples of how neural networks can be applied to different "fuzzy logic" questions. In my research of this topic, I have not been able to find many company-specific examples of neural networks in specific fields. Instead, there are many neural network software packages that provide suggestions on how to harness the power of neural networking.
One specific example on the neural networking software website says they used the technology to predict stock market trends, such as the next day's closing price and sensitivity analysis.
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