Quote to Note: Newspaper Style
January 16, 2016
Wikipedia produces clicks for the search engines. Wikipedia allows students to output “essays” on almost any topic which catches the fancy of the common core crowd. Wikipedia also triggers some interesting comments about its approach.
Navigate to “Wikipedia: An Old-Fashioned Corner of Truth on the Internet.” The write up, which appears in an old fashioned newspaper, contains a quote to note. Here it is, gentle reader:
Because it was factual, updated quickly, and didn’t use that annoying newspaper style of trying to make stuff sensational. Wikipedia, the news source?
Annoying newspaper style? Great stuff. The write up about Wikipedia somewhat reluctantly points out that the service is useful.
I wonder if real journalists recycle Wikipedia information. What do you think? The tip off is the list of the strangest things found in Wikipedia.
Stephen E Arnold, January 16, 2016
The Key to Revenue? Ads. Predictive Analytics Not So Much
January 16, 2016
I read a darned amazing write up in a marketing blog. First, the story the marketing blog turned into “real news” is a sponsored study. That means an ad. But, even more interesting, the source of the funded study is a mid tier consulting firm. Now you know there are blue chip consulting outfits. I used to work at one and have done consulting projects for other blue chip outfits over the last 40 years. The blue chip outfits are more subtle in their thought leadership, which is one reason why there are blue chip outfits sitting on top of a pile of azure chips and gray chip vendors of expertise.
The second point is that the sponsored study conveniently converted into “real news” is that revenue comes from predicative analytics. Excuse me. But if a company is paid to flog an ad messages, doesn’t that mean the revenue comes from advertising or, in this case, clumsy propaganda. If the predictive analytics thing actually worked revenue wonders, wouldn’t the mid tier consulting firm use predictive analytics to generate cash? Wouldn’t the marketing newsletter use predictive analytics to generate cash?
To see this sponsored content daisy chain in action, navigate to “Forrester Report: Companies Using Predictive Analytics Make More Money.” The mid tier outfit in question is Forrester. Is their logo azure tinted? If not, maybe that is a color to consider. None of the stately expensive tie colors required.
The publication recycling the sponsored content as “real” news is Marketing Land. The name says it all, gentle reader.
What is the argument advanced for EverString by Forrester and Marketing Land?
Here’s the biggie:
The big takeaway: “Predictive marketing analytics use correlates with better business results and metrics.”
That is, compared with those in the survey who do not use predictive analytics (which it calls Retrospective Marketers). “Predictive Marketers,” the report notes, “are 2.9x more likely to report revenue growth at rates higher than the industry average.” They are also 2.1 times more likely to “occupy a commanding leadership position in the product/service markets they serve” and 1.8 times more likely to “consistently exceed goals when measuring the value their marketing organizations contribute to the business,” compared to the Retrospective Marketers in the survey. Forrester analyst Laura Ramos, who was involved in the report, told me the main point is clear: “Predictive analytics pays off.”
What froth? The 2.9x suggests real analysis. Sure, sure, I know about waves and magic squares.
There are companies delivering predictive analytics. Some of these outfits have been around for decades. Some of the methods have been known for centuries. I won’t remind you, gentle reader, about my wonky relative and his work for the stats guy Kolmogorov.
Suffice it to say that EverString paid Forrester. Forrester directly or indirectly smiled at Marketing Land. The reader learns that predictive analytics generate revenue.
Nope, the money comes from selling ads and, I assume, “influence.”
Put that in your algorithm and decide which is better: Selling ads or figuring out how to construct a predictive numerical recipe?
Right. Mid tier firms go the ad route. The folks recycling ads as news grab a ride on the propaganda unicycle.
Stephen E Arnold, January 16, 2016
Newspaper Reveals Tricks for Google Search
January 15, 2016
I love it when newspapers get into the online research game. I think fondly about the newspaper in Nevada. Its reporters were not able to figure out who owned the newspaper. Hint: Casino owner.
I read “How to Use Search Like a Pro: 10 Tips and Tricks for Google and Beyond.” The word “beyond” is darned popular when it comes to search. I wonder who has been using the phrase “beyond search” for a decade or more? Hmm. No idea.
The write up includes some jaw droppers for the folks who are not familiar with SDC Orbit or the conventions of Lockheed Dialog; for example:
Use quotes to search for a bound phrase. Okay. What happens when Google does not locate an exact phrase match? What then, gentle Guardian? No comment? Okay.
Here’s another tip and trick:
Use the OR operator. Now that is helpful when one is looking for a really big result set. How does one narrow a Google result set when the GOOG says, “About 1,400,000 results. Thoughts? Nope. Okay.
And one more. For the other seven you will have to read the source write up:
Use the “Related” operator to find more sites like — wait for it — the guardian.com. Nothing like using a dead tree publication to flog some clicks from the punters.
I wish to point out that the GOOG is deeply concerned about the decline in boat anchor type searches. The effort is being directed at providing information before the user knows s/he needs it. This is called predictive search.
I am delighted that the newspaper is describing how to use a search system which is losing traction. But, hey, that’s what makes real journalists and dead tree publishers the type of outfit that Jeff Bezos and Sheldon Adelson hungry to buy these companies.
Stephen E Arnold, January 15, 2016
Meg Whitman Prediction: From Advocates of Quitting
January 15, 2016
I love predictions. Most folks forget the ones which do not materialize. The others get a moment of Internet fame and then die like day lilies.,
I read an interesting chunk of prognosticative fluff in “Meg Whitman Will Leave HP and 4 Other Predictions For 2016.”
The prediction is that Ms. Whitman will “declare victory” and head to a more halcyon place. Fortune asks, “Who could blame her?”
That’s nifty. A quitter. I suppose when one works at Fortune, the idea of quitting is a pretty attractive one.
Will Ms. Whitman depart? I don’t know. I do know that the litigation she spawned will continue through 2016 and probably years to come.
When she departs, the law firms dealing with her Autonomy allegations may give her a bouquet of —what?—day lilies?
Stephen E Arnold, January 15, 2016
More Open Source Smart Software
January 15, 2016
The gift giver this time is Baidu. Navigate to “Baidu Open-Sources Its WARP-CTC Artificial Intelligence Software.” Baidu’s method is call the connectionist temporal classification or CTC method. Is the innovation from the Middle Kingdom? Nah. Switzerland. You know, the country where Einstein whacked away with his so so computational skills.
According to the write up:
The CTC approach involves recurrent neural networks (RNNs), an increasingly common component used for a type of AI called deep learning. Recurrent nets have been shown to work well even in noisy environments.
Have at the code, gentle read. The link is https://github.com/baidu-research/warp-ctc
Stephen E Arnold, January 14, 2016
Hello, Big Algorithms
January 15, 2016
The year had barely started and it looks lime we already have a new buzzword to nestle into our ears: big algorithms. The term algorithm has been tossed around with big data as one of the driving forces behind powerful analytics. Big data is an encompassing term that refers to privacy, security, search, analytics, organization, and more. The real power, however, lies in the algorithms. Benchtec posted the article, “Forget Big Data-It’s Time For Big Algorithms” to explain how algorithms are stealing the scene.
Data is useless unless you are able to are pull something out of it. The only way get the meat off the bone is to use algorithms. Algorithms might be the powerhouses behind big data, but they are not unique. The individual data belonging to different companies.
“However, not everyone agrees that we’ve entered some kind of age of the algorithm. Today competitive advantage is built on data, not algorithms or technology. The same ideas and tools that are available to, say, Google are freely available to everyone via open source projects like Hadoop or Google’s own TensorFlow…infrastructure can be rented by the minute, and rather inexpensively, by any company in the world. But there is one difference. Google’s data is theirs alone.”
Algorithms are ingrained in our daily lives from the apps run on smartphones to how retailers gather consumer detail. Algorithms are a massive untapped market the article says. One algorithm can be manipulated and implemented for different fields. The article, however, ends on some socially conscious message about using algorithms for good not evil. It is a good sentiment, but kind of forced here, but it does spur some thoughts about how algorithms can be used to study issues related to global epidemics, war, disease, food shortages, and the environment.
Whitney Grace, January 15, 2016
Sponsored by ArnoldIT.com, publisher of the CyberOSINT monograph
Strong and Loud or Quiet and Weak, Googles Robot Grandkids Fail to Impress the Marines
January 15, 2016
The article titled Why the Marines Don’t Want Google’s Robot Soldiers in Combat on Fortune discusses the downside of the Google-owned company Boston Dynamics’ robots. You might guess, moral concerns, or more realistically, funding. But you would be wrong, since DARPA already shelled out over $30 million for the four-legged battle bots. Instead, the issue is that a single robot, which looks like a huge insect wearing a helmet and knee and elbow pads, emits a noise akin to a motorcycle revving, or a jackhammer drilling, for small movements. The article explains,
“Anyone who’s seen Boston Dynamics’ four-legged robots in action typically is wowed by their speed, strength, and agility, but also note how loud they are. They sound like chainsaws on steroids. And that decibel level is apparently a problem for potential customers, namely the U.S. military.
For Marines who took the robot out for a spin, that noise is apparently a deal breaker. “They took it as it was: a loud robot that’s going to give away their position.”
The reason for all this hullaballoo on the part of the robot is its gas engine, intended for increased robustness. The military was looking for a useful helpmate capable of carrying heavy loads of up to 400 lbs. There has been some back and forth between military representatives and Boston Dynamics, but the current state of affairs seems to be a quieter, and weaker, robot. Not ideal.
Chelsea Kerwin, January 15, 2016
Sponsored by ArnoldIT.com, publisher of the CyberOSINT monograph
Google Has Some Supporters in China
January 14, 2016
Google, China wants you back. Well, more accurately, some folks in China what Google back. What is needed is unbiased search results.
According to “Chinese Citizens Are Boycotting Search Engine Baidu—and Praying for Google to Come Back”:
This week, though, tens of thousands of Chinese citizens pledged to boycott Baidu entirely, after they discovered the Beijing company has been earning profits by giving chronically ill users biased information through its chat rooms, known as “post bar” services.
The write up explains:
Launched in 2003, Baidu Post Bar, or Tieba, is a massive online community with about 19 million discussion groups that range from food to films to foreign affairs. Tieba’s numerous illness-related post bars serve as online support groups, where patients share experiences about their diseases and treatment.
Then there was a hint that Baidu was in the dark:
A Baidu spokesman told Quartz he couldn’t say what percentage of Baidu’s 19 million post bar groups were run by a commercial partner.
Yep, there’s is nothing like an objective, ad supported search system to deliver the results folks need, want, believe to be accurate.
The only hitch may be the Chinese authorities who are able to reflect on companies which tell China what to do.
Stephen E Arnold, January 14, 2016
What Makes You Ill? Social Media? Nope
January 14, 2016
I read “Loneliness, Social Networks, and Health: A Cross-Sectional Study in Three Countries.” The study reveals that people who are unhappy also get sick.
Lots of effort went into this statement:
In all three countries, loneliness was the variable most strongly correlated with health after controlling for depression, age, and other covariates. Loneliness contributed more strongly to health than any component of the social network.
My hunch is that those who are believers in social media will be able to link Snapchat snaps and Reddit posts with feeling good and being healthy.
I have a different view of social media and its possible benefits: Social media posts are outstanding sources of data for those who want to predict where a Google Map thinks you will go. Other groups like social media data as well; for example, bad actors.
My thought is that heavy users of social media may find themselves making new friends. For example, when you get out of your autonomous vehicle and know no one, you can ask, “Yo, dude, where am I?” Then say, “Let’s be friends.” This is a great ice breaker in Woodlawn, for instance.
Another function is that your college roomie now supporting certain groups of interest may open some new “friendship doors.” For example, if an investigative group exploring relationships with certain tools, you will spend quite a bit of time with your new friends.
Social media, therefore, addresses loneliness. That leads to a healthier life. Obvious, no?
Stephen E Arnold, January 14, 2016
Reverend Bayes Is Inevitable
January 14, 2016
I read “R Users Will Now Inevitably Become Bayesians.” Years ago a non mathy content management maven told me that Bayes’s methods were baloney. There is not much one can do to undo misspent youth and a lack of a technical background in things that require numbers in my experience.
The write up explains that wrestle with R will find themselves turning into adherents of Bayes’s methods, and I assume these R fans will end up looking like this:
The notion of inverse probability, according the write up,
Bayesian modeling is a general machine that can model any kind of regression you can think of….With the advent of
brmsandrstanarm, R users can now use extremely flexible functions from within the familiar and powerful R framework. Perhaps we won’t all become Bayesians now, but we now have significantly fewer excuses for not doing so. This is very exciting!
I feel a tingle, but I don’t think the CMS oriented, non mathy types will experience much of a quiver. Too bad.
Stephen E Arnold, January 14, 2016
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