Picking and Poking Palantir Technologies: A New Blood Sport?

April 25, 2018

My reaction to “Palantir Has Figured Out How to Make Money by Using Algorithms to Ascribe Guilt to People, Now They’re Looking for New Customers” is a a sign and a groan.

I don’t work for Palantir Technologies, although I have been a consultant to one of its major competitors. I do lecture about next generation information systems at law enforcement and intelligence centric conferences in the US and elsewhere. I also wrote a book called “CyberOSINT: Next Generation Information Access.” That study has spawned a number of “experts” who are recycling some of my views and research. A couple of government agencies have shortened by word “cyberosint” into the “cyint.” In a manner of speaking, I have an information base which can be used to put the actions of companies which offer services similar to those available from Palantir in perspective.

The article in Boing Boing falls into the category of “yikes” analysis. Suddenly, it seems, the idea that cook book mathematical procedures can be used to make sense of a wide range of data. Let me assure you that this is not a new development, and Palantir is definitely not the first of the companies developing applications for law enforcement and intelligence professionals to land customers in financial and law firms.

baseball card part 5

A Palantir bubble gum card shows details about a person of interest and links to underlying data from which the key facts have been selected. Note that this is from an older version of Palantir Gotham. Source: Google Images, 2015

Decades ago, a friend of mine (Ev Brenner, now deceased) was one of the pioneers using technology and cook book math to make sense of oil and gas exploration data. How long ago? Think 50 years.

The focus of “Palantir Has Figured Out…” is that:

Palantir seems to be the kind of company that is always willing to sell magic beans to anyone who puts out an RFP for them. They have promised that with enough surveillance and enough secret, unaccountable parsing of surveillance data, they can find “bad guys” and stop them before they even commit a bad action.

Okay, that sounds good in the context of the article, but Palantir is just one vendor responding to the need for next generation information access tools from many commercial sectors.

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Taking Time for Search Vendor Limerance

April 18, 2018

Life is a bit hectic. The Beyond Search and the DarkCyber teams are working on the US government hidden Web presentation scheduled this week. We also have final research underway for the two Telestrategies ISS CyberOSINT lectures. The first is a review of the DarkCyber approach to deanonymizing Surface Web and hidden Web chat. The second focuses on deanonymizing digital currency transactions. Both sessions provide attendees with best practices, commercial solutions, open source tools, and the standard checklists which are a feature of  my LE and intel lectures.

However, one of my associates asked me if I knew what the word “limerance” meant. This individual is reasonably intelligent, but the bar for brains is pretty low here in rural Kentucky. I told the person, “I think it is psychobabble, but I am not sure.”

The fix was a quick Bing.com search. The wonky relevance of the Google was the reason for the shift to the once indomitable Microsoft.

Limerance, according to Bing’s summary of Wikipedia means “a state of mind which results from a romantic attraction to another person typically including compulsive thoughts and fantasies and a desire to form or maintain a relationship and have one’s feelings reciprocated.”

limerance

Upon reflection, I decided that limerance can be liberated from the woozy world of psychologists, shrinks, and wielders of water witches.

Consider this usage in the marginalized world of enterprise search:

Limerance: The state of mind which causes a vendor of key word search to embrace any application or use case which can be stretched to trigger a license to the vendor’s “finding” system.

 

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Speeding Up Search: The Challenge of Multiple Bottlenecks

March 29, 2018

I read “Search at Scale Shows ~30,000X Speed Up.” I have been down this asphalt road before, many times in fact. The problem with search and retrieval is that numerous bottlenecks exist; for example, dealing with exceptions (content which the content processing system cannot manipulate).

Those who want relevant information or those who prefer superficial descriptions of search speed focus on a nice, easy-to-grasp metric; for example, how quickly do results display.

May I suggest you read the source document, work through the rat’s nest of acronyms, and swing your mental machete against the “metrics” in the write up?

Once you have taken these necessary steps, consider this statement from the write up:

These results suggest that we could use the high-quality matches of the RWMD to query — in sub-second time — at least 100 million documents using only a modest computational infrastructure.

Image result for speed bump

The path to responsive search and retrieval is littered with multiple speed bumps. Hit any one when going to fast can break the search low rider.

I wish to list some of the speed bumps which the write does not adequately address or, in some cases, acknowledge:

  • Content flows are often in the terabit or petabit range for certain filtering and query operations., One hundred million won’t ring the bell.
  • This is the transform in ETL operations. Normalizing content takes some time, particularly when the historical on disc content from multiple outputs and real-time flows from systems ranging from Cisco Systems intercept devices are large. Please, think in terms of gigabytes per second and petabytes of archived data parked on servers in some countries’ government storage systems.
  • Populating an index structure with new items also consumes time. If an object is not in an index of some sort, it is tough to find.
  • Shaping the data set over time. Content has a weird property. It evolves. Lowly chat messages can contain a wide range of objects. Jump to today’s big light bulb which illuminates some blockchains’ ability house executables, videos, off color images, etc.
  • Because IBM inevitably drags Watson to the party, keep in mind that Watson still requires humans to perform gorilla style grooming before it’s show time at the circus. Questions have to be considered. Content sources selected. The training wheels bolted to the bus. Then trials have to be launched. What good is a system which returns off point answers?

I think you get the idea.

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Crime Prediction: Not a New Intelligence Analysis Function

March 16, 2018

We noted “New Orleans Ends Its Palantir Predictive Policing Program.” The interest in this Palantir Technologies’ project surprised us from our log cabin with a view of the mine drainage run off pond. The predictive angle is neither new nor particularly stealthy. Many years ago when I worked for one of the outfits developing intelligence analysis systems, the “predictive” function was a routine function.

Here’s how it works:

  • Identify an entity of interest (person, event, organization, etc.)
  • Search for other items including the entity
  • Generate near matches. (We called this “fuzzification” because we wanted hits which were “near” the entity in which we had an interest. Plus, the process worked reasonably well in reverse too.)
  • Punch the analyze function.

Once one repeats the process several times, the system dutifully generates reports which make it easy to spot:

  • Exact matches; for example, a “name” has a telephone number and a dossier
  • Close matches; for example, a partial name or organization is associated with the telephone number of the identity
  • Predicted matches; for example, based on available “knowns”, the system can generate a list of highly likely matches.

The particular systems with which I am familiar allow the analyst, investigator, or intelligence professional to explore the relationships among these pieces of information. Timeline functions make it trivial to plot when events took place and retrieve from the analytics module highly likely locations for future actions. If an “organization” held a meeting with several “entities” at a particular location, the geographic component can plot the actual meetings and highlight suggestions for future meetings. In short, prediction functions work in a manner similar to Excel’s filling in items in a number series.

heat map with histogram

What would you predict as a “hot spot” based on this map? The red areas, the yellow areas, the orange areas, or the areas without an overlay? Prediction is facilitated with some outputs from intelligence analysis software. (Source: Palantir via Google Image search)

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The New York Times Wants to Change Your Google Habit

March 1, 2018

Sunday is a slightly less crazy day. I took time to scan “The Case Against Google.” I had the dead tree edition of the New York Times Magazine for February 25, 2018. You may be able to access this remarkable hybridization of Harvard MBA think, DNA engineered to stick pins in Google, and good old establishment journalism toasted at Yale University.

image

The author is a wildly successful author. Charles Duhigg loves his family, makes time for his children, writes advice books, and immerses himself in a single project at a time. When he comes up for air, he breathes deeply of Google outputs in order to obtain information. If the Google fails, he picks up the phone. I assume those whom he calls answer the ring tone. I find that most people do not answer their phones, but that’s another habit which may require analysis.

I worked through the write up. I noted three things straight away.

First, the timeline structure of the story is logical. However, leaving it up to me to figure out which date matched which egregious Google action was annoying. Fortunately, after writing The Google Legacy, Google Version 2.0, and Google: The Digital Gutenberg, I had the general timeline in mind. Other readers may not.

Second, the statement early in the write up reveals the drift of the essay’s argument. The best selling author of The Power of Habit writes:

Within computer science, this kind of algorithmic alchemy is sometimes known as vertical search, and it’s notoriously hard to master. Even Google, with its thousands of Ph.D.s, gets spooked by vertical-search problems.

I am not into arguments about horizontal and vertical search. I ran around that mulberry tree with a number of companies, including a couple of New York investment banks. Been there. Done that. There are differences in how the components of a findability solution operate, but the basic plumbing is similar. One must not confuse search with the specific technology employed to deliver a particular type of output. Want to argue? First, read The New Landscape of Search, published by Pandia before the outfit shut down. Then, send me an email with your argument.

Third, cherry picking from Google’s statements makes it possible to paint a somewhat negative picture of the great and much loved Google. With more than 60,000 employees, many blogs, many public presentations, oodles of YouTube videos, and a library full of technical papers and patents, the Google folks say a lot. The problem is that finding a quote to support almost any statement is not hard; it just takes persistence. Here’s an example:

We absolutely  do not make changes 5to our search algorithm to disadvantage competitors.

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Governance: Now That Is a Management Touchstone for MBA Experts

February 27, 2018

I read “Unlocking the Power of Today’s Big Data through Governance.” Quite a lab grown meat wiener that “unlocking,” “power,” “Big Data,” and “governance” statement is that headline. Yep, IDG, the outfit which cannot govern its own agreements with the people the firm pays to make the IDG experts so darned smart. (For the back-story, check out this snapshot of governance in action.)

Image result for wishful thinking

What’s the write up with the magical word governance about?

Instead of defining “governance,” I learn what governance is not; to wit:

Data governance isn’t about creating a veil of secrecy around data

I have zero idea what this means. Back to the word “governance.” Google and Wikipedia define the word in this way:

Governance is all of the processes of governing, whether undertaken by a government, market or network, whether over a family, tribe, formal or informal organization or territory and whether through the laws, norms, power or language of an organized society.

Okay, governing. What’s governing mean? Back to the GOOG. Here’s one definition which seems germane to MBA speakers:

control, influence, or regulate (a person, action, or course of events).

The essay drags out the chestnuts about lots of information. Okay, I think I understand because Big Data has been touted for many years. Now, mercifully I assert, the drums are beating out the rhythm of “artificial intelligence” and its handmaiden “algos,” the terrific abbreviation some of the marketing jazzed engineers have coined. Right, algos, bro.

What’s the control angle for Big Data? The answer is that “data governance” will deal with:

  • Shoddy data
  • Incomplete data
  • Off point data
  • Made up data
  • Incorrect data

Presumably these thorny issues will yield to a manager who knows the ins and outs of governance. I suppose there are many experts in governance; for example, the fine folks who have tamed content chaos with their “governance” of content management systems or the archiving mavens who have figured out what to do with tweets at the Library of Congress. (The answer is to not archive tweets. There you go. Governance in action.)

The article suggests a “definitive data governance program.” Right. If one cannot deal with backfiles, changes to the data in the archives, and the new flows of data—how does one do the “definitive governance program” thing? The answer is, “Generate MBA baloney and toss around buzzwords.” Check out the list of tasks which, in my experience, are difficult to accomplish when resources are available and the organization has a can-do attitude:

  • Document data and show its lineage.
  • Set appropriate policies, and enforce them.
  • Address roles and responsibilities of everyone who touches that data, encouraging collaboration across the organization.

These types of tasks are the life blood of consultants who purport to have the ability to deliver the near impossible.

What happens if we apply the guidelines in the Governance article to the data sets listed in “Big Data And AI: 30 Amazing (And Free) Public Data Sources For 2018.” In my experience, the cost of normalizing the data is likely to be out of reach for most organizations. Once these data have been put in a form that permits machine-based quality checks, the organization has to figure out what questions the data can answer with a reasonable level of confidence. Getting over these hurdles then raises the question, “Are these data up to date?” And, if the data are stale, “How do we update the information?” There are, of course, other questions, but the flag waving about governance operates at an Ivory Tower level. Dealing with data takes place with one’s knees on the ground and one’s hands in the dirt. If the public data sources are not pulling the hay wagon, what’s the time, cost, and complexity of obtaining original data sets, validating them, and whipping them into shape for use by an MBA?

You know the answer: “This is not going to happen.”

Here’s a paragraph which I circled in Oscar Mayer wiener pink:

One of the more significant, and exciting, changes in data governance has been the shift in focus to business users. Historically, data has been a technical issue owned by IT and locked within the organization by specific functions and silos. But if data is truly going to be an asset, everyday users—those who need to apply the data in different contexts—must have access and control over it and trust the data. As such, data governance is transforming from a technical tool to a business application. And chief data officers (CDOs) are starting to see the technologies behind data governance as their critical operating environment, in much the same way SAP serves CFOs, and Salesforce supports CROs. It is rare to find an opportunity to build a new system of record for a market.

Let’s look at this low calorie morsel and consider some of its constituent elements. (Have you ever seen wieners being manufactured? Fill in that gap in your education if you have not had the first hand learning experience.)

First, business users want to see a pretty dashboard, click on something that looks interesting in a visualization, and have an answer delivered. Most of the business people I know struggle to understand if the data in their system is accurate and limited expertise to understand the mathematical processes which churn away to display an “answer.”

The reference to SAP is fascinating, but I think of IBM-type systems as somewhat out of step with the more sophisticated tools available to deal with certain data problems. In short, SAP is an artifact of an earlier era, and its lessons, even when understood, have been inadequate in the era of real time data analysis.

Let me be clear: Data governance is a management malarkey. Look closely at organizations which are successful. Peer inside their data environments. When I have looked, I have seen clever solutions to specific problems. The cleverness can create its own set of challenges.

The difference between a Google and a Qwant, a LookingGlass Cyber and IBM i2, or Amazon and Wal-Mart is not Big Data. It is not the textbook definition of “governance.” Success has more to do with effective problem solving on a set of data required by a task. Google sells ads and deals with Big Data to achieve its revenue goals. LookingGlass addresses chat information for a specific case. Amazon recommends products in order to sell more products.

Experts who invoke governance on a broad scale as a management solution are disconnected from the discipline required to identify a problem and deal with data required to solve that problem.

Few organizations can do this with their “content management systems”, their “business intelligence systems,” or their “product information systems.” Why? Talking about a problem is not solving a problem.

Governance is wishful thinking and not something that is delivered by a consultant. Governance is an emergent characteristic of successful problem solving. Governance is not paint; it is not delivered by an MBA and a PowerPoint; it is not a core competency of jargon.

In Harrod’s Creek, governance is getting chicken to the stores in the UK. Whoops. That management governance is not working. So much in modern business does not work very well.

Stephen E Arnold, February 27, 2018

The Next Stage in Information Warfare: Quantum Weaponization

February 20, 2018

We have been tracking the emergence of peer to peer technologies. Innovators have been working to deal with the emergence of next generation mainframe computing architectures like those available from Amazon, Facebook, and Google. The idea is that these new mainframes have popped up a level and are using software to integrate individual computing devices into larger constructs which are command and control systems.

Examples of the innovations can be found in the digital currency sector with the emergence of IOTA like systems. There are other innovation nodes as well; for example, discussed in online publications like Medium, technical fora, and implemented by outfits like Anonymous Portugal.

One of the popular methods used by my former colleagues at Halliburton Nuclear Utility Services was to look at a particular problem. The nuclear engineers would then try to fit the problem into a meta-schema. The idea was that a particular problem in some nuclear applications could not be tackled directly. A nuclear engineer tried to find ways to address the problem without poking the specific issue because once probed, the problem morphed. Hence, the meta-method was more useful.

Here’s a diagram which I think shows one facet of the approach:

See the source image

The idea is to come at a problem in different way. Edward de Bono called it “lateral thinking.” For me, the idea is to pop outside a problem, not in two dimensions, but three or four if time plays a part. Maybe “meta-thining” or “meta-analysis”?

What’s ahead for “the Internet” is what I conceptualize as urban warfare in the online world.

Non-traditional approaches to security, messaging, and data routing will combine to create a computing environment that’s different. Smart software will allow nodes or devices to make local decisions, and then that same smart software will use random message pathways to accomplish a task like routing. The difference between today’s concentrated Internet will be similar to Caesar’s Third Legion engaging in urban warfare. Caesar’s troops have swords; the urban fighters have modern weapons. Not even mighty Caesar can deal with the mismatch in technology.

Several observations:

  1. More robust encryption methods will make timely sense making of intercepted data very, very difficult
  2. Smart software will create polymorphic solutions to what are today difficult problems
  3. The diffusion of intelligent computing devices (including light bulbs) generate data volumes which will be difficult to process for meaningful signals by components not embedded in the polymorphic fabric. (Yes, this means law enforcement and intelligence entities).
  4. The era of the “old” Internet is ending, but the shift is underway. The movement is from a pointed stick to a cellular structure filled with adaptable proteins. The granularity and the “intelligence” of the tiny bits will be fascinating to observe.

In one sense, the uncertainty inherent in many phenomena will migrate into online.

The shift is not inherently “bad.” New opportunities will arise. The shift will have significant impacts, however. Just as the constructs of the industrial age have been reshaped by the “old” Internet, the new polymorphic, quantum-ized Internet will usher in some interesting changes.

Is digital Ebola replicating now, gentle reader?

Stephen E Arnold, February 20, 2018

Big Data, Search, and Artificial Intelligence: Quite a Mash Up

January 29, 2018

I read a consultant-technology mash up marketing essay. The write up is “Big Data and Search: The Time for Artificial Intelligence Is Now.” The write up is mostly jargon. I wonder if the engineer driving the word processing train pushed the wrong button.

Image result for train wreck

Here are the “keywords” I noted in the write up:

Analytics
Artificial intelligence
Big Data
Blockchain
Business action and business use cases
Chatbots
Cognitive (presumably not the IBM which maybe doesn’t work as advertised)
Consumer services
Customer / citizen facing (some government speak too)
Digital assistants
False or biased results (yes, fake news)
Keywords
Machine learning
Natural language processing
Platforms
Real time results
Resources
SQL databases
Search
Transparency
Trust
Video

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Online: Welcome to 1981 and 2018

January 8, 2018

I have been thinking about online. I met with a long-time friend and owner of a consumer-centric Web site. For many years (since 1993, in fact), the site grew and generated a solid stream of revenue.

At lunch, the site owner told me that in the last three years, the revenue was falling. As I listened to this sharp businessperson, I realized that his site had shifted from ads which he and his partners sold to ads provided by automated systems.

From direct control to the ease of automated ad provision created the current predicament: Falling revenue. At the same time, the mechanisms for selling ads directly evolved as well. The shift from many industry events to a handful of large business sector conferences took place. There were more potential customers at these shows, but the attendance shifted from hands-on marketers to people who wanted to make use of online automated sales and marketing systems began to dominate.

image

He said, “In the good old days of 1996, I could go to a trade show and meet people who made advertising and marketing decisions based on experience with print and TV advertising, dealer promotions, and ideas.”

“Now,” he continued, “I meet smart people who want to use methods which rely on automated advertising. When I talk about buying an ad on our site or sponsoring a section of our content, the new generation look at me like I’m crazy. What’s that?”

I listened. What could I say.

The good, old days maybe never existed.

I read “Facebook and Google Are Free. They Shouldn’t Be.” The write up has a simple premise: Users should pay for information.

I am not certain if the write up realizes that paying for online information was the only way to generate revenue from digital content in the past. I know that partners in law firms realize that running queries on LexisNexis and Westlaw have to allocate cash to pay for the digital information about laws, decisions, and cases. For the technical information in Chemical Abstracts, researchers and chemists have to pay as well. Financial data for traders costs money as well.

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SIXGILL: Dark Web Intelligence with Sharp Teeth

December 14, 2017

“Sixgill” refers to the breathing apparatus of a shark. Deep. Silent. Stealthy. SIXGILL offers software and services which function like “your eyes in the Dark Web.”

Based in Netanya, just north of Tel Aviv, SIXGILL offers services for its cyber intelligence platform for the Dark Web. What sets the firm apart is its understanding of social networks and their mechanisms for operation.*

The company’s primary product is called “Dark-i.” The firm’s Web site states that the firm’s system can:

  • Track and discover communication nodes across darknets with the capability to trace malicious activity back to their original sources
  • Track criminal activity throughout the cyber crime lifecycle
  • Operate in a covert manner including the ability to pinpoint and track illegal hideouts
  • Support clients with automated and intelligence methods.

The Dark-i system is impressive. In a walk through of the firm’s capabilities, I noted these specific features of the Dark-i system:

  • Easy-to-understand reports, including summaries of alleged bad actors behaviors with time stamp data
  • Automated “profiles” of Dark Web malicious actors
  • The social networks of the alleged bad actors
  • The behavior patterns in accessing the Dark Web and the Dark Web sites the individuals visit.
  • Access to the information on Dark Web forums.

Details about the innovations the company uses are very difficult to obtain. Based on open source information, a typical interface for SIXGILL looks like this:

Related image

Based on my reading of the information in the screenshot, it appears that this SIXGILL display provides the following information:

  • The results of a query
  • Items in the result set on a time line
  • One-click filtering based on categories taken from the the sources and from tags generated by the system, threat actors, and Dark Web sources
  • A list of forum posts with the “creator” identified along with the source site and the date of the post.

Compared with reports about Dark Web activity from other vendors providing Dark Web analytic, monitoring, and search services, the Dark Web Notebook team pegs s SIXGILL in the top tier of services.

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