Targeting 101: Disabling Google and Finding Software Alternatives
June 30, 2019
I read “Completely Block Google and Its Services.” If you are concerned about Google’s data policies, you may want to read the article and follow the instructions in the pihole-google.txt file. It appears that there are more than 7,000 services which Google uses to obtain one’s personal information.
Is this a surprise? No, what’s interesting is that disabling items one by one in an Android device is not going to do the job. I particularly liked the listing of DoubleClick add ons. Here’s a sampling of the more than two dozen items:
analytics.txt
firebase.txt
fonts.txt
mail.txt
products.txt
Some readers of DarkCyber may find the DoubleClick patents interesting. An overview of the cookie method appears in “Method and Apparatus for Transaction Tracking Over a Computer Network.” You can locate the document at this link. DoubleClick has other interesting inventions as well. I covered more of these in my 2003 The Google Legacy and the follow-up monograph, Google Version 2. I am not returning to the Museum of Googzilla.
Once Google has been removed from your Android device, you may want to find replacement for the Google Play and Google provided apps. You can find a useful list in “The Complete List of Alternatives to All Google Products.” The “all” makes me nervous because DarkCyber has heard rumors than not even Google has a list which is comprehensive. Like the personnel data the US government once requested, that’s just too difficult. Creating such a list is impossible because once the list has been whipped up, it might leak. Google still tries to be as secretive as possible, but its track record has changed as the firm has aged.
Stephen E Arnold, June 30, 2019
When Is a Deletion a Real Deletion
June 29, 2019
Years ago we created the Point (Top 5% of the Internet). You oldsters may remember our badge which was for a short period of Internet time a thing.
When we started work on the service in either 1992 or 1993, one of the people working with the team put the demo in the Paradox database. Hey, who knew that traffic would explode, and advertisers would contact us to put their messages on the site.
The Paradox database was not designed to deal with the demands we put upon it. One of its charming characteristics was that when we deleted something, the space was not reclaimed. Paradox — like many, many other databases — just removed the index pointer. The “space” and hence some charming idiosyncrasies remained.
Flash forward decades. A deletion may not be a deletion. Different “databases” handle deletions in different ways. Plus anyone with experience working with long forgotten systems like the Information Dimensions’ system to the total weirdness of a CICS system knows that paranoid people back up and back up as often as possible. Why? Fool with an AS400 database at the wrong time doing something trivial and poof. Everything is gone. More modern databases? Consider this passage from the Last Pickle:
The process of deletion becomes more interesting when we consider that Cassandra stores its data in immutable files on disk. In such a system, to record the fact that a delete happened, a special value called a “tombstone” needs to be written as an indicator that previous values are to be considered deleted.
When one digs around in database files, it is possible to come across these deleted data. People are amazed when a Windows file can be recovered. Yep, deletions don’t explain exactly what has been “deleted” and the conditions under which the data can be undeleted. Deletion allows one to assume one thing when the data have been safely archived, converted to tokens, or munged into a dossier.
Put these two things together and what do you get? A minimum of two places to look for deleted data. Look in the database files themselves, and look in backups.
In short, deleted data may not be deleted.
How does one know if data are “there”? Easy. Grunt work.
Why is this journey to the world of Paradox relevant?
Navigate to “Google Now Lets Users Auto-Delete Their Location and Web History.” Note this passage:
Specifically, Google account holders will be able to choose a time limit of either 3 or 18 months, after which, their location, web, and app history will automatically be deleted.
Some questions?
- Who verifies that the content has been removed from indexes and data files?
- Who verifies that the data have been expunged from metadata linked to the user?
- What does deletion mean as the word is used by Google?
- From what has something been deleted?
Like hotel temperature controls, fiddling with the knobs may change nothing.
Stephen E Arnold, June 29, 2019
Machine Learning: Whom Does One Believe?
June 28, 2019
Ah, another day begins with mixed messages. Just what the relaxed, unstressed modern decider needs.
First, navigate to “Reasons Why Machine Learning can Prove Beneficial for Your Organization.” The reasons include:
- Segment customer coverage. No, I don’t know what this means either.
- Accurate business forecasts. No, machine learning systems cannot predict horse races or how a business will do. How about the impact of tariffs or a Fed interest rate change?
- Improved customer experience. No, experiences are not improving. How do I know? Ask a cashier to make change? Try to get an Amazon professional to explain how to connect a Mac laptop to an Audible account WITHOUT asking, “May I take control of your computer with our software?”
- Make decisions confidently. Yep, that’s what a decider does in the stable, positive, uplifting work environment of a electronic exchange when a bug costs millions in a two milliseconds.
- Automate your routine tasks. Absolutely. Automation works well. Ask the families of those killed by “intelligence stoked” automobiles or smart systems on a 737 Max.
But there’s a flip side to these cheery “beneficial” outcomes. Navigate to “Machine Learning Systems Are Stuck in a Rut.” We noted these statements. First a quote from a technical paper.
In this paper we argue that systems for numerical computing are stuck in a local basin of performance and programmability. Systems researchers are doing an excellent job improving the performance of 5-year old benchmarks, but gradually making it harder to explore innovative machine learning research ideas.
Next this comment by the person who wrote the “Learning Systems” article:
The thrust of the argument is that there’s a chain of inter-linked assumptions / dependencies from the hardware all the way to the programming model, and any time you step outside of the mainstream it’s sufficiently hard to get acceptable performance that researchers are discouraged from doing so.
Which is better? Which is correct?
Be a decider either using a black box or the stuff between your ears.
Stephen E Arnold, June 28, 2019
Google Maps: A Metaphor for the Here and Now Google
June 28, 2019
In a way, I have some sympathy for the GOOG. The company, allegedly an online search service, demonstrated the inherent irrelevance of its systems and methods. I read the allegedly true story “EasyAsk Drive 7-% More Revenue” which was the headline displayed over the CNN story about Google Maps directing more than 100 individuals to a muddy field. Yep, the ad covered up the story. That’s our here and now Google.
Sure, the story was amusing even if the title was obliterated in a quest to get me to license a product in which I have zero interest. According to the write up:
Technology isn’t always foolproof, as about 100 Colorado drivers learned when Google Maps offered them a supposedly quick way out of a traffic jam.
That’s a refreshing assessment of a really screwed up mess.
I learned:
The alternate route took drivers down a dirt road that rain had turned into a muddy mess, and cars started sliding around. Some vehicles couldn’t make it through the mud, and about 100 others became trapped behind them.
Google explained the problem this way:
“We take many factors into account when determining driving routes, including the size of the road and the directness of the route,” the company said in a statement. “While we always work to provide the best directions, issues can arise due to unforeseen circumstances such as weather. We encourage all drivers to follow local laws, stay attentive, and use their best judgment while driving.”
I love the royal we.
Let’s review the flaws this single incident and news story reveal:
- Just bad information. Google Maps direct people to routes which are not passable
- Sheep like humans. Humans depend on Google to do the thinking for them and end up with incorrect information
- Talk down rhetoric. Google explains the problem with parent type talk.
- Desperate advertisers. Marketers are paying to put their message in front of people indifferent to the annoyance a person like me experiences when an irrelevant ad covers up the headline of something that interests me.
The drivers are not the only ones stuck in the mud. Quite a mess.
Stephen E Arnold, June 28, 2019
Bilderberg Attendees
June 28, 2019
Who attended the exclusive Bilderberg meeting this year?
It is the most prestigious and consequential meeting you may never have heard of, and it has been going on since 1954. The Bilderberg Meeting is an annual conference where elites from Europe and North America discuss the fate of the world. Originally formed to avoid another World War, the gathering includes some 120 to 150 of the world’s top movers and shakers in politics, industry, finance, academics, and the media. This year’s meeting was held in Dresden, Germany, the first week of June and, thanks to From the Trenches World Report, we know who was invited—just see the “Bilderberg 2019 Annotated Members List.” Blogger Video Rebel introduces their roster:
“I prefer an in depth look at the participants which is why I have been doing annotated Bilderberg participants lists for several years. This year has lots of AI experts. As usual lots of military experts and bankers plus media and politicians. But lots of experts in populist revolts and movements. Based on their invitations to attend, they seem to want to co-opt gender studies, Gays, Greens and the Trump administration.”
We are interested to see the increase in AI experts; that makes sense right now. Navigate to the write-up for the full list, but here are some names that caught my eye: Jared Cohen, Jared Kushner, Eric Schmidt, and Peter Thiel. Oh, to have been a fly on the wall for some of those conversations!
Cynthia Murrell, June 24, 2019
The Cloud, SaaS, PaaS, and CaaS: Old Wine, Newish Bottles
June 27, 2019
I read in SaaStr, a blog whose name I have no idea how to pronounce, “SaaS Unicorns vs New Categories.” The information in this post is a list of “100+ public SaaS companies and unicorns.” What’s interesting about the list is that “70 percent” of the entries are recycled software. How is this possible? The obvious answer is that a new name, a strong elevator pitch, and investors looking for a big pay day seems to work magic.
A couple of observations:
- A rose by any other name is still a rose. What hack said that?
- The cloud is a version of time sharing; that is, forget the computer on every desk. The network is the computer. Who said that crazy thing?
- Who cares? What me worry? Who said that?
Net net: Innovation is moving in the direction of cereal. New box, new slogan, and new colors. Same stuff: Pressed genetically modified grain. The staff of life goes digital.
I use three, maybe four applications each day. I have used these same digital tools since I got my hands on my own terminal at Halliburton Nuclear in 1972, maybe 1973. True, I couldn’t carry it around.
The innovations have less to do with the functionality and more to do with convenience, economies of scale, and short attention spans. Just the view from Harrod’s Creek.
Wine now comes in tubes. Great innovation but not much of an improvement over clay jars used thousands of years ago. Just like timesharing.
Stephen E Arnold, June 27, 2019
Handy List of Smart Software Leaders
June 27, 2019
As the field of AI grows, it can be difficult to keep track of the significant players. Datamation shares a useful list in, “Top 45 Artificial Intelligence Companies.” If you skim the lineup, just keep in mind—entries are not ranked in any way, simply listed in alphabetical order. Writer Andy Patrizio begins with some observations about the industry:
“AI is driving significant investment from venture capitalist firms, giant firms like Microsoft and Google, academic research, and job openings across a multitude of sectors. All of this is documented in the AI Index, produced by Stanford University’s Human-Centered AI Institute. …
We noted:
“Consulting giant Accenture believes AI has the potential to boost rates of profitability by an average of 38 percentage points and could lead to an economic boost of US$14 trillion in additional gross value added (GVA) by 2035. In Truth, artificial intelligence holds a plethora of possibilities—and risks. ‘It will have a huge economic impact but also change society, and it’s hard to make strong predictions, but clearly job markets will be affected,’ said Yoshua Bengio, a professor at the University of Montreal, and head of the Montreal Institute for Learning Algorithms.”
For their selections, Datamation chose companies of particular note and those that have invested heavily in AI. Many names are ones you would expect to see, like Amazon, Google, IBM, and Microsoft. Others are more specialized—robotics platforms Anki and CloudMinds, for example, or iCarbonX, Tempus, and Zebra Medical Vision for healthcare. Several entries are open source. Check out the article for more.
Cynthia Murrell, June 24, 2019
DeepMind Studies Math
June 27, 2019
It’s like magic! ExtremeTech reports, “Google Fed a Language Algorithm Math Equations. It Learned How to Solve New Ones.” While Google’s DeepMind is, indeed, used as a language AI, it’s neural network approach enables it to perform myriad tasks, like beating humans at games from Go to Capture the Flag. Writer Adam Dachis describes how researchers taught DeepMind to teach itself math:
“For training data, DeepMind received a series of equations along with their solutions—like a math textbook, only without any explanation of how those solutions can be reached. Google then created a modular system to procedurally generate new equations to solve, with a controllable level of difficulty, and instructed the AI to provide answers in any form. Without any structure, DeepMind had to intuit how to solve new equations solely based on seeing a limited number of completed examples. Challenging existing deep learning algorithms with modular math presents a very difficult challenge to an AI and existing neural network models performed at relatively similar levels of accuracy. The best-performing model, known as Transformer, managed to provide correct solutions to 50 percent of the time and it was designed for the purpose of natural language understanding—not math. When only judging Transformer on its ability to answer questions that utilized numbers seen in the training data, its accuracy shot up to 76 percent.”
Furthermore, Dachis writes, DeepMind’s approach to math suggests a solution to a persistent problem facing those who would program computers to do math—while our mathematics is built on a base-10 system, software “thinks” in binary. The article goes into detail, with illustrations, about why this is such a headache. See the write-up for those details, but here is the upshot—computers cannot represent every possible number on the number line. They rely on strategic rounding to get as close as they can. Usually this works out fine, but on occasion it does produce a significant rounding error. Dachis hopes analysis of the Transformer language model will point the way toward greater accuracy, through both changes to the algorithm and new training data. Perhaps.
Cynthia Murrell, June 27, 2019
Sentiment Analysis: Can a Monkey Can Do It?
June 27, 2019
Sentiment analysis is a machine learning tool companies are employing to understand how their customers feel about their services and products. It is mainly deployed on social media platforms, including Facebook, Instagram, and Twitter. The Monkey Learn blog details how sentiment analysis is specifically being used on Twitter in the post, “Sentiment Analysis Of Twitter.”
Using sentiment analysis is not a new phenomenon, but there are still individuals unaware of the possible power at their fingertips. Monkey Learn specializes in customer machine learning solutions that include intent, keywords, and, of course, sentiment analysis. The post is a guide on the basics of sentiment analysis: what it is, how it works, and real life examples. Monkey Learn defines sentiment analysis as:
“Sentiment analysis (a.k.a opinion mining) is the automated process of identifying and extracting the subjective information that underlies a text. This can be either an opinion, a judgment, or a feeling about a particular topic or subject. The most common type of sentiment analysis is called ‘polarity detection’ and consists in classifying a statement as ‘positive’, ‘negative’ or ‘neutral’.”
It also relies on natural language processing (NLP) to understand the information’s context.
Monkey Learn explains that sentiment analysis is important because most of the world’s digital data is unstructured. Machine learning with NLP’s assistance can quickly sort large data sets and detect their polarity. Monkey Learn promises with their sentiment analysis to bring their customers scalability, consistent criteria, and real-time analysis. Many companies are using Twitter sentiment analysis for customer service, brand monitoring, market research, and political campaigns.
The article is basically a promotional piece for Monkey Learn, but it does work as a starting guide for sentiment analysis.
Whitney Grace, June 27, 2019
GDPR Violators: A List
June 26, 2019
If you want to know who and what have violated GDPR rules, navigate to GDPR Enforcement Tracker on the Enforcement Tracker Web site. Here a three interesting names on the list:
- Italian political party Movimento 5 Stelle
- Mayor’s Office of the city of Kecdkemét, Hungary.
Stephen E Arnold, June 26, 2019
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