Tech. Biz. Culture. Law.
My August 2017 book for Oracle Press – an imprint of McGraw-Hill Education – is still selling well. Amazon this morning ranks it as follows:
Not bad for a 2017 book! It’s available here:
https://www.amazon.com/gp/product/B07484STST
“The world’s best band” – that’s how Forbes magazine describes Doc Scantlin and his Imperial Palms Orchestra. It’s true, I’ve seen them several times and there is no better show imaginable. Doc and his amazing talents and his amazing friends will be performing at the Carlyle Club in Alexandria, Virginia, on May 4, 2019.
Here’s the link for tickets: Doc Scantlin and his Imperial Palms Orchestra at the Carlyle Club.
Wealthy donors have contributed billions to America’s colleges and universities for years. The Chronicle for Higher Education, which I’ve often quoted in my companion website TrainingMagazine.com, published the following article citing a history of major contributions over the last few decades, going back to 1967:
Some examples:
The list is lengthy and the donors are interesting – it’s worth checking out.
So there’s this:
Lori Loughlin & Husband Feel ‘Manipulated’ in College Scam: ‘They Had No Bad Intention,’ Source Says
It wouldn’t surprise me if Lori Loughlin and her husband are telling the truth. There is, after all, a presumption of innocence in America – doesn’t that still exist? I wouldn’t be surprised if, in the final analysis, they end up
The reason I think that way is this fantastic 2014 book by Sidney Powell:
It reads like a John Grisham novel, but is real. Powell is a former federal prosecutor who defended one of the very many defendants to a series of cases brought on by the US Department of Justice’ Enron Task Force. In her book, Powell details a pattern of overreach and legal abuse, documented in great detail and backed up by appellate court decisions – including at least one appellate court-ordered investigation. It’s a stunning, maddening book, and when these “college scam” cases hit the news in the last few weeks, I was reminded of her work. The similarities are striking.
Time – and millions of dollars in legal fees – will tell.
I’ve been posting about th3 2008 film 21. The video below is an interview with the real MIT blackjack team on whom the film is based. They discuss the math behind their card counting scheme that won them anywhere from $10,000 to $500,000 per trip to Las Vegas as they “counted cards”.
I posted earlier this week about an old Rihanna song and I mentioned that it’s featured in the 2008 film 21. That’s a really interesting movie that tells the true story – sort of – of the MIT team that beat the blackjack tables in Las Vegas. The methods they used are real, the movie embellishes a bit to create a rather sinister nemesis to make it a more compelling film. But the core story is real. If you’ve seen it, it’s fun to watch.
Here’s the trailer:
Funny YouTube comment:
Jess MacDougall 3 years ago hey...this isn't about driving cars.. childhood ruined
Poor Jess … and for the record, I have no idea who that is, I just found the comment and thought it was funny.
This tune is featured in the awesome 2008 film 21, about the MIT team that beat the blackjack tables in Las Vegas.
Earlier I posted about Python. Since then I’ve worked a Python project that interacted with a SQL database using SQL Alchemy and got an opportunity to do quite a bit in the way of data transformation and data integration, leveraging Python’s remarkably flexible capabilities and rich libraries for use with analytics, machine learning and deep learning, and more. It’s a great language. I love Java but I can certainly see why Python has the momentum right now. It’s an easy language to pick up. Whereas a typical Java program requires quite a bit to reach the simple “Hello, World” test program, Python gets you there much more quickly.
print("Hello, world");
That’s it! Crazy easy. Compare this to Java:
public class demonstrateHelloWorld {
public static void main(String[] args) {
System.out.println("Hello, World");
}
}
Now – to be fair – there’s a reason this Java example is more involved, its due to Java’s requirement that code be structured in the form of a class. Python doesn’t require that but offers the same option. If you’ve seen this sort of Python code:
if __name__ == "__main__":
print('Hello, world')
… then you know what I’m talking about.
A side-by-side comparison of the two languages is more involved that I intend to do here, my only point is that Python is very easy to start with and get productive with quickly.
For example, interacting with local text files can be involved with other languages. With Python, its simple:
file = open('output.txt', 'w')
file.write('Hello World')
file.close()
That’s it. Stupid simple.
My last project this year involved middleware servers, REST services, data tranformations of all sorts, multiple databases and data stores leveraging SQL and JSON, abstracted environment configurations to support dev ops in the larger environment, and more. Easy to manage in Python.
It’s a great language. Java is still number one, and the last I checked I think even C++ still dominates over Python on the list of world’s most in-use software languages.
But Python is the one to watch.
“Everyone doesn’t have their own well at their house … we tap into the water network, we tap into the electricity network … we get better service at a lower price by having our water come from a utility … we’ll experience the same economic advantages … as we get our data from an information utility.” – Larry Ellison, Oracle Open World, September 18, 2016, spoken during his keynote presentation: “The Cloud: A New Era of Utility Computing”
Don’t be misled. Larry Ellison is not late to the Cloud Party. Consider this quote from 1998:
… Larry Ellison, the founder and CEO of the Oracle Corporation, has frequently mused at what life would be like if common household appliances had the same complexity of maintenance as a PC … “Sorry, I can’t go out tonight, I’m staying home so I can upgrade my TV to version 7.0.” … Ellison’s argument … the PC must become as easy to use as any common household appliance.
In a recent presentation … Ellison was asked … if the network will be stable enough—won’t it crash from time to time? … Ellison’s response … what is the last thing that crashed on you: your telephone, or Windows … ? The audience roared with laughter, making the answer obvious …
The number of networks we already depend on is impressive: plumbing, electricity, highways, television, radio—all networks professionally run by others … Why should a computer user experience anything different?
— Excerpted from Chapter 27, “Oracle Web Application Server”, in the book “Oracle8 Server Unleashed” published by Macmillan Computer Publishing.
That’s a brilliant quote, isn’t it? You better say ‘yes’ because I selected that 1998 quote for my chapter in that “Oracle8 Server Unleashed” book. (I only authored that one chapter, this work was the result of a few dozen authors, I only authored the one chapter.)
I selected that quote because of Larry Ellison’s leadership. Ellison, way ahead of his time, had already seen the potential of the World Wide Web and its underlying protocol, HTTP, all of which had been invented barely five years prior. Ellison had already seen the vision and was leading the charge towards “utility computing”.
So I was thrilled to see Ellison keeping that phrase alive yesterday at OOW. To me, “utility” computing is significantly more descriptive than the phrase “cloud” computing. Clouds paint images of vague, nebulous, fragmented things somewhere far away, without specificity or form or shape. But utility computing is much more descriptive of what is really intended: a ubiquitous workhorse that is simultaneously both specific and dependable, scientifically complex at it core, cutting edge in its usefulness, yet easy for everyone to access and use for common purposes.
Cloud computing is not just a “computer somewhere else”, as I’ve heard it described – that’s a funny one-liner and I’ve been guilty of laughing at it myself. But cloud computing – utility computing – is significantly much more than that. It is a series of complex hardware, network, and software components, brought together in an easy-to-configure, easy-to-access, ever-present simple interface through which an end-user can quickly set up (provision) whatever resources he or she needs quickly to meet whatever business requirement is currently demanding attention, in a more cost-effective manner than ever before, by compartmentalizing the various desired components and optimizing their deployment.
It is true that Amazon Web Services has much of the market momentum at the moment, and has captured a lot of the headlines in the tech industry.
But the truth is that Oracle is still, to this moment, the unquestioned leader in all forms of serious professional business software applications, and he has been actively and purposely working for years to leverage the power of the network in support of business objectives. The multi-year re architecture of most of the world’s leading enterprise resource planning (ERP) applications is just one example of what they’ve done toward this goal.
This week’s Oracle Open World should be very eventful.
For Larry Ellison’s keynote last night, see this link, but recognize this link might not survive much longer than OOW 2016: https://www.oracle.com/openworld/on-demand/index.html
For an OOW YouTube channel highlight that might survive the link above, see here: https://youtu.be/S1p_TcS9bxk
Four years ago I blogged about the fact that medal counts are often reported on a per-country basis without regard for the population of any country. I’ve always thought that approach to be silly. The per-capita medal count is much more interesting to me.
So this morning I used Bing to get the latest count by country of gold medals at the ongoing Rio Olympics. I included only those nations that have won two or more gold medals.
I then visited Wikipedia to get recent population counts for those nations.
I used all this data to determine the per-capita win per nation and ranked them. The results are below.
| Ranking | Country | Per Capita Ratio (Descending Order) |
Gold Medal Count | Population | Date Population Reported |
|---|---|---|---|---|---|
| 1 | Jamaica | 7.34418E-07 | 2 | 2,723,246 | 31-Dec-14 |
| 2 | New Zealand | 6.37423E-07 | 3 | 4,706,450 | 16-Aug-16 |
| 3 | Hungary | 6.10811E-07 | 6 | 9,823,000 | 1-Jan-16 |
| 4 | Croatia | 4.77251E-07 | 2 | 4,190,669 | 31-Dec-15 |
| 5 | Netherlands | 3.52389E-07 | 6 | 17,026,640 | 16-Aug-16 |
| 6 | Australia | 2.48343E-07 | 6 | 24,160,100 | 16-Aug-16 |
| 7 | United Kingdom | 2.45738E-07 | 16 | 65,110,000 | 30-Jun-15 |
| 8 | Switzerland | 2.39762E-07 | 2 | 8,341,600 | 31-Mar-16 |
| 9 | Greece | 1.84196E-07 | 2 | 10,858,018 | 1-Jan-15 |
| 10 | Cuba | 1.77952E-07 | 2 | 11,239,004 | 31-Dec-15 |
| 11 | Belgium | 1.76637E-07 | 2 | 11,322,674 | 1-Jun-16 |
| 12 | Italy | 1.31871E-07 | 8 | 60,665,551 | 1-Jan-16 |
| 13 | South Korea | 1.18107E-07 | 6 | 50,801,405 | 1-Jul-16 |
| 14 | Kazakhstan | 1.12656E-07 | 2 | 17,753,200 | 1-May-16 |
| 15 | Germany | 1.10064E-07 | 9 | 81,770,900 | 30-Sep-15 |
| 16 | France | 1.04891E-07 | 7 | 66,736,000 | 1-Jul-16 |
| 17 | Spain | 8.61356E-08 | 4 | 46,438,422 | 1-Jan-16 |
| 18 | United States | 8.01855E-08 | 26 | 324,248,000 | 16-Aug-16 |
| 19 | North Korea | 7.91108E-08 | 2 | 25,281,000 | 1-Jul-16 |
| 20 | Russia | 7.50345E-08 | 11 | 146,599,183 | 1-May-16 |
| 21 | Uzbekistan | 6.33406E-08 | 2 | 31,575,300 | 1-Jan-16 |
| 22 | Canada | 5.53166E-08 | 2 | 36,155,487 | 1-Apr-16 |
| 23 | Japan | 5.51225E-08 | 7 | 126,990,000 | 1-Jul-16 |
| 24 | Poland | 5.20329E-08 | 2 | 38,437,239 | 31-Dec-15 |
| 25 | Kenya | 4.52934E-08 | 2 | 44,156,577 | 1-Jul-15 |
| 26 | Colombia | 4.09887E-08 | 2 | 48,793,900 | 16-Aug-16 |
| 27 | Thailand | 3.04279E-08 | 2 | 65,729,098 | 31-Dec-15 |
| 28 | Iran | 2.51689E-08 | 2 | 79,463,100 | 16-Aug-16 |
| 29 | China | 1.08836E-08 | 15 | 1,378,220,000 | 16-Aug-16 |
| 30 | Brazil | 9.68457E-09 | 2 | 206,514,000 | 16-Aug-16 |
You could argue that some country populations and/or medal counts are too small to prove anything other than an interesting anomaly. Or not. But there’s no question that Hungary and the Netherlands are very impressive, each with six gold medals for an overall per-capita rate that’s dramatically higher than, for example, Great Britain, the United States, and especially China, who – in spite of their very high medal count, is actually very far behind the other nations on a per capita basis.
These are the results presented graphically, which I created using Microsoft Excel, captured with the built-in “snipping” tool in Microsoft Windows: :
The Pareto Line seems to curve most significantly above Hungary and the Netherlands, which have the most striking performance in terms of size of population and count of gold medals. Also – note how far back China ranks. They might have a lot of medals, but they also have the largest population in the world.
To me, this sort of analysis is much more interesting than the simple medal count that doesn’t account for population.
Sources: