The unfortunate 900 Indian academicians are going to be killed by exposing them to the toxic language Qsharp, which is so foul that nobody uses it unless MS pays them to do so.
August 30, 2020
August 25, 2020
August 24, 2020
On Cloud 9
Today, I am on cloud 9. Do you know the origins of the idiom “on cloud nine”? According to this reference
The origin of sense 1 (“a state of bliss”) is uncertain; however, the following etymology has been suggested:
The first edition of the International Cloud Atlas (1896),[1] which defined ten types of cloud, described the ninth type as the cumulonimbus which rises to 10 km (6.2 miles), the highest a cloud can be.
I’m on cloud 9 because I received an acknowledgement on Twitter from one of my heroes
August 15, 2020
Decision Trees & Bayesian Networks
Decision Trees (DTs) are certainly cool and it is not my intention to belittle them here. Compared to Bayesian Networks (bnets), they seem easier to construct. In fact, I just wrote a poem dedicated to Decision Trees. It starts “I think that I shall never see an AI as lovely as a decision tree”. DTs do have some drawbacks, but, like I said, the purpose of this blog post is not to criticize them.
The real purpose of this post is to point out that there is a secret romance going on between Decision Trees and Bayesian Networks. Say you have a simple binary DT with YES and NO branches. Then you can construct an equivalent bnet with exactly the same tree graph. You turn the branches into arrows pointing down from the apex root node. Each fork in the tree becomes a node of the bnet. However, the nodes will have to have three states instead of two: NO, YES and NULL. This third state called NULL is a small overhead cost, a small price to pay. In return, you get to keep the tree structure in the equivalent bnet.
I explain all this more precisely in my FREE book Bayesuvius, where I describe this technique in 2 new chapters, entitled:
- Decision Trees
- Binary Decision Diagrams
Brought to you by http://www.ar-tiste.xyz, purveyors of high quality and low cost Bayesian Network software & services. A COOP.
How complicated projects like building a skyscraper or a rocket or a bridge manage so many details?
It’s a question I often wondered about as a child, as I marveled at NYC skyscrapers during my first visit there, or when I visited Cape Canaveral or the Golden Gate Bridge. I don’t claim I will provide a full answer to this question in this short post. However, recently, I’ve been studying two types of tools that are very useful in handling the mindbogglingly numerous details of a complicated project. And I would like to tell you about those 2 tools here. So this is going to be a very limited answer to the question posed in the title, but still, I hope it will be an interesting answer.
The 2 tools I am referring to are:
- PERT diagrams.
PERT diagrams are used for scheduling a project consisting of a series of interdependent activities and estimating how long it will take to finish the project. PERT diagrams were invented by the US NAVY in 1958 to manage a submarine project. Nowadays they are taught in many business and management courses. - Reliability Box Diagrams (and the closely related Fault Tree Diagrams)
Complicated devices with a large number of components such as cars or airplanes or submarines or aircraft carriers or rockets can fail in many ways. If their performance depends on some components working in series and one of the components in the series fails, this may lead to catastrophic failure. To avert such disasters, engineers use equivalent components connected in parallel instead of in series, thus providing multiple backup systems. They analyze the device to find its weak points and add backup capabilities there. They also estimate the average time to failure for the device.
Standard presentations of these 2 tools do not use Bayesian Networks. In the last week, I added 2 new chapters to my FREE book Bayesuvius, one chapter for each of the above 2 tools. What I did was to translate both of those tools into Bayesian Networks. So now you can do PERT analysis and Reliability/Failure Analysis without leaving your B net comfort zone, using only B Nets.
Brought to you by http://www.ar-tiste.xyz, purveyors of high quality and low cost Bayesian Network software & services. A COOP.
Quantum Bayesian Networks 