Fuzzifying Data: Yeah, Sure

January 19, 2022

Data are often alleged to be anonymous, but they may not be. Expert companies such as LexisNexis, Acxiom, and mobile phone providers argue that as long as personal identifiers, including names, address, etc., are removed from data it is rendered harmless. Unfortunately data can be re-anonymized without too much trouble. Wired posted Justin Sherman’s article, “Big Data May Not Know Your Name. But It Knows Everything Else.”

Despite humans having similar habits, there is some truth in the phrase “everyone is unique.” With a few white hat or black hat tactics, user data can be traced back to the originator. Data proves to be not only individualized based on a user’s unique identity, but there are also minute ways to gather personal information ranging from Internet search history, GPS logs, and IP address. Companies that want to sell you goods and services purchase the data, but also governments and law enforcement agencies do as well.

There are stringent privacy regulations in place, but in the face of the all mighty dollar and governments bypassing their own laws, it is like spitting in the wind. The scariest fact is that nothing is secret anymore:

“The irony that data brokers claim that their “anonymized” data is risk-free is absurd: Their entire business model and marketing pitch rests on the premise that they can intimately and highly selectively track, understand, and micro target individual people.

This argument isn’t just flawed; it’s also a distraction. Not only do these companies usually know your name anyway, but data simply does not need to have a name or social security number attached to cause harm. Predatory loan companies and health insurance providers can buy access to advertising networks and exploit vulnerable populations without first needing those people’s names. Foreign governments can run disinformation and propaganda campaigns on social media platforms, leveraging those companies’ intimate data on their users, without needing to see who those individuals are.”

Companies and organizations need to regulate themselves, while governments need to pass laws that protect their citizens from bad actors. Self-regulation in the face of dollar signs is like asking a person with sweet tooth to stop eating sugar. However, if governments concentrated on types of data and types of data collection and sharing to regulate rather than a blanket solution could protect users.

Let’s think about the implications. No, let’s not.

Whitney Grace January 19, 2022

Semantics and the Web: A Snort of Pisco?

November 16, 2021

I read a transcript for the video called “Semantics and the Web: An Awkward History.” I have done a little work in the semantic space, including a stint as an advisor to a couple of outfits. I signed confidentiality agreements with the firms and even though both have entered the well-known Content Processing Cemetery, I won’t name these outfits. However, I thought of the ghosts of these companies as I worked my way through the transcript. I don’t think I will have nightmares, but my hunch is that investors in these failed outfits may have bad dreams. A couple may experience post traumatic stress. Hey, I am just suggesting people read the document, not go bonkers over its implications in our thumbtyping world.

I want to highlight a handful of gems I identified in the write up. If I get involved in another world-saving semantic project, I will want to have these in my treasure chest.

First, I noted this statement:

“Generic coding”, later known as markup, first emerged in the late 1960s, when William Tunnicliffe, Stanley Rice, and Norman Scharpf got the ideas going at the Graphics Communication Association, the GCA.  Goldfarb’s implementations at IBM, with his colleagues Edward Mosher and Raymond Lorie, the G, M, and L, made him the point person for these conversations.

What’s not mentioned is that some in the US government became quite enthusiastic. Imagine the benefit of putting tags in text and providing electronic copies of documents. Much better than loose-leaf notebooks. I wish I have a penny for every time I heard this statement. How does the government produce documents today? The only technology not in wide use is hot metal type. It’s been — what? — a half century?

Second, I circled this passage:

SGML included a sample vocabulary, built on a model from the earliest days of GML. The American Association of Publishers and others used it regularly.

Indeed wonderful. The phrase “slicing and dicing” captured the essence of SGML. Why have human editors? Use SGML. Extract chunks. Presto! A new book. That worked really well but for one drawback: The proliferation of wild and crazy “books” were tough to sell. Experts in SGML were and remain a rare breed of cat. There were SGML ecosystems but adding smarts to content was and remains a work in progress. Yes, I am thinking of Snorkel too.

Third, I like this observation too:

Dumpsters are available in a variety of sizes and styles.  To be honest, though, these have always been available.  Demolition of old projects, waste, and disasters are common and frequent parts of computing.

The Web as well as social media are dumpsters. Let’s toss in TikTok type videos too. I think meta meta tags can burn in our cherry red garbage container. Why not?

What do these observations have to do with “semantics”?

  1. Move from SGML to XML. Much better. Allow XML to run some functions. Yes, great idea.
  2. Create a way to allow content objects to be anywhere. Just pull them together. Was this the precursor to micro services?
  3. One major consequence of tagging or the lack of it or just really lousy tagging, marking up, and relying of software allegedly doing the heavy lifting is an active demand for a way to “make sense” of content. The problem is that an increasing amount of content is non textual. Ooops.

What’s the fix? The semantic Web revivified? The use of pre-structured, by golly, correct mark up editors? A law that says students must learn how to mark up and tag? (Problem: Schools don’t teach math and logic anymore. Oh, well, there’s an online course for those who don’t understand consistency and rules.)

The write up makes clear there are numerous opportunities for innovation. And the non-textual information. Academics have some interesting ideas. Why not go SAILing or revisit the world of semantic search?

Stephen E Arnold, November 16, 2021

Exposing Big Data: A Movie Person Explains Fancy Math

April 16, 2021

I am not “into” movies. Some people are. I knew a couple of Hollywood types, but I was dumbfounded by their thought processes. One of these professionals dreamed of crafting a motion picture about riding a boat powered by the wind. I think I understand because I skimmed one novel by Herman Melville, who grew up with servants in the house. Yep, in touch with the real world of fish and storms at sea.

However, perhaps an exception is necessary. A movie type offered some interesting ideas in the BBC “real” news story “Documentary Filmmaker Adam Curtis on the Myth of Big Data’s Predictive Power: It’s a Modern Ghost Story.” Note: This article is behind a paywall designed to compensate content innovators for their highly creative work. You have been warned.

Here are several statements I circled in bright True Blue marker ink:

  • “The best metaphor for it is that Amazon slogan, which is: ‘If you like that, then you’ll like this,'” [Adam] Curtis [the documentary film maker]
  • [Adam Curtis] pointed to the US National Security Agency’s failure to intercept a single terrorist attack, despite monitoring the communications of millions of Americans for the better part of two decades.
  • [Big data and online advertising] a bit like sending someone with a flyer advertising pizzas to
    the lobby of a pizza restaurant,” said Curtis. “You give each person one of those flyers as they come into the restaurant and they walk out with a pizza.  “It looks like it’s one of your flyers that’s done it. But it wasn’t – it’s a pizza restaurant.”

Maybe I should pay more attention to the filmic mind. These observations strike me as accurate.

Predictive analytics, fancy math, and smart software? Ghosts.

But what if ghosts are real?

Stephen E Arnold, April 16, 2021

MIT Deconstructs Language

April 14, 2021

I got a chuckle from the MIT Technology Review write up “Big Tech’s Guide to Talking about AI Ethics.” The core of the write up is a list of token words like “framework”, “transparency”, by design”, “progress”, and “trustworthy.” The idea is that instead of explaining the craziness of smart software with phrases like “yeah, the intern who set up the thresholds is now studying Zen in Denver” or “the lady in charge of that project left in weird circumstances but I don’t follow that human stuff.” The big tech outfits which have a generous dollop of grads from outfits like MIT string together token words to explain what 85 percent confidence means. Yeah, think about it when you ask your pediatrician if the antidote given your child will work. Here’s the answer most parents want to hear: “Ashton will be just fine.” Parents don’t want to hear, “probably 15 out of every 100 kids getting this drug will die. Close enough for horse shoes.”

The hoot is that I took a look at MIT’s statements about Jeffrey Epstein and the hoo-hah about the money this estimable person contributed to the MIT outfit. Here are some phrases I selected plus their source.

  • a thorough review of MIT’s engagements with Jeffrey Epstein (Link to source)
  • no role in approving MIT’s acceptance of the donations. (Link to source)
  • gifts to the Institute were approved under an informal framework (Link to source)
  • for all of us who love MIT and are dedicated to its mission (Link to source)
  • this situation demands openness and transparency (Link to source).

Yep, “framework”, “openness,” and “transparency.” Reassuring words like “thorough” and passive voice. Excellent.

Word tokens are worth what exactly?

Stephen E Arnold, April 14, 2021

Palantir Fourth Quarter Results Surprises One Financial Pundit

February 22, 2021

I read “Palantir Stock Slides As It Posts a Surprise Loss in Fourth Quarter.” The pundit noted:

Palantir stock has been very volatile this year. It is among the stocks that were been pumped by the Reddit group WallStreetBets. Palantir stock had a 52-week high of $45 amid frenzied buying. However, as has been the case with other meme stocks, it is down sharply from its recent highs. Based on yesterday’s closing prices, Palantir stock has lost almost 30% from its 52-week highs. The drawdown is much lower than what we’ve seen in stocks like GameStop and AMC Theatres. But then, the rise in Palantir stock was also not comparable to the massive gains that we saw in these companies.

Yikes. Worse than GameStop? Quite a comparison.

The pundit pointed out:

Palantir has been diversifying itself away from government business that currently accounts for the bulk of its revenues. This year, it has signed many deals that would help it diversify its revenues. Earlier this month, Palantir announced that it has extended its partnership with energy giant BP for five more years.

Who knew that a company founded in 2003 would have difficulty meeting Wall Street expectation? Maybe that IBM deal and the new US president’s administration can help Palantir Technologies meet financial experts’ expectations?

Search and content processing companies have been worn down by long sales cycles, lower cost competitors, and the friction of customization, training, and fiddling with content intake.

Palantir might be an exception. Stakeholders are discomfited by shocks.

Stephen E Arnold, February 22, 2021

Where Did You Say “Put the Semantic Layer”?

February 10, 2021

Eager to add value to their pricey cloud data-warehouses, cloud vendors are making a case for processing analytics right on their platforms. Providers of independent analytics platforms note such an approach falls short for the many companies that have data in multiple places. VentureBeat reports, “Contest for Control Over the Semantic Layer for Analytics Begins in Earnest.” Writer Michael Vizard tells us:

“Naturally, providers of analytics and business intelligence (BI) applications are treating data warehouses as another source from which to pull data. Snowflake, however, is making a case for processing analytics in its data warehouse. For example, in addition to processing data locally within its in-memory server, Alteryx is now allowing end users to process data directly in the Snowflake cloud. At the same time, however, startups that enable end users to process data using a semantic layer that spans multiple clouds are emerging. A case in point is Kyligence, a provider of an analytics platform for Big Data based on open source Apache Kylin software.”

Alteryx itself acknowledges the limitations of data-analysis solutions that reside on one cloudy platform. The write-up reports:

“Alteryx remains committed to a hybrid cloud strategy, chief marketing officer Sharmila Mulligan said. Most organizations will have data that resides both in multiple clouds and on-premises for years to come. The idea that all of an organization’s data will reside in a single data warehouse in the cloud is fanciful, Mulligan said. ‘Data is always going to exist in multiple platforms,’ she said. ‘Most organizations are going to wind up with multiple data warehouses.’”

Kyligence is one firm working to capitalize on that decentralization. Its analytics platform pulls data from multiple platforms in an online analytical processing database. The company has raised nearly $50 million, and is releasing an enterprise edition of Apache Kylin that will run on AWS and Azure. It remains to be seen whether data warehouses can convince companies to process data on their platforms, but the push is clearly part of the current trend—the pursuit of a never-ending flow of data.

Cynthia Murrell, February 10, 2021

Infodemic: Another Facet of Good Old 2020

November 12, 2020

It is difficult to locate non political, non Covid, and non frightening information. I read “Misinformation in the New Normal in a technology publication.” The essay is descriptive; that is, one does not solve a problem or spell out a fix. It’s like a florid passage in James Fennimore Cooper’s novels. There were some factoids in the essay; for example:

According to one piece of research, websites spreading misinformation about the pandemic received nearly half a billion views via Facebook in April alone…

Source? Not stated.

I also noted this statement in the write up:

As defensive measures evolve, so do the attacks, and the further development of deep fake technology is a worrying growth area for misinformation campaigns. Like fake domains, these altered recordings aim to create a veneer of trust in order to seed bad or dangerous information – but deep fakes are now around five years ahead, in technological development terms, of our ability to defend against them.

Five years? That’s another interesting number: 2025. And the lingo like infodemic? Snappy.

I have added the word “infodemic” to my list of interesting neologisms which contain gems like these: neurosymbolic AI, perception hacks, digital detox, and dissonance score.

But the article “Can the Law Stop Internet Bots from Undressing You?” raises another viewpoint about online data; specifically:

For women and men over the age of 18, the production of a sexual pseudo-image of a person is not in itself illegal under international law or in the UK, even if it is produced and distributed without the consent of the person portrayed in the image.

Have government regulators failed? Have educators been unable to impart ethical values to students? Have clever people embraced the methods of some Silicon Valley-type wizards?

Problem solved in 2025?

Stephen E Arnold, November 12, 2020

ThoughtTrace Launches AI Document Comprehension and Management Combo

November 5, 2020

Great idea—Will it work? “ThoughtTrace Unveils the First All-in-One A.I. Document Understanding and Management Platform,” we learn at PR Newswire. The press release explains:

“Today, ThoughtTrace, Inc., the leader in contract and document analytics for asset intensive industries since 2017, announced the official release of their new Document Understanding platform. The new platform combines self-organizing document management with contract analytics and powerful contextual search to discover critical contract data in seconds, condensing weeks of work down to minutes. ‘ThoughtTrace was built to be fundamentally different from both traditional document management and ‘train your own A.I.’ style contract analytics,’ said Nick Vandivere, Chief Executive Officer at ThoughtTrace. ‘With the new platform we are able to completely disrupt traditional approaches to document review that rely on very structured document organization and workflow, and replace that with the ability for the software to actually understand the meaning of the documents being managed. Rather than rigid processes where several different people need to review a document to understand what it says, just ask ThoughtTrace the appropriate question, and it will surface the appropriate results – even for industry specific language, and across thousands to millions of documents.’”

The “appropriate question,” he says. That may be the sticking point for many users. If one can find the magic wording, ThoughtTrace promises to greatly simplify the process of making difficult decisions. We’re told the platform runs on machine learning models tailored to each industry, so no tweaking is required to get started. It can, however, be customized to automate business processes that involve other applications. ThoughtTrace was founded in 1999 and is based in Houston, Texas.

Cynthia Murrell, November 5, 2020

Linear Math Textbook: For Class Room Use or Individual Study

October 30, 2020

Jim Hefferon’s Linear Algebra is a math textbook. You can get it for free by navigating to this page. From Mr. Hefferon’s Web page for the book, you can download a copy and access a range of supplementary materials. These include:

  • Classroom slides
  • Exercise sets
  • A “lab” manual which requires Sage
  • Video.

The book is designed for students who have completed one semester of calculus. Remember: Linear algebra is useful for poking around in search or neutralizing drones. Zaap. Highly recommended.

Stephen E Arnold, October 30, 2020

Content Management: A New Spin

October 27, 2020

What do you get when a young wizard reinvents information management? First, there was records management. Do you know what that was supposed to do? Yep, manage records and know when to destroy them according to applicable guidelines. Next, there was content management. In the era of the Internet, newly minted experts declared that content destined for a Web site had to be management. There were some exciting solutions which made some consultants lots of money; for example, Broadvision/Aurea. Excellent solution. Then there was document management exemplified by companies like Exstream Software which still lives at OpenText as a happy 22 year old solution.) These “disciplines” generated much jargon and handwaving, but most of the chatter sank into data lakes and drowned. Once in a while, like Nessie, an XML/JSON monster emerges and roars, “Success. All your content belong to us.” On the shore of the data lake, eDiscovery vendors shiver in fear. Information management is a scary place.

I read because someone sent me a link, knowing my interest in crazy mid tier consulting speak, to this article: “The Problem with Books of Record and How an EMS Could Help Solve That Problem.” Now here’s the subtitle: “Execution management systems are a new category of software that unlocks value in the hairball of enterprise IT landscapes. Here’s how.”

The acronym EMS means “execution management systems.” Okay. EMS is similar to CMS (content management systems) but with a difference. Execution has a actionable edge. Execution. Get something done. Terminate with extreme prejudice.

Another clarification appears in the write up:

To be a book of record, the data would be in one place, always current and complete. Today’s business systems often have data stored, redundantly, in many places, with many elements incomplete and possibly out of date.

Okay, a book of record and the reference to the existing content chaos which exists in most of these “management” systems.

I am now into new territory. The filing cabinet has yielded to the data lake which suggests dumping everything in one big pool and relying of keywords, Fancy Dan solution like natural language processing, and artificial intelligence to deliver what the person looking for information needs. (The craziness of this approach can be relived by reading about the Google Search Appliance or using an enterprise search system to locate a tweet by a crazed marketer who decided to criticize a competitor after a two hour Zoom meeting followed by a couple of cans of Mountain Dew.)

The write up explains:

Solutions like Celonis’ EMS (execution management) exist because few vendors have focused on all these information handshakes. To create a really efficient business environment, the devil is in the nooks, crannies, handoffs, manual steps, integrations, systems changes, queues, and more. Execution management is about documenting, understanding, integrating, streamlining, optimizing and reengineering how work gets done.  Put simply, Celonis’ tools, in short, document processes, mine what’s happening from the underlying systems to see what kinds of tortured paths are being followed to get work done and then, via benchmarks, best practices and smart automation capabilities, straighten out the flow.

Is this a sales pitch for a company called Celonis?

image

The firm, according to its Web site, is the number one in the execution management system space. I believe everything I read on the Internet.

Several observations:

  • Automation is a hot topic. Hooking information to workflow makes sense.
  • The word choice or attempt at creating awareness with the EMS moniker could be confusing to some. For me, EMS means emergency management solutions.
  • Founded in 2011, Celonis has ingested (according to Crunchbase) more than $300 million in funding. Investors are optimistic and know that the trajectories of FileNet and FatWire are in their future.

The information management revolution continues. At some point, the problem with information in an organization will be solved. On the other hand, it may be one of those approaching infinity thing-a-ma-bobs. You can’t get there from here.

Some corporate executives experience stress when dealing with content and information challenges: Legal discovery, emails with long forgotten data, and references to documents which no longer “exist.”

Net net: Stress can lead to heart attacks. That’s when the real EMS is needed.

Stephen E Arnold, October 27, 2020

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