Got started with using code.google.com project hosting for ngwallpaper project. When you execute this python script, it fetches the wallpaper of the day from National Geographic site and sets it up as your Desktop Wallpaper.
Currently works on Windows platform with python windows extensions installs ( it uses SETDESTOPWALLPAPER attribute of a particular windows call).
Desirable features for ngwallpaper is 1) Platform agnostic (Windows, Linux, Mac) 2)More structure code. One present is just a start version 3)the program as a service or scheduled service which will run a particular time everyday. 4) Fix the windows set wallpaper issue where after few hours the wallpaper gets reset.
scrapbook extention for firefox
At times I read the Netspeak column in The Hindu. A particular weekly issue, mentioned about Offline Browsing Tool for Firefox called Scrapbook. This solved my much desired feature of having pages offline in firefox. Internet explorer provides this feature wherein it cache the visited pages and when the user goes offline and try to browse, it loads up the page from the internal cache. This is a useful feature when Internet connection is costly and slow, and u want to visit the pages you browsed at your work place, to be browsed( usually good long articles to read) at home without connecting to internet.
Scrapbook extension for firefox, solves this limitation. Its sits a title bar, and on page am on, I can scrap it, so that will be available offline.
In the offline reading mode, I can add comments, highlight text, and search the pages as well.
Quite a good firefox extension.
Scrapbook extension for firefox, solves this limitation. Its sits a title bar, and on page am on, I can scrap it, so that will be available offline.
In the offline reading mode, I can add comments, highlight text, and search the pages as well.
Quite a good firefox extension.
Using Windows Modem Troubleshooter
I connect to internet using my laptop running Windows XP from home, using the BSNL dial-up connection over the Internal Conexant D110 MDC V.92 Modem. Past few days, I was unable to dial up, the dial up died with in few seconds, and I could not figure out the reason as well.
Followed the Windows Troubleshooter tool today, which asked me to check for correct protocol set at Internet Connection settings (TCP/IP), and suggested a step called "Show Terminal Window" in the Security tab of the Network Connections settings for the Modem Connection.
When Enabled and Dialed again, I am able to connect now. The problem is solved, but I am not yet to bottom of it.
Let me enjoy it now.
Followed the Windows Troubleshooter tool today, which asked me to check for correct protocol set at Internet Connection settings (TCP/IP), and suggested a step called "Show Terminal Window" in the Security tab of the Network Connections settings for the Modem Connection.
When Enabled and Dialed again, I am able to connect now. The problem is solved, but I am not yet to bottom of it.
Let me enjoy it now.
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Subscribe Lisp Notes -2. On REPL
Subsequent to Lisp Notes 1;these are my notes for the Chapter 2 "Lather, Rinse, Repeat: A Tour of the REPL" in the Practical Common Lisp Book
* Lisp provides an interactive read-eval-print loop.
* Lisp can be used in Automated Theorem proving, planning and scheduling and computer vision. Large scale battlefield simulation, automated planning and natural language interfaces.
* Help in EMACS, Press CTRL Key, type h, release CTRL key and press t. This key combination called key-chord is represented like this. C-t h
* Info system is available by C-h i
* C-h ? brings complete list.
* C-h k lets us type any key combination and lets us know the command which will be invoked.
* C-h w lets us enter the command and returns the corresponding key combination.
Crucial bit of emacs terminology is a Buffer. While working with EMACS, each file you edit will be represented by a different buffer, only one of which is current in any way.
Buffers:
* C-x b is command to switch to buffer.
Some key combinations may be available for switches to certain buffer.
For e.g. to switch to lisp source file.
* C-c C-z switch to buffer where you interact with lisp ( REPL)
CL-USER>
This is the lisp command prompt. Lisp reads the lines of lisp expressions evaluates them according to the rules of lisp and prints the result.
The endless cycle of reading, evaluating and printing is why it is called read-eval-print loop or REPL for short.
Its also refered to as top level, top level listner, lisp listner.
From REPL we can:
- Define or redefine variables, functions, classes and methods.
- Evaluate any lisp expression.
- Load files containing lisp source code or compiled code.
- compile other files or individual functions.
- Enter Debugger.
- Step through the code.
- Inspect the state of the individual lisp command.
CL-USER>10
10
R - Reads "10" and coverts to lisp object "10"
E - Evaluates to itself.
P - Prints "10"
CL-USER>(+ 2 3)
+ symbol is coverted to + function which takes 2 and 3 as parameters.
CL-USER>"hello,world"
"hello,world"
That was a "hello,world" value.
Format function:
Format takes a variable number of arguments, but the only two required to send the output a string.
CL-USER>(format t "hello,world")
"hello,world"
NIL
* t sends the output to stdout.
* NIL is the return value of the function.
CL-USER>(defun hello-world() (format t "hello,world"))
HELLO-WORLD
CL-USER>(hello-world)
hello,world
NIL
Saving the file:
* C-x C-f type the file name with extension as .lisp or .cl
* Inside the SLIME mode, C-c C-q invokes the command slime-close-parens-at-point which will insert as many closing paranthesis as necessary to match all the open paranthesis.
* To get the source file to lisp enviroment:
* C-c C-c ( slime-compile-defun)
Or switch the REPL Buffer:
* C-c C-z (directly from SLIME to REPL)
Or
* C-x b and all the buffer.
Make some changes and type again.
(defun hello-world()
(format t "Hello,World!"))
* C-c C-c
Or
* C-c C-z
(hello,world)
Hello,World!
NIL
Save the changes to hello.lisp by typing C-x C-s in EMACS which invokes (save-buffer)
Exit SLIME, which is in REPL type ',' -a comma.
Invoke again:
M-x slime
CL-USER>(hello-world)
Will got get invoke because REPL is not aware and it will put you in the debugger mode. pressing 'q' will exit the debugger.
CL-USER>(hello-world)
;Evaluation aborted
CL-USER>
Letting the REPL Know.
1) C-x b hello.lisp and then compling using C-c C-c
2) Load the whole file:
(load "hello.lisp")
; Loading file
T
T- means loaded correctly.
FASL - Fast Load file
(load(compile-file("hello.lisp"))
From the SLIME enviroment itself, the following features are available:
* C-c C-l (slime-load-file)
* C-c C-k to compile and load the file represented by the current buffer.
* Lisp provides an interactive read-eval-print loop.
* Lisp can be used in Automated Theorem proving, planning and scheduling and computer vision. Large scale battlefield simulation, automated planning and natural language interfaces.
* Help in EMACS, Press CTRL Key, type h, release CTRL key and press t. This key combination called key-chord is represented like this. C-t h
* Info system is available by C-h i
* C-h ? brings complete list.
* C-h k lets us type any key combination and lets us know the command which will be invoked.
* C-h w lets us enter the command and returns the corresponding key combination.
Crucial bit of emacs terminology is a Buffer. While working with EMACS, each file you edit will be represented by a different buffer, only one of which is current in any way.
Buffers:
* C-x b is command to switch to buffer.
Some key combinations may be available for switches to certain buffer.
For e.g. to switch to lisp source file.
* C-c C-z switch to buffer where you interact with lisp ( REPL)
CL-USER>
This is the lisp command prompt. Lisp reads the lines of lisp expressions evaluates them according to the rules of lisp and prints the result.
The endless cycle of reading, evaluating and printing is why it is called read-eval-print loop or REPL for short.
Its also refered to as top level, top level listner, lisp listner.
From REPL we can:
- Define or redefine variables, functions, classes and methods.
- Evaluate any lisp expression.
- Load files containing lisp source code or compiled code.
- compile other files or individual functions.
- Enter Debugger.
- Step through the code.
- Inspect the state of the individual lisp command.
CL-USER>10
10
R - Reads "10" and coverts to lisp object "10"
E - Evaluates to itself.
P - Prints "10"
CL-USER>(+ 2 3)
+ symbol is coverted to + function which takes 2 and 3 as parameters.
CL-USER>"hello,world"
"hello,world"
That was a "hello,world" value.
Format function:
Format takes a variable number of arguments, but the only two required to send the output a string.
CL-USER>(format t "hello,world")
"hello,world"
NIL
* t sends the output to stdout.
* NIL is the return value of the function.
CL-USER>(defun hello-world() (format t "hello,world"))
HELLO-WORLD
CL-USER>(hello-world)
hello,world
NIL
Saving the file:
* C-x C-f type the file name with extension as .lisp or .cl
* Inside the SLIME mode, C-c C-q invokes the command slime-close-parens-at-point which will insert as many closing paranthesis as necessary to match all the open paranthesis.
* To get the source file to lisp enviroment:
* C-c C-c ( slime-compile-defun)
Or switch the REPL Buffer:
* C-c C-z (directly from SLIME to REPL)
Or
* C-x b and all the buffer.
Make some changes and type again.
(defun hello-world()
(format t "Hello,World!"))
* C-c C-c
Or
* C-c C-z
(hello,world)
Hello,World!
NIL
Save the changes to hello.lisp by typing C-x C-s in EMACS which invokes (save-buffer)
Exit SLIME, which is in REPL type ',' -a comma.
Invoke again:
M-x slime
CL-USER>(hello-world)
Will got get invoke because REPL is not aware and it will put you in the debugger mode. pressing 'q' will exit the debugger.
CL-USER>(hello-world)
;Evaluation aborted
CL-USER>
Letting the REPL Know.
1) C-x b hello.lisp and then compling using C-c C-c
2) Load the whole file:
(load "hello.lisp")
; Loading file
T
T- means loaded correctly.
FASL - Fast Load file
(load(compile-file("hello.lisp"))
From the SLIME enviroment itself, the following features are available:
* C-c C-l (slime-load-file)
* C-c C-k to compile and load the file represented by the current buffer.
AI Class Notes - 2
Notes from AI Class
Any solution goes from (0,0) state to (goal state).
- Reasoning.
- AI stands for positive thinking.*
- Chess playing, alternative choices, multiple choices.
- Humans can think simultaneously different things.
- Machine intelligence revolution.
What is A.I?
Artificial: Produced by art. Not genuine or natural, not pertaining to
the sense of matter.
Synonymous: Synthetic, fictitious, pretend, simulated, spurious,
unnatural.
Antonyms: Actual, genuine, honest, real, natural, truthful and
unaffected.
Intelligence: Endowed with a faculty of reasoning, quick of mind, well
informed and communicative.
* Marvin Minsky's initial writings provide a very good introduction.
* Do plants think?
Objectives of AI?
Primary Goal: To Make the computers smart. (CS)
Secondary Goal: To understand the nature of human intelligence.(psychologist)
Entreprenuers" To make machines more useful and economical (eventually
replace humans)
Japanese tried to create machines that will help humans when they fail.
* Fuzzy Logic in washing machines.
* Inacessible to humans? Machines with intelligence needed.
Normal missiles will be shot, but missiles with intelligence have chances of
hitting the target.
Virtual reality system help in designing the A.I system.
What is an A.I problem today may not be same 20 years down.
Definition:
AI is the study of how to make computers do things at which at the moment,
human beings are better.
(2) AI is the study of mental faculties through the use of computational
methods.
Questions:
1) What are our own underlying assumptions about intelligence?
2) At what level of details are we going to model and mimic intelligence?
3) What kind of tools and techniques we have at present for study of AI?
4) How will we know that we have succeeded in building an intelligent system?
5) What computers can and cannot do?
6) Can machines think?
7) Can a machine fool a human being into thinking that (s)he chatting with
another human being?
Computational methods:
- Number crunching.
- Huristic programming.
- Automatic programming.
8) Why we think that machines cannot?
9) For that matter, do humans think? and How do we think?
Chart:
A Modern AI Lab.
* Reasoning about objects.
* Programming [ lisp, prolog ]
* Architecture [ fifth generation, parallel]
* Design and Analysis Systems [ knowledge based expert AI
systems, decision support systems]
* Speech and Language
* Learning.
* Vision and Speech
* Robotics
Intelligent Behaviour:
Use of huristics: using some rules of thumb for deciding any of the several
alternative choices.
Huristic:
Best first search, breadth first search and depth first search.
- Huristic should help us in dramatically reducing the search for solution in
the large problem spaces.
- No guarantee of optimal solution.
Two approaches to Designing AI Based Computers.
Top-Down Approach
A.I. Application
*
* Predicate Logic
* Frames
* Semantic Nets.
* Knowledge Representation
A.I. Languages
* Lisp
* Prolog
* Smalltalk
Bottom Up Approach
Computing Model
* Control Flow
* Data Structure
* Data
A.I Architecture
Assignment:
To Reach IISc from your Home.
Parameters: Vehicle, Mode of transport, Map.
Time and shortest path constraints.
Between own vehicle and public transport, which one is preferable?
Knowledge:
Search Engine, algorithm is intelligence and database is knowledge.
Human information processing.
All knowledge structures are Tree Structures.
Reasoing:
Reasoning refers to the different kinds of activities:
- Drawing conclusions from different set of facts.
- Diagnosing possible cause of conditions.
- Making assumption about a situation.
- Analysis of organizing facts and data about problem
- Solving a problem or a puzzle.
- Arguing with a person with a particular point of view.
Classification of Reasoning activities:
Based on Degree of perception
* Deductive reasoning.
* Inductive reasoning.
* Default reasoning.
Based on level of reasoning.
* Problem level reasoning.
* Meta level reasning.
Based on generality
* Casual
* Common Sense.
* von monotonic
* Plausible
* special
* Temporal
* Reasoning systems involve the representation of information and word.
Any solution goes from (0,0) state to (goal state).
- Reasoning.
- AI stands for positive thinking.*
- Chess playing, alternative choices, multiple choices.
- Humans can think simultaneously different things.
- Machine intelligence revolution.
What is A.I?
Artificial: Produced by art. Not genuine or natural, not pertaining to
the sense of matter.
Synonymous: Synthetic, fictitious, pretend, simulated, spurious,
unnatural.
Antonyms: Actual, genuine, honest, real, natural, truthful and
unaffected.
Intelligence: Endowed with a faculty of reasoning, quick of mind, well
informed and communicative.
* Marvin Minsky's initial writings provide a very good introduction.
* Do plants think?
Objectives of AI?
Primary Goal: To Make the computers smart. (CS)
Secondary Goal: To understand the nature of human intelligence.(psychologist)
Entreprenuers" To make machines more useful and economical (eventually
replace humans)
Japanese tried to create machines that will help humans when they fail.
* Fuzzy Logic in washing machines.
* Inacessible to humans? Machines with intelligence needed.
Normal missiles will be shot, but missiles with intelligence have chances of
hitting the target.
Virtual reality system help in designing the A.I system.
What is an A.I problem today may not be same 20 years down.
Definition:
AI is the study of how to make computers do things at which at the moment,
human beings are better.
(2) AI is the study of mental faculties through the use of computational
methods.
Questions:
1) What are our own underlying assumptions about intelligence?
2) At what level of details are we going to model and mimic intelligence?
3) What kind of tools and techniques we have at present for study of AI?
4) How will we know that we have succeeded in building an intelligent system?
5) What computers can and cannot do?
6) Can machines think?
7) Can a machine fool a human being into thinking that (s)he chatting with
another human being?
Computational methods:
- Number crunching.
- Huristic programming.
- Automatic programming.
8) Why we think that machines cannot?
9) For that matter, do humans think? and How do we think?
Chart:
A Modern AI Lab.
* Reasoning about objects.
* Programming [ lisp, prolog ]
* Architecture [ fifth generation, parallel]
* Design and Analysis Systems [ knowledge based expert AI
systems, decision support systems]
* Speech and Language
* Learning.
* Vision and Speech
* Robotics
Intelligent Behaviour:
Use of huristics: using some rules of thumb for deciding any of the several
alternative choices.
Huristic:
Best first search, breadth first search and depth first search.
- Huristic should help us in dramatically reducing the search for solution in
the large problem spaces.
- No guarantee of optimal solution.
Two approaches to Designing AI Based Computers.
Top-Down Approach
A.I. Application
*
* Predicate Logic
* Frames
* Semantic Nets.
* Knowledge Representation
A.I. Languages
* Lisp
* Prolog
* Smalltalk
Bottom Up Approach
Computing Model
* Control Flow
* Data Structure
* Data
A.I Architecture
Assignment:
To Reach IISc from your Home.
Parameters: Vehicle, Mode of transport, Map.
Time and shortest path constraints.
Between own vehicle and public transport, which one is preferable?
Knowledge:
Search Engine, algorithm is intelligence and database is knowledge.
Human information processing.
All knowledge structures are Tree Structures.
Reasoing:
Reasoning refers to the different kinds of activities:
- Drawing conclusions from different set of facts.
- Diagnosing possible cause of conditions.
- Making assumption about a situation.
- Analysis of organizing facts and data about problem
- Solving a problem or a puzzle.
- Arguing with a person with a particular point of view.
Classification of Reasoning activities:
Based on Degree of perception
* Deductive reasoning.
* Inductive reasoning.
* Default reasoning.
Based on level of reasoning.
* Problem level reasoning.
* Meta level reasning.
Based on generality
* Casual
* Common Sense.
* von monotonic
* Plausible
* special
* Temporal
* Reasoning systems involve the representation of information and word.
Marvin Minsky on AI
Found this post at /. which references the podcasts of Marvin Minsky on AI, but I am unable to locate them.
Found two comments which are very insightful and written by people whose thoughts were along the same direction as our discussions in proficience course over the weekends.
Quotes:
While much of the "traditional AI" hype could be considered dead, robotics is continuing to advance, and much symbolic AI research has evolved into data-driven statistical techniques. So while the top-down ideas that the older AI researches didn't pan out yet, bottom-up techniques will still help close the gap.
Also, you have to remember that AI is pretty much defined as "the stuff we don't know how to do yet". Once we know how to do it, then people stop calling it AI, and then wonder "why can't we do AI?" Machine vision is doing everything from factory inspections to face recognition, we have voice recognition on our cell phones, and context-sensitive web search is common. All those things were considered AI not long ago. Calculators were once even called mechanical brains. by SnowZero.
----
Personally I don't think it's quantum computers that will be the breakthrough, but simply a different architecture for conventional computers. Let me go on a little tangent here.
Now that we've reached the limits of the Von Neumann architecture [wikipedia.org], we're starting to see a new wave of innovation in CPU design. The Cell is part of that, but also the stuff ATI [amd.com] and NVIDIA [nvidia.com] are doing is also very interesting. Instead of one monolithic processor connected to a giant memory through a tiny bottleneck, processors of the future will be a grid of processing elements interleaved with embedded memory in a network structure. Almost like a Beowulf cluster on a chip.
People are worried about how conventional programs will scale to these new architectures, but I believe they won't have to. Code monkeys won't be writing code to spawn thousands of cooperating threads to run the logic of a C++ application faster. Instead, PhDs will write specialized libraries to leverage all that parallel processing power for specific algorithms. You'll have a raytracing library, an image processing library, an FFT library, etc. These specialized libraries will have no problem sponging up all the excess computing resources, while your traditional software continues to run on just two or three traditional cores.
Back on the subject of AI, my theory is that these highly parallel architectures will be much more suited to simulating the highly parallel human brain. They will excel at the kinds pattern matching tasks our brains eat for breakfast. Computer vision, speech recognition, natural language processing; all of these will be highly amenable to parallelization. And it is these applications which will eventually prove the worth of non-traditional architectures like Intel's 80-core chip. It may still be a long time before the sentient computer is unveiled, but I think we will soon finally start seeing real-world AI applications like decent automated translation, image labeling, and usable stereo vision for robot navigation. Furthermore, I predict that Google will be on the forefront of this new AI revolution, developing new algorithms to truly understand web content to reject spam and improve rankings.
by Modeless
Found two comments which are very insightful and written by people whose thoughts were along the same direction as our discussions in proficience course over the weekends.
Quotes:
While much of the "traditional AI" hype could be considered dead, robotics is continuing to advance, and much symbolic AI research has evolved into data-driven statistical techniques. So while the top-down ideas that the older AI researches didn't pan out yet, bottom-up techniques will still help close the gap.
Also, you have to remember that AI is pretty much defined as "the stuff we don't know how to do yet". Once we know how to do it, then people stop calling it AI, and then wonder "why can't we do AI?" Machine vision is doing everything from factory inspections to face recognition, we have voice recognition on our cell phones, and context-sensitive web search is common. All those things were considered AI not long ago. Calculators were once even called mechanical brains. by SnowZero.
----
Personally I don't think it's quantum computers that will be the breakthrough, but simply a different architecture for conventional computers. Let me go on a little tangent here.
Now that we've reached the limits of the Von Neumann architecture [wikipedia.org], we're starting to see a new wave of innovation in CPU design. The Cell is part of that, but also the stuff ATI [amd.com] and NVIDIA [nvidia.com] are doing is also very interesting. Instead of one monolithic processor connected to a giant memory through a tiny bottleneck, processors of the future will be a grid of processing elements interleaved with embedded memory in a network structure. Almost like a Beowulf cluster on a chip.
People are worried about how conventional programs will scale to these new architectures, but I believe they won't have to. Code monkeys won't be writing code to spawn thousands of cooperating threads to run the logic of a C++ application faster. Instead, PhDs will write specialized libraries to leverage all that parallel processing power for specific algorithms. You'll have a raytracing library, an image processing library, an FFT library, etc. These specialized libraries will have no problem sponging up all the excess computing resources, while your traditional software continues to run on just two or three traditional cores.
Back on the subject of AI, my theory is that these highly parallel architectures will be much more suited to simulating the highly parallel human brain. They will excel at the kinds pattern matching tasks our brains eat for breakfast. Computer vision, speech recognition, natural language processing; all of these will be highly amenable to parallelization. And it is these applications which will eventually prove the worth of non-traditional architectures like Intel's 80-core chip. It may still be a long time before the sentient computer is unveiled, but I think we will soon finally start seeing real-world AI applications like decent automated translation, image labeling, and usable stereo vision for robot navigation. Furthermore, I predict that Google will be on the forefront of this new AI revolution, developing new algorithms to truly understand web content to reject spam and improve rankings.
by Modeless
Lisp Notes - 1
The nearest thing Common Lisp has to a motto is the koan-like description, "the programmable programming language." While cryptic,that description gets at the root of the biggest advantage Common Lisp still has over other languages.
Ideas first introduced in Lisp include the if/then/else construct,recursive function calls, dynamic memory allocation, garbage collection,first-class functions, lexical closures, interactive programming, incremental compilation and dynamic typing.
Ideas first introduced in Lisp include the if/then/else construct,recursive function calls, dynamic memory allocation, garbage collection,first-class functions, lexical closures, interactive programming, incremental compilation and dynamic typing.
vi notes
http://www.vim.org/tips/tip.php?tip_id=1465
The above vim article has some notes to accompany to tutorial, "An
Introdution to display editing using vi" written by Bill Joy and Mark
Horton.Might be helpful to get started with editing using vim or to read the tutorial.
The above vim article has some notes to accompany to tutorial, "An
Introdution to display editing using vi" written by Bill Joy and Mark
Horton.Might be helpful to get started with editing using vim or to read the tutorial.
Using cdrecord HOWTO
When any GUI based CD Burning utility is not present in your Linux box,
you might end up using cdrecord. This is a two step HOW TO to use
cdrecord to burn an iso image.
will give you the device to which your CDRW is attached.
It will be in the format:
0,0,0
0,0,1
etc
Mine was 0,0,0
Thats it and its working for me now.
you might end up using cdrecord. This is a two step HOW TO to use
cdrecord to burn an iso image.
1) /usr/bin/cdrecord --scanbus
will give you the device to which your CDRW is attached.
It will be in the format:
0,0,0
0,0,1
etc
Mine was 0,0,0
2)/usr/bin/cdrecord -v gracetime=2 dev= speed=32 -dao driveropts=burnfree -eject -data
Thats it and its working for me now.
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