PHP-ML – Machine Learning library for PHP

PHP-ML is a machine learning library for PHP.  Given, PHP is probably not the best choice when it comes to machine learning, but sometimes one is limited in technology stack choices, so it’s good have options like this one.

Fresh approach to Machine Learning in PHP. Algorithms, Cross Validation, Neural Network, Preprocessing, Feature Extraction and much more in one library.

Here’s the documentation for more details.

Grakn and Graql – a database for AI

From the website:

Grakn is a distributed hyper-relational database for knowledge-oriented systems. Grakn enables machines to manage complex data that serves as a knowledge base for cognitive/AI systems.

Graql is Grakn’s reasoning (through OLTP) and analytics (through OLAP) query language. Graql is a much higher level abstraction over traditional query language – SQL, NoSQL, or Graphs.

Learning Machine Learning and NLP from 187 Quora Questions

Machine Learning is a hot subject these days.  And as with any other hot subject, there is a multitude of books, online course, videos, and guides available all over the web.  I think that this curated list of  187 Quora questions is an interesting way of approaching the subject.

While Quora has FAQ pages for many topics (e.g. FAQ for Machine Learning), they are far from comprehensive. In this post, I’ve tried to provide a more thorough Quora FAQ for several machine learning and NLP topics.

Quora doesn’t have much structure, and many questions you find on the site are either poorly answered or extremely specific. I’ve tried to include only popular questions that have good answers on general interest topics.

Trust in Automation

Trust in Automation is by far the best thing I’ve read on the subjects of artificial intelligence and machine learning, and their affects on human society.  There are plenty of links and quotes that make you think and want you to learn more … until you don’t.  It’s not depressing, but it is quite concerning.  Here are a couple of quotes from the article (some of them are quotes of other people), which I liked:

As the cost of labor goes up and the cost of machinery goes down, at some point, it’ll be cheaper to use machines than people. With the increase in productivity, the GDP goes up, but so does unemployment. What do you do? … The best way is to reduce the time a certain portion of the population spends living, and then find ways to keep them busy.

—Jingfang Hao, Folding Beijing (2014)

Also this one:

If you think discrimination is bad today, just wait until the machines take over. They will discriminate based on the the shade of your iris, the shape of your brow, the size of a tatoo, or any arbitrary collection of low-level traits whose presence triggers a subtle bias.

The Definitive Guide to Natural Language Processing

The Definitive Guide to Natural Language Processing” is an easy to follow article on what a challanging task it is for machines to understand human language.  There’s also this cool video of two bots talking to each other.

Google Reader recommends

Imagine my surprise when I looked at “Top Recommendations” area of my Google Reader today and found … my own blog over there.

Yes, I know that these recommendations are based on the feeds that I read.  But still!  Is it the time to celebrate the recommendations technology, which recommended me to me over a gadzillion of other blogs?  Or maybe this is a day of Ultimate Technological Silliness, when Google, a search company that forgets nothing, somehow arrived to the conclusion that I might not be reading my own blog?  These questions remind me of a “half-empty or half-full glass of water“.  I guess a lot depends on the personal perspective…

Humans in image recognition

It looks like humans aren’t all that useless when it comes to technology.  There are still a few areas that we do better than machines.  Image recognition is one of them.  TechCrunch runs the story about one company that seems to be using humans in image recognition process.  Comments to that story also mention Google doing the same.

To me it feels like a problem with timing.  There is a need to tag and search a whole lot of images.  But there is no good automated solution available.  So we are falling back on humans.  It’s easy to come up with a few other areas, in which there is a need today for solutions which won’t even be here tomorrow.  Technology needs help, I guess.

Getting ready for Artificial Intelligence … again

Being a lazy bastard and a horrible student such as I am, there is no surprise in that I failed so many courses in the college. I am currently standing at 3 courses left for my Bachelor’s degree. One of these three courses – Artificial Intelligence – seems to be the toughest target due to a number of arguments I had with a teacher. And there is no way there will be any other teacher for this course in the near future, so I have to prepare myself real good. It is about time that I start with my morale. :)

Not to scare myself away, I will start small. I will just put three links in this post to other web resources, that I will visit during my preliminary preparations. Here they come:

  • Website of MIT Artificial Intelligence Laboratory is here. Maybe I will anything entertaining to read in the number of publicationis and researches there that will interest me in the subject.
  • AI on the Web – is a collection of useful links to many AI resources. I am using at as a cheat, since I decided to have only 3 links in this post. :)
  • A Prolog Introduction for Hackers – another useful article at kuro5hin. Last time I took A.I., a substential amount of time was dedicated to Prolog studying without any useful materials provided, so this bookmark should help.

That’s it. This post is my greatest advance in studing A.I. during the last two years. :)