Quantum Fog Comes Out of the Closet

Quantum Fog, my old Mac application for doing calculations with quantum Bayesian networks, is now open-source (it’s finally out of the closet). Check it out at GitHub.

Quantum Fog was originally written in C++, but we plan to rewrite most of it in Python

By the way, I highly recommend the GitHub website if you need to collaborate with a group of people on the writing of a computer app or a website or an arXiv paper, or many other possibilities. I use GitHub in conjunction with a Windows app called TortoiseGit, which I also love and highly recommend.

I just want to MOOC them All

( I conceived this poster in response to a friendly argument with CapitalistImperialistPig (that’s his nom de plume). You are a better pig than I am Gunga Din)

A Quantum Computer is the Ultimate Group Theory Box

In previous blog posts, I mentioned my recent interest in using group theory in quantum computing algorithms. See for example,

• I’m a Born-Again Group-Pest
• Symmetry Makes The World Go Round; Symmetry, It’s What’s For Dinner

Group Theory and quantum mechanics are like co-joined twins. It’s hard to tell where one begins and the other ends. So I was somewhat ashamed that I had used so little Group Theory in my quantum computing programming. I’ve been trying to overcome that weakness of mine in the last year and I’m beginning to see the results. I am all stoked up today because this week I filed my second US patent applying Group Theory (GT) to quantum computing (QC). This means our company http://www.artiste-qb.net now has 12 US patents on QC programming (6 granted, 6 pending)

I can’t say much about my 2 QC-GT patents for now because loose lips sink ships, but I did prepare some pictures to convey my enthusiasm for QC-GT. In a previous blog post, I waxed poetic about the connections between the movie “2001, A Space Odyssey” and quantum computing. This time, I adapted a “2001- a Space Odyssey” T-shirt that I think is really cool so that the black monolith has some hieroglyphics on it dealing with GT. Ta-tan, here are my pictures:


Very little of the art work in these two images was originally created by me. My images were mostly based on the following two previous images

1. T-shirt image from this page of the website of tshirtbordello.com.
2. Image from Wikipedia article on Point Groups in Three Dimensions. There are 7 infinite sequences of point groups in 3 dimensions with cylindrical (uniaxial) symmetry. The groups in each sequence are indexed by . The -th group has -fold rotational symmetry about the axis of symmetry. This figure from Wikipedia shows those 7 sequences for .
   

Google Open-sources TensorFlow (A Fakesian Networks Software Library). Microsoft, Tear Down This Infer.NET Wall

Check out

• TensorFlow, Google’s Open Source AI, Signals Big Changes in Hardware Too by Cade Metz (Wired, 11/10/15)
• http://nuit-blanche.blogspot.com/2015/11/tensorflow-large-scale-machine-learning.html

On Nov. 10, Google announced to much fanfare that it was open-sourcing TensorFlow, their software library for “Large-Scale Machine Learning on Heterogeneous Distributed Systems”. At this point in time, I know very little about TensorFlow, but I can already see that it is not very Bayesian Networky. Me being such an avid B Net fan, I can’t deny that I was a little disappointed by how little B net stuff it contains. To me, TensorFlow is Fakesian Networks instead of Bayesian Networks 🙂

In my opinion, TensorFlow has VERY LITTLE in common, except for the name, with what quantum information theorists call “quantum tensor networks”, although I’m sure that some sleazy, opportunistic physicists and science journalists will claim that the two are co-joined twins. Unlike the classical, mostly deterministic, highly distributed calculations that TensorFlow performs, quantum computers have to deal mostly with quantum probabilistic instead of deterministic calculations, and distributed computing for QCs would be very different than its classical counterpart. I think when dealing with probabilistic calculations, either on a classical or quantum computer, Classical Bnets and Quantum Bnets are the most natural and productive framework/strategy, as I’ve explained before.

Despite TensorFlow being Fakesian Networks, I welcome Google’s move to open TensorFlow, because it certainly raises the level of visibility, cooperation, competition and tension/suspense in the AI arena. For example, I’m sure that right about now Microsoft is facing a lot of pressure to respond in kind to the news about TensorFlow. So what does Microsoft use instead of TensorFlow to do its Bing AI? Is it Infer.net? Whatever it is, will MS have to open source part of its Bing AI, to keep up with the Joneses and the Kardashians?

I like Infer.net. It looks much more Bayesian Networky to me than TensorFlow does. Unfortunately, so far MS has only released to the public infer.net’s binary and API, and it has forbidden non MS people from using infer.net for commercial purposes.

Told you so UPDATE1: Ta-tan, Nostradamucci’s predictions have turned into reality again. Nov 12, just 2 days after Google released TensorFlow, Microsoft announces the open-sourcing of DMTK (Distributed Machine Learning ToolKit). And of course, Facebook was the first, with its open-sourcing of Torch enhancements on Jan 16 of this year.

UPDATE2: Related news. After UPDATE1, I learned that IBM has also been busy open-sourcing distributed AI software. It has recently open-sourced SystemML (ML=Machine Learning), a small part of its Watson software. The GitHub repository of SystemML was started on Aug 17, 2015. According to this press article, circa Nov 23, 2015, SystemML was accepted into the Apache incubator (a preliminary step to being declared a saint, where sainthood means you have performed 2 miracles and you are declared officially integrated and compatible with the Apache libraries, especially Spark, which SystemML will rely heavily upon.)