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IBM Research: An Inside Look

October 13, 2015

The Los Angeles Times may be on the ropes, but it ran an interesting story with the alluring title “A Peek Inside IBM’s Research Lab Points to the Shortcomings of Corporate R&D.” The information seems to come from an interview with a former laborer in the Big Blue Vineyards.

I noted several points which I found interesting:

First, the congeniality of IBM researchers:

“Bell evolved into a very competitive internal culture. People were really knocking against each other. Internal seminars were quite an ordeal because you were subjected to really heavy scrutiny. Internal dealings among scientists at IBM were much more congenial.”

Perhaps that is why no one at IBM Watson points out the silliness of the Jeopardy promotion, the notion that newspaper readers grasp APIs, or Bob Dylan’s pitching cognitive computing. Congenial. Good.

Second, the role of physics and physicists. Now keep in mind that Google relies on physicists. Maybe not as much as the physicists would like, but the folks are there. Here’s the snippet about IBM and physics:

IBM still has a physics department, but at this point almost every physicist is somehow linked to a product plan or customer plan.

Yes, I knew it. The secret to a successful product and growing revenues is linking a physicist to a product used by a Jeopardy aficionado. Obvious.

Finally, the patent league table:

The corporation in 2014 notched its 22nd straight year leading the world in the number of patents granted, with 7,534 patents granted, absolutely smoking the competition. (Samsung was second, with 4,952; Microsoft and Google were well down the list with ranks of 5th and 8th, according to a reckoning by Fortune.)

I can hear the chant now, “We’re number one. We’re number one.” Perhaps IBM will adopt a Black Lace tune like Do the Conga to promote Watson. You know:

It’s conga It’s Watson night so join the party everyone. The night has just begun. Do do do. Come on and do the Watson. (Source: LyricsMania with inputs from the addled goose.)

Remember, we’re all having fun, Watson.

Stephen E Arnold, October 13, 2015

Kroger IT Management and the Pain of Reality

October 12, 2015

In Harrod’s Creek, we have access to a giant store doing business here as Kroger. There is an all organic outfit down the hollow. I like that outfit. The prices are higher for some things, but the store is human scale. The Kroger warehouse requires me to walk almost 500 meters to get my dogs treats, my wife some ersatz milk product, and for me to stock up on Mountain Dew and M&Ms.

The salad bar is history, replaced with plastic boxes of pre-jigged stuff. The Fancy Dan foods cost the same and Organic City, so many folks in this area avoid the Kroger offerings. The center of our store houses Wal-Mart and Home Depot type products. I don’t think about buying red and blue non stick pans when I do a trip to the grocery for my wife.

I read with considerable interest “Kroger CIO: Four lessons for strategic IT.”

Each time I visit the Kroger in Harrod’s Creek or more accurately, Prospect, Kentucky, I fear the Hitachi based automatic check out systems. Kroger is trimming humans at check outs, presumably the Hitachi units are better, faster, and cheaper.

A trip to the local chain grocery can be an enjoyable experience. Don’t forget your customer loyalty card. Don’t complain about the difficulty of finding a product. Don’t hassle the Kroger humans about one price on the product and a different price in the Kroger database. Have a nice day.

I learned from Chris Hjelm, the CIO, of Kroger, one of the world’s largest companies, that information technology must be relevant. I wonder, “To whom, Mr. Hjelm.” Your boss, to suppliers, or to the individual customer? Mr. Hjelm is responsible for managing the company’s nationwide network of Information and Technology Systems, including systems used in retail stores, manufacturing plants, distribution centers and offices, as well as Research & Development. He also oversees 84.51°, Aviation, Corporate Travel, Indirect Sourcing, and the Check Recovery Center.” He has an honorary PhD degree and before joining Kroger in 2005, he was CIO of Cendant’s Travel Distribution Services, eBay, and Excite@Home, and a CIO at Federal Express. He “has a particular passion for food and is an aspiring amateur chef.” Cendant broke up into four companies. eBay is an online flea market. Excite@Home was a darned exciting outfit when it purchased Kendara’s personalization technology before Excite lost its excitement. FedEx, well, FedEx ships stuff.

Now what are the lessons for strategic IT. I assume this is different from making information technology actually work.

First, the lesson numero uno is to earn credibility as a reliable service provider. I think this means deal with vendors who will implement systems which meet the needs of the Kroger person or unit with an IT need. Yep, making stuff work is good.

Second, one must learn the business. This is no small task when one considers that work experience in shipping, Internet flame outs, online flea marketing, and travel may not seem to be directly related to selling groceries. No, I understand. The IT part is the fiber of these businesses. Ergo, food is just like eBay.

Third, form relationships with one superiors. Okay, that seems to be a safe statement. Due to the ultra conservative, siege mentality of most senior executives in many traditional businesses facing heat from online vendors, that’s good. Keeping one’s job is strategic.

Fourth, use experts. Nay, rely on experts. The good manager, it seems, can terminate experts or ignore them if down the hall. The strategy may reinforce self preservation like the relationships with those higher on the food chain (pun intended).

Now reality. Annoying reality.

At the local Kroger, senior management have deployed self check out units. Most of the time, about one third of the available units are operating. The reason is management’s desire to funnel customers to few self check outs and thus reduce the need for expensive humans who have to intervene frequently when customer transactions go off the rails.

Example: I bought an Ambrosia apple, number 3438. The Hitachi scanning system registered one pound of cheddar cheese. A moonlighting law enforcement officer was at the self check out and managed to clear the transaction. I got the apple for free. Ah, an annoying anomaly.

The new Kroger stores are large. They are organized according to the type of anti social thinking pioneered by Paco Underhill; to wit, make customers who want bread and fruit and milk walk from the entrance along a path of an equilateral triangle. Why put frequently purchased products in one convenient location? The strategy is to force a person to walk so the person will buy stuff not on the person’s list.

The scale of the products in our local Kroger is astounding. One employee told me that were more than 90,000 things in the story. Wow, how many red skillets sit for months without a human touching them? How much food is dumped at expiration time because no one buys the product?

Our local Kroger offers printed on paper maps, not mobile content, to help a customer find a product. Do you know where mustard is? Do you know where a mixture of mustard and relish is? Answer: in separate aisles, not together.

Kroger cannot alphabetize. Look at the signs hanging from the ceiling list products in an aisle. Are these alphabetized. Nope. Waste of time.

Everything in the Kroger—from the database which is out of sync with the product codes to the location of the products—is presented in a way that says, “Hey, go to Paul’s or Fresh Market.”

What is the information strategy at Kroger stores?

  1. Create a perception of credibility among your co workers and colleagues.
  2. Implement the routine business and learn the camp fire stories about how wonderful Kroger was and is.
  3. Get to be pals with those with more Kroger juice
  4. Use those consultants because it is easier to deflect criticism than take responsibility for tasks.

Kroger is a grocery store. Information technology should make it easier for customers. IT should make it possible for management to know when databases are not in sync. Partners can use Kroger IT to reduce waste and inefficiency.

Kroger, like any retail chain based on the build it they will come principle, will have to deal with two types of technical debt. Like credit card debt, the interest adds friction to keeping the flawed systems u9p and running. Like Walgreen’s, the interest on the real estate is not chimera.

Excitement is ahead for those living the retail dream in a world in which Amazon wants to use technology to eliminate the need to experience the pain and waste the time dribbled away at the grocery store.

Has Kroger IT entertained this statement, “When will that automated delivery arrive? I just ordered 10 minutes ago.” Amazon, are you listening?

Stephen E Arnold, October 12, 2015

Artificial Intelligence: A Jargon Mandala to Understand the Universe of Search

October 12, 2015

I read “Lux: Useful Sankey Diagram on AI.” A Sankey diagram, according to Sankey Diagrams a “Sankey diagram says more than 1,000 pie charts.” The assumption is, of course, that a pie chart presents meaningful data. In the energy sector you can visual flows in complex systems. It helps to have numbers when one is working towards a Sankey map, but if real data are not close at hand, one can fudge up some data.

Here’s the Sankey diagram in the write up:


You can see an almost legible version at this link.

What the diagram suggests is that certain information access and content processing functions flow into data mining, machine learning, and statistics. If you are a fan of multidimensionality, the arrow of time may flow in the reverse direction; that is from data mining, machine learning, and statistics to affective computing, cognitive computing, computational discovery, image and video analytics, language translation, navigation, recommender systems, and speech recognition.

The intermediary state, tinted a US currency green provides intermediating operations or conditions; for example, anomaly detection, collaborative filtering, computer eavesdropping, computer vision, pattern recognition, NLP, path planning, clustering, deep learning, dimensionality reduction, networks graphic models, online reinforcement learning, pattern similarity, probabilistic modeling, regression, and, my favorite, search algorithms.

The diagram, like the wild and crazy chemical imagery for Watson, seems to be a way to:

  1. Collect a number of discrete operations
  2. Arrange the operations into some orderly framework
  3. Allow the viewer to perceive relationships or the potential for relationships among the operations.

In short, skip the wild and crazy presentations by search and content processing vendors about how search enables broader and, hence, more valuable activities. Search is relegated to an entry in the intermediating column of the Sankey diagram.

My thought is that some folks will definitely love the idea that the many different specialties of content processing can be presented in a mandala which invites contemplation and consideration.

The diagram makes clear that when a company wants to know what one can do with the different and often clever operatio0ns one can perform with content, the answer may be, “Make a poster and hang it on the wall.”

In terms of applications, the chart makes quite explicit that some clever team will have to put the parts in order. Does this remind you of building a Star Wars character from Lego blocks.

The construct is the value, not the individual enabling blocks.

Stephen E Arnold, October 12, 2015

Web Site Search Goes Camping

October 12, 2015

It is a common fact that if you are a major retailer and your Web site’s search function is horrible, you are losing millions of dollars in sales.  Cabela’s is the world’s largest marketer of hunting, fishing, camping, and other outdoor merchandise decided to upgrade their Web site with GroupBy says PR Newswire in the press release, “Cabela’s And GroupBy Partner To Improve Site Search.”

With GroupBy’s advice, Cabela’s has made a good choice:

“After careful evaluation, Cabela’s selected Searchandiser to replace their Oracle Endeca site search, as they required a robust solution that would deliver accurate search results and an improved user experience for their customers. ‘At Cabela’s we strive to continually improve our customer experience and search relevance is an opportunity area we have identified,’ said Scott Johnstone, Cabela’s Technology Partner Relationship Manager.  ‘To that end, we are partnering with GroupBy Inc. to leverage their merchandising tools, search expertise and the underlying technology.’”

As Cabela’s market expands, with Searchandiser creates a better online shopping experience for users with more secure transactions.  Any outdoor enthusiast with tell you that equipment is vital for a good adventure.   As more people are heading outside to experience the great outdoors, they rely on a decent Web site to order their supplies and gear.  Cabela’s is set to meet the new surge with better searching functionalities.

Whitney Grace, October 12, 2015
Sponsored by, publisher of the CyberOSINT monograph

Real Journalism Forks Real Humans

October 9, 2015

AP’s Robot Journalists Are Writing Their Own Stories Now” suggests that wizards who suggest that automation creates jobs may want to outthink their ideas. Remember the good old days. The Associated Press, United Press International and other “we use humans” news gathering organizations hired people. Now some of the anecdotes about real journalists are derogatory. I never met a journalist who was inebriated at 9 30 am. Noon? Maybe?

In the write up, the Associated Press, which has a fascinating approach to its ownership, rolled out Automated Insights. The idea was that software filtered and assembled real news stories.

Well, how is that working out?

IBM’s CEO believes that automation will not decimate the work force. Gannett is making an effort to buy up more newspapers so these too can be tooled to the tolerances of the Louisville Courier Journal. Fine newspaper. Fine operation.

And the AP itself? Well, the accumulated loss continues to go up. I recall reading “Employment Rates Are Improving For Everyone But Journalism Majors.”

I noted this passage in a NASDAQ write up:

The prospect of technology-driven job destruction is a matter of great debate for many scientists, technologists, and economists, some of whom predict massive losses in the labor market. In the past, new technology has destroyed jobs and created new ones, but some experts wonder if the increasing power of information technology will leave relatively less and less for people to do.

Journalism majors, unemployed “real” journalists, and contract journalists once called stringers—life is only going to get better. Lyft will make it easier for some folks to become taxi drivers. There are plenty of jobs as data scientists, a profession eager for those who can write prose. There are also opportunities to become experts in search and content processing. Hey, words are words.

Stephen E Arnold, October 9, 2015

Amazon Updates Sneaker Net

October 8, 2015

I remember creating a document, copying the file to a floppy, and then walking up one flight of steps to give the floppy to my boss. He took the floppy, loaded it into his computer, and made changes. A short time later he would walk down one flight of steps, hand me the floppy with his file on it, and I would review the changes.

I thought this was the cat’s pajamas for two reasons:

  1. I did not have to use ledger paper, sharpen a pencil, and cramp my fingers
  2. Multiple copies existed so I no longer had to panic when I spilled my Fresca across my desk.

Based on the baloney I read every day about the super wonderful high speed, real time cloud technology, I was shocked when I read “Snowball’s Chance in Hell? Amazon Just Launched a Physical Data Transfer Service.” The news struck me as more important than the yap and yammer about Amazon disrupting cloud business and adding partners.

Here’s the main point I highlighted in pragmatic black:

A Snowball device is ordered through the AWS Management Console and is delivered to site within a few days; customers can order multiple devices and devices can be run in parallel. Described as coming in its “own shipping container” (it doesn’t require packing or unpacking) the Snowball is entirely self-contained, complete with 110 Volt power, a 10 GB network connection on the back and an E Ink display/control panel on the front. Once received it’s simply a matter of plugging the device in, connecting it to a network, configuring the IP address, and installing the AWS Snowball client; a job manifest and 25 character unlock code complete the task. When the transfer of data is complete the device is disconnected and a shipping label will automatically appear on the E Ink display; once shipped back to Amazon (currently only the Oregon data center is supporting the service, with others to follow) the data will be decrypted and copied to S3 bucket(s) as specified by the customer.

There you go. Sneaker net updated with FedEx, UPS, or another shipping service. Definitely better than carrying an appliance up and down stairs. I was hoping that individuals participating in the Mechanical Turk system would be available to pick up an appliance and deliver it to the Amazon customer and then return the gizmo to Amazon. If Amazon can do Etsy-type stuff, it can certainly do Uber-type functions, right?

When will the future arrive? No word on how the appliance will interact with Amazon’s outstanding search system. I wish I knew how to NOT out unpublished books or locate mysteries by Japanese authors available in English. Hey, there is a sneaker net. Focus on the important innovations.

Stephen E Arnold, October 8, 2015

Compare Cell Phone Usage in Various Cities

October 8, 2015

Ever wonder how cell phone usage varies around the globe? Gizmodo reports on a tool that can tell us, called ManyCities, in their article, “This Website Lets You Study Cell Phone Use in Cities Around the World.” The project is a team effort from MIT’s SENSEable City Laboratory and networking firm Ericsson. Writer Jamie Condliffe tells us that ManyCities:

“…compiles mobile phone data — such as text message traffic, number of phone calls, and the amount of data downloaded —from base stations in Los Angeles, New York, London, and Hong Kong between April 2013 and January 2014. It’s all anonymised, so there’s no sensitive information on display, but there is enough data to understand usage patterns, even down the scale of small neighbourhoods. What’s nice about the site is that there are plenty of intuitive interpretations of the data available from the get-go. So, you can see how phone use varies geographically, say, or by time, spotting the general upward trend in data use or how holidays affect the number of phone calls. And then you can dig deeper, to compare data use over time between different neighbourhoods or cities: like, how does the number of texts sent in Hong Kong compare to New York? (It peaks in Hong Kong in the morning, but in the evening in New York, by the way.)”

The software includes some tools that go a little further, as well; users can cluster areas by usage patterns or incorporate demographic data. Condliffe notes that this information could help with a lot of tasks; forecasting activity and demand, for example. If only it were available in real time, he laments, though he predicts that will happen soon. Stay tuned.

Cynthia Murrell, October 8, 2015

Sponsored by, publisher of the CyberOSINT monograph

Predictions about Technology: Digital Retreading

October 7, 2015

I like it when a person tells me that software or a human can predict the future. My question is, “If the predictions are spot on, why is the owner of the prediction system talking? Why not play fantasy football, pick stocks, or hang out at Keeneland during an auction and buy horses whose value will skyrocket?

The answer is, “Err, well, hmmm.”

Exactly. Predicting the future is a bit like imagining oneself putting on soccer boots and filling in for the injured Lionel Messi. Easy to thing. Essentially impossible to do.

The fix is to be fuzzy. Instead of getting into a win-lose situation, there are caveats. I find these predictions and their predictors amusing. Not as enjoyable as the antics of something like IBM cognitive computing marketed by Bob Dylan or the silliness of Hewlett Packard management activities. But close, darned close.

I read “Gartner: Top 10 Strategic Technology Trends For 2016.” I noted this statement from the capitalist tool:

…the evolution of digital business is clearly at the heart of what is covered.

Okay, the trends are going to identify trends which will allow an MBA or a savvy marketer to look at business and understand how “business” will evolve. Darwin to the future, not Darwin from the past I assume.

The question in my mind is, “Are these retread ideas?”

Here are three “trends” which caught my attention. To get the full intellectual payload, you will need to read the article or, better yet, seek out a Gartner wizard and get the trend thing straight from the horse’s mouth. Yep, right, mouth.

Trend 2: Ambient user experience.

I remember hearing about ambient computing years ago. The idea was that one could walk around and compute. I also ran across Deloitte’s identification of a similar trend months ago. But it was in the late 1990s or early 2000s when an MIT person talked about the concept. Obviously if one is computing whilst walking around, there is an experience involved. With mobile devices outselling tethered devices, it seems disingenuous to talk about this trend. According to Forbes, the capitalist tool:

Gartner posits that the devices and sensors will become so smart that they will be able to organize our lives without our even noticing that they are doing so.

I like posit. The word means “to dispose or set firmly, assume or affirm the existence of, and propose as an explanation.” Yep, posit something that academics and blue chip consulting firms have been saying for a while.

Trend 4: Information of Everything

Now these universal statements are rhetorical tactics which make my tail feathers stand up. “Everything” is a broad concept. A critical reader may want to ask, “Will you provide me with information about line 24 million in Watson’s 100 million lines of code?” The “everything” is going to provide this answer. Nope. Logical flaw. But here’s how the capitalist tool, a font of logical thought, presents this “information of everything” trend:

According to Gartner, by 2020, 25 billion devices will be generating data about almost every topic imaginable. This is equal parts opportunity and challenge.  There will be a plethora of data, but making sense of it will be the trick. Those companies that harness the power of this tidal wave of information will leapfrog competitors in the process.

I like the plethora. I like the leapfrog. I like the tidal wave. I have a sneaking suspicion that most folks with a computer device have experienced a moment of information confusion. “With every topic imaginable”, confusion is a familiar neighbor. Now how long has this concept of lots of information from lots of devices with communications capability been around? Forbes, the capitalist tool, published in June 2014 “A Very Short History of the Internet of Things.” If the Forbes’ writer had taken the time to look at that article, the concept poked its nose into the world in the early 1930s. Well, that is only 80 years ago. But it is a trend. Hmm. Trend.

Now my favorite.

Trend 9. Mesh App and Service Architecture

The notion that computer systems able to exchange information is a good one. I can’t recall when I learned about this concept. Wait. No, I remember. It was in 1963 when I took my first class in computer programming. The professor, a fine autistic polymath, explained that the mainframe—a 1710—was a collection of components. He said in 1962 that different machines would talk to one another in the future. Well, there you go. A third rate university with dullards like me in class got a prognostication which seems to be true. That was more than half a century ago. Here’s the modern version of this old chestnut:

More apps are being built to be plugged together, and the value of the combination is much greater than the sum of the parts.  As Lyft has integrated with comparable offerings in other countries, its ability to expand its offering for traditional customers traveling abroad and the reverse has meant faster growth with minimal cost implications.

Enough of these walks down memory lane. Three observations:

  1. These trends are recycled concepts
  2. The presentation of the trends is a marketing play, nothing more, nothing less
  3. Mid tier consulting firms are trying really hard to sound very authoritative, important, and substantial.

That would work if footnotes provides pointers to those who offered the ideas before. Whether a blue chip consulting firm like Deloitte or a half wild computer science professor in the Midwest, the trends are not trends.

We are, gentle reader, looking at digital retreaded tires. A recap. A remold. Old stuff made fresh. Just don’t drive too quickly into the future on these babies. Want to bet on this?

Stephen E Arnold, October 7, 2015

Full Text Search Gets Explained

October 6, 2015

Full text search is a one of the primary functions of most search platform.  If a search platform cannot get full text search right, then it is useless and should be tossed in the recycle bin.    Full text search is such a basic function these days that most people do not know how to explain what it is.  So what is full text?

According to the Xojo article, “Full Text Search With SQLite” provides a thorough definition:

“What is full text searching? It is a fast way to look for specific words in text columns of a database table. Without full text searching, you would typically search a text column using the LIKE command. For example, you might use this command to find all books that have “cat” in the description…But this select actually finds row that has the letters “cat” in it, even if it is in another word, such as “cater”. Also, using LIKE does not make use of any indexing on the table. The table has to be scanned row by row to see if it contains the value, which can be slow for large tables.”

After the definition, the article turns into advertising piece for SQLite and how it improves the quality of full text search.  It offers some more basic explanation, which are not understood by someone unless they have a coding background.   It is a very brief with some detailed information, but could explain more about what SQLite is and how it improves full text search.

Whitney Grace, October 6, 2015
Sponsored by, publisher of the CyberOSINT monograph

Learn One of These Programming Languages, Crunch Big Data. Easy, Right?

October 3, 2015

I read a listicle called “Ten Top Languages for Crunching Big Data.” The list is interesting but the underlying assumption about the languages and “crunching” Big Data was remarkable.

The core of the write up is a list of 10 programming languages which make it possible (maybe semi easy) to “generate insights.” The list has some old familiar programming languages; for example, SQL or structured query language. There’s the graduate student in psychology fave SAS. Some might argue that SPSS Clem is the way to chop Big Data down to size. There is a toolkit in the list. Remember Matlab, which for a “student” is only $49. For the sportier crowd, I would add Mathematica to the list, but I don’t want to melt the listicle.

Also on the list are Python and R. Both get quite a bit of love from some interesting cyber OSINT outfits.

For fans of Java, the list points to Scala. The open source fan can use HiveQL, Julia, or Pig Latin.

The listicle includes a tip of the hat to Alphabet Google. According to the write up:

Go has been developed by Google and released under an open source license. Its syntax is based on C, meaning many programmers will be familiar with it, which has aided its adoption. Although not specifically designed for statistical computing, its speed and familiarity, along with the fact it can call routines written in other languages (such as Python) to handle functions it can’t cope with itself, means it is growing in popularity for data programming.

Yep, a goodie from the GOOG spells Big Data magic. For how long? Well, I don’t know.

However, the assumption from which the listicle hangs is that a programming language allows Big Data to be crunched.

Whoa, Nellie.

There may be a couple of “trivial” intermediary steps required. Let me mention one. The Big Data cruncher has to code up something to get useful outputs. Now that “code up” step may require some other bothersome tasks; for example, dealing with messy data to ensure that the garbage in, garbage out problem does not arise. The mathematically inclined may suggest that the coded up “script” actually work within available computer time and memory resources. Wow, that might make a script to crunch Big Data either not work or output results which are dead wrong. What if the script implements algorithmic bias?

Whoa, whoa, Nellie.

I know that programming languages are important. But some other tasks deserve attention in my experience.

Stephen E Arnold, October 3, 2015

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