Wednesday, October 25, 2006
New version of my NLP toolkit
I have done a fair amount of work in the last year on my KBtextmaster product (although not in the last 5 months due to a consulting contract). I hope to release version 4 next spring. For previous versions I did my R&D in Common Lisp, then converted to Java (bigger market!). While I may eventually do a Java port, I decided that I would rather stick with Common Lisp and go for maximum features and performance for the next release.
I did just formed a VAR relationship with Franz to use their Allegro Common Lisp for development and deployment. Allegro has support for compiling to a library that is accessible from Java applications, so that may be be OK for Java customers. The high runtime performance of Allegro is amazing.
I did just formed a VAR relationship with Franz to use their Allegro Common Lisp for development and deployment. Allegro has support for compiling to a library that is accessible from Java applications, so that may be be OK for Java customers. The high runtime performance of Allegro is amazing.
Wednesday, October 04, 2006
Software environments for working on AI projects
In the new global economy of driving production and service costs towards zero, it makes a lot of sense for computer scientists to learn specialized skills to differentiate themselves in the marketplace. Since you are reading this blog I assume that you are interested in learning more about AI so I thought that I would list the AI development environments that I have found to be particularly useful - and a lot of them are free.
Classic AI Languages
Although not strictly required for work in AI, a few AI oriented languages have proven especially useful in the past: Lisp, Scheme, and Prolog. Scheme is a great language but suffers from an "embarrassment of riches": there are almost too many fine implementations available to choose from. That said, I would recommend the excellent and free DrScheme and MzScheme as a very good place to start because it is supported by a repository of useful libraries that are very easy to install. If you want to mix logic programming with Scheme then the following book (with examples that work with DrScheme) is recommended: The Reasoned Schemer
If you want to use Common Lisp (which is what I use for most of my AI development consulting) there are two commercial products that are very good and have free (non-commercial only!) versions: Franz Lisp and LispWorks. There is no need however to stick just with commercial offerings: SBCL (MIT license) and CLisp (GPL license) are two good choices among many.
If you want to use Prolog, the open source (LGPL) SWI-prolog and the commercial Amzi Prolog are both excellent choices and have lots of third party libraries.
Scripting Languages
I have found two scripting scripting languages to be particularly useful for AI projects: Ruby and Python. Python has more third party libraries and projects for AI but I personally enjoy developing in Ruby.
Pick an environment and stick with it
Believe it or not, I tend to follow this advice myself: I tend to use one language for a year or so, and then switch (usually because of customer preference or the availability of a great library written in one specific language). It pays to take the time to master one language and environment, then use that environment a lot.
So my advice is to spend just a few hours each with a few of my suggestions in order to pick one to learn really well. Once you pick a language stick with it until you master it.
Classic AI Languages
Although not strictly required for work in AI, a few AI oriented languages have proven especially useful in the past: Lisp, Scheme, and Prolog. Scheme is a great language but suffers from an "embarrassment of riches": there are almost too many fine implementations available to choose from. That said, I would recommend the excellent and free DrScheme and MzScheme as a very good place to start because it is supported by a repository of useful libraries that are very easy to install. If you want to mix logic programming with Scheme then the following book (with examples that work with DrScheme) is recommended: The Reasoned Schemer
If you want to use Common Lisp (which is what I use for most of my AI development consulting) there are two commercial products that are very good and have free (non-commercial only!) versions: Franz Lisp and LispWorks. There is no need however to stick just with commercial offerings: SBCL (MIT license) and CLisp (GPL license) are two good choices among many.
If you want to use Prolog, the open source (LGPL) SWI-prolog and the commercial Amzi Prolog are both excellent choices and have lots of third party libraries.
Scripting Languages
I have found two scripting scripting languages to be particularly useful for AI projects: Ruby and Python. Python has more third party libraries and projects for AI but I personally enjoy developing in Ruby.
Pick an environment and stick with it
Believe it or not, I tend to follow this advice myself: I tend to use one language for a year or so, and then switch (usually because of customer preference or the availability of a great library written in one specific language). It pays to take the time to master one language and environment, then use that environment a lot.
So my advice is to spend just a few hours each with a few of my suggestions in order to pick one to learn really well. Once you pick a language stick with it until you master it.
Labels: Lisp, Prolog, Ruby, Scheme
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