I am a consultant and the author of 24 books on artificial intelligence, machine learning, and the semantic web. My favorite languages are Java, Haskell, Python, Common Lisp, and Ruby.
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Great (and free) Lisp programming environment
It is good to know more than one programming language: Common Lisp is one of my favorite languages and I often encourage other programmers to learn it. Peter Seibel (author of " Practical Common Lisp" and well known in the Lisp community) has put together a complete programming environment LispBox that integrates a free Lisp development system (CLisp, OpenMcl, or AllegroLisp) for Windows, OS X, and Linux. He included Emacs, Slime, and ASDF (a package system for automatically loading systems from the web).
Neat stuff and a huge time saver if you want to set up your own Lisp development system.
Here are some of my of my recent notes that might save you some time, or teach you a new trick. I have had good results using the py4cl library if I wrap API calls to TensorFlow or spaCy in a short Python library that calls Python libraries and returns results in simple types like strings and dictionaries. I just committed a complete example (Python library and Common Lisp client code) to the public repo for my book Loving Common Lisp, or the Savvy Programmer's Secret Weapon that will be added to the next edition of my book. Here is a link to the subdirectory with this new example in my repo: https://github.com/mark-watson/loving-common-lisp/tree/master/src/spacy I frequently make standalone executable programs using SBCL and I just noticed a great tip from Zach Beane for compressing the size of standalone executables. Start with rebuilding SBCL from source to add the compression option; get the source code and: ./make.sh --with-sb-thread --with-sb-core-compression sh in
My wife Carol and I have been practicing social distancing and wearing masks for shopping for over 5 months now. Welcome to the new normal and a crazy world in which entertaining and seeing friends is done by meeting in people's yards and everyone bringing their own "meal in a bag." I enjoy writing so I have been updating my recent books, starting with Loving Common Lisp, or the Savvy Programmer's Secret Weapon and A Lisp Programmer Living in Python-Land: The Hy Programming Language . These are free to read online and licensed with Creative Commons Share and Share Alike, No Commercial Reuse, so you can also find copies on the web (hopefully up to date copies!). Last month I started a much larger project: I have not updated my book Practical Artificial Intelligence Programming With Java since the fourth edition was published in 2013. I have discarded a lot of older material like exert systems, and have three new chapters on the semantic web and also a new chapter on
I retired this spring after working on artificial intelligence projects since the 1980s. Freedom from having to work on large projects for other people and companies is liberating and frees up time for thinking about new ideas. Currently I am most interested in deep learning models for generating and evaluating recipes - for now I am using a GAN model (which I am calling RecipeGAN). When I managed a deep learning team at Capital One, I used GANs to synthesize data. During a Saturday morning quiet-time hacking sprint the first month at my new job, I had the idea to take an example program SimpleGAN that generated MINST digits and instead generate numeric spreadsheet data (using the Wisconsin Cancer Data Set that I had previously used in my books as example machine learning data). I was really surprised how well this worked: I could generate fake Wisconsin cancer data, train a classification model on the fake data, and get classification prediction accuracy on real data samples that wa