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Closed 11 years ago.
I am a build engineer in my current position but I dabble in applying AI techniques to improve our capabilities. What I am interested in is how your teams use AI Techniques (Pattern Recognition, Machine Learning, Bayesian Classification, or Neural Networks) in real life. I am looking for ideas on other ways of improving our processes and fun projects to start.
Examples of things I have tried:
Naive bayesian classifier for automatically assigning class labels (misdemeanor, felony, traffic violation) to free form text entered by court reporters.
Generated team schedule for the year using a genetic algorithm using a fitness function that assigned demerits for any scheduling conflicts, over / under allocation of team members, holidays, personal time off.
Binary associative memory for quickly querying environment configuration information for all applications deployed to all environments; including URLs, ports, source control location, environment, server, os, etc
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Closed 10 years ago.
Any help would be most welcome...
Thank you ^^
Simulation is the imitation of the operation of a real-world process or system over time.1 The act of simulating something first requires that a model be developed; this model represents the key characteristics or behaviors of the selected physical or abstract system or process. The model represents the system itself, whereas the simulation represents the operation of the system over time.
Benchmarking is the process of comparing one's business processes and performance metrics to industry bests or best practices from other industries. Dimensions typically measured are quality, time and cost. In the process of benchmarking, management identifies the best firms in their industry, or in another industry where similar processes exist, and compare the results and processes of those studied (the "targets") to one's own results and processes. In this way, they learn how well the targets perform and, more importantly, the business processes that explain why these firms are successful.
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Closed 11 years ago.
Which database is secure for web application for avoiding hacking
They're mostly all secure (though you can subscribe to any vulnerability announcement mailing lists... so if a major security flaw shows up in the next few years, you will have time to react).
Security issues are mostly design flaws, for example SQL injection, unescaped content that you print out directly onto your webpage, etc.
To learn some of the basics of web design security, you may wish to do all the tutorials on http://google-gruyere.appspot.com/
There's no guarantee that any software can be made immune from hacking. All databases can be made to work if secured properly; all of them are vulnerable if not. No one can say that database X is always secure.
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Closed 12 years ago.
By what mechanism does LinkedIn tell so fast, on group page, the connections of user who are also a part of that group?
I don't have any inside knowledge of Linkedin source code but I do know that they are the founding contributors of the nosql distributed key-value database Project Voldermort http://project-voldemort.com/. These kinds of systems are architected to return queries blazingly fast. They are often ideal for write seldom/read often scenarios and tend to sacrifice consistency for high scalability and availability.
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Closed 10 years ago.
I'm very curious as to what programming languages were used to make Google. In the early days, Larry Page and Sergey Brin tried to licence their search algorithm to Yahoo. What I would love to know is what language(s) was the software first written in?
I have a feeling it was C or perl (CGI), anyone know for sure?
Thank you in advance
Anyone know the database they used too?
The Google search engine was originally called BackRub and was written in Java and Python. To quote Wikipedia:
BackRub is written in Java and Python and runs on several Sun Ultras and Intel Pentiums running Linux. The primary database is kept on an Sun Ultra II with 28GB of disk. Scott Hassan and Alan Steremberg have provided a great deal of very talented implementation help. Sergey Brin has also been very involved and deserves many thanks.
-Larry Page page#cs.stanford.edu
Also interesting is Google's Corporate History page, though it doesn't talk as much about the original software.
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Closed 13 years ago.
One of my interests in AI focuses not so much on data but more on biologic computing. This includes neural networks, mapping the brain, cellular-automata, virtual life and environments.
Described below is an exciting project that includes develop a virtual environment for bots to evolve in.
"Polyworld is a cross-platform (Linux, Mac OS X) program written by Larry Yaeger to evolve Artificial Intelligence through natural selection and evolutionary algorithms."
http://en.wikipedia.org/wiki/Polyworld "
Polyworld is a promising project for studying virtual life but it still is far from creating an "intelligent autonomous" agent.
Here is my question, in theory, what parameters would you use create an AI environment? Possibly a brain environment? Possibly multiple self contained life organisms that have their own "brain" or life structures.
I would like a create a spin on the game of life simulation. What if you have a 64x64 game of life grid. But instead of one grid, you might have N number of grids. The N number of grids are your "life force" If all of the game of life entities die in a particular grid then that entire grid dies. A group of "grids" makes up a life form.
I don't have an immediate goal. First, I want to simulate an environment and visualize what is going on in the environment with OpenGL and see if there are any interesting properties to the environment. I then want to add "scarce resources" and see if the AI environment can manage resources adequately.
Since you said "in theory", that implies you are interested in reading a lot of academic papers on the subject, because I think there's plenty of theoretical work out there, usually supported by proof-of-concept experiments.
I took a class on this 3 years ago, so my knowledge is both introductory and out-of-date, but try searching for something like "neural network language evolution" on Google Scholar*. The simulations in those papers should give you some ideas of what other researchers have tried. Then, a good place to start is to replicate one of the experiments that you find interesting.
Disclaimer: I had to do just that for the class, and it sucked. I decided that I preferred working programs to theoretical experiments. But you said "in theory" so this might be the kind of thing you really like.
*Sorry, I can't remember the exact papers we read.