Newton and Shoulders of Giants? Baloney. Is It Everyday Theft?

January 31, 2023

Here I am in rural Kentucky. I have been thinking about the failure of education. I recall learning from Ms. Blackburn, my high school algebra teacher, this statement by Sir Isaac Newton, the apple and calculus guy:

If I have seen further, it is by standing on the shoulders of giants.

Did Sir Isaac actually say this? I don’t know, and I don’t care too much. It is the gist of the sentence that matters. Why? I just finished reading — and this is the actual article title — “CNET’s AI Journalist Appears to Have Committed Extensive Plagiarism. CNET’s AI-Written Articles Aren’t Just Riddled with Errors. They Also Appear to Be Substantially Plagiarized.”

How is any self-respecting, super buzzy smart software supposed to know anything without ingesting, indexing, vectorizing, and any other math magic the developers have baked into the system? Did Brunelleschi wake up one day and do the Eureka! thing? Maybe he stood on line and entered the Pantheon and looked up? Maybe he found a wasp’s nest and cut it in half and looked at what the feisty insects did to build a home? Obviously intellectual theft. Just because the dome still stands, when it falls, he is an untrustworthy architect engineer. Argument nailed.

The write up focuses on other ideas; namely, being incorrect and stealing content. Okay, those are interesting and possibly valid points. The write up states:

All told, a pattern quickly emerges. Essentially, CNET‘s AI seems to approach a topic by examining similar articles that have already been published and ripping sentences out of them. As it goes, it makes adjustments — sometimes minor, sometimes major — to the original sentence’s syntax, word choice, and structure. Sometimes it mashes two sentences together, or breaks one apart, or assembles chunks into new Frankensentences. Then it seems to repeat the process until it’s cooked up an entire article.

For a short (very, very brief) time I taught freshman English at a big time university. What the Futurism article describes is how I interpreted the work process of my students. Those entitled and enquiring minds just wanted to crank out an essay that would meet my requirements and hopefully get an A or a 10, which was a signal that Bryce or Helen was a very good student. Then go to a local hang out and talk about Heidegger? Nope, mostly about the opposite sex, music, and getting their hands on a copy of Dr. Oehling’s test from last semester for European History 104. Substitute the topics you talked about to make my statement more “accurate”, please.

I loved the final paragraphs of the Futurism article. Not only is a competitor tossed over the argument’s wall, but the Google and its outstanding relevance finds itself a target. Imagine. Google. Criticized. The article’s final statements are interesting; to wit:

As The Verge reported in a fascinating deep dive last week, the company’s primary strategy is to post massive quantities of content, carefully engineered to rank highly in Google, and loaded with lucrative affiliate links. For Red Ventures, The Verge found, those priorities have transformed the once-venerable CNET into an “AI-powered SEO money machine.” That might work well for Red Ventures’ bottom line, but the specter of that model oozing outward into the rest of the publishing industry should probably alarm anybody concerned with quality journalism or — especially if you’re a CNET reader these days — trustworthy information.

Do you like the word trustworthy? I do. Does Sir Isaac fit into this future-leaning analysis. Nope, he’s still pre-occupied with proving that the evil Gottfried Wilhelm Leibniz was tipped off about tiny rectangles and the methods thereof. Perhaps Futurism can blame smart software?

Stephen E Arnold, January 31, 2023

The Failure of Search: Let Many Flowers Bloom and… Die Alone and Sad

November 1, 2022

I read “Taxonomy is Hard.” No argument from me. Yesterday (October 31, 2022) I spoke with a long time colleague and friend. Our conversations usually include some discussion about the loss of the expertise embodied in the early commercial database firms. The old frameworks, work processes, and shared beliefs among the top 15 or 20 for fee online database companies seem to have scattered and recycled in a quantum crazy digital world. We did not mention Google once, but we could have. My colleague and I agreed on several points:

  • Those who want to make digital information must have an informing editorial policy; that is, what’s the content space, what’s included, what’s excluded, and what problem does the commercial database solve
  • Finding information today is more difficult than it has been our two professional lives. We don’t know if the data are current and accurate (online corrections when publications issue fixes), fit within the editorial policy if there is one or the lack of policy shaped by the invisible hand of politics, advertising, and indifference to intellectual nuances. In some services, “old” data are disappeared presumably due to the cost of maintaining, updating if that is actually done, and working out how to make in depth queries work within available time and budget constraints
  • The steady erosion of precision and recall as reliable yardsticks for determining what a search system can find within a specific body of content
  • Professional indexing and content curation is being compressed or ignored by many firms. The process is expensive, time consuming, and intellectually difficult.

The cited article reflects some of these issues. However, the mirror is shaped by the systems and methods in use today. The approaches pivot on metadata (index terms) and tagging (more indexing). The approach is understandable. The shift to technology which slash the needed for subject matter experts, manual methods, meetings about specific terms or categories, and the other impedimenta are the new normal.

A couple of observations:

  1. The problems of social media boil down to editorial policies. Without these guard rails and the specialists needed to maintain them, finding specific items of information on widely used platforms like Facebook, TikTok, or Twitter, among others is difficult
  2. The challenges of processing video are enormous. The obvious fix is to gate the volume and implement specific editorial guidelines before content is made available to a user. Skipping this basic work task leads to the craziness evident in many services today
  3. Indexing can be supplemented by smart software. However, that smart software can drift off course, so specialists have to intervene and recalibrate the system.
  4. Semantic, statistical, or behavior centric methods for identifying and suggesting possible relevant content require the same expert centric approach. There is no free lunch is automated indexing, even for narrow vocabulary technical fields like nuclear physics or engineered materials. What smart software knows how to deal with new breakthroughs in physics which emerge from the study of inter cell behavior among proteins in the human brain?

Net net: Is it time to re-evaluate some discarded systems and methods? Is it time to accept the fact that technology cannot solve in isolation certain problems? Is it time to recognize that close enough for horseshoes and good enough are not appropriate when it comes to knowledge centric activities? Search engines die when the information garden cannot support the buds and shoots of finding useful information the user seeks.

Stephen E Arnold, November 1, 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

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

Text Analytics: Are These Really the Companies to Watch in the Next 12 Weeks?

October 16, 2020

DarkCyber spotted “Top 10 Text Analytics Companies to Watch in 2020.” Let’s take a quick look at some basic details about each firm:

Alkymi, founded in 2017, makes an email indexing system. The system, according to the company’s Web site, “understands documents using deep learning and visual analysis paired with your human in-the-loop expertise.” Interesting but text analytics appears to be a component of a much larger system. What’s interesting is that the business relies in some degree upon Amazon Web Services. The company’s Web site is https://alkymi.io/.

Aylien Ltd., based in Ireland, appears to be a company with text analysis technology. However, the company’s system is used to create intelligence reports for analysts; for example, government intelligence officers, business analysts, and media outlets. Founded in 2010, the company’s Web site is https://aylien.com.

Hewlett Packard Enterprise. The inclusion of HPE was a bit of a surprise. This outfit once owned the Autonomy technology, but divested itself of the software and services. To replace Autonomy, the company developed “Advanced Text Analysis” which appears to be an enterprise search centric system. The service is available as a Microsoft Azure function and offers 60 APIs (which seems particularly generous) “that deliver deep learning analytics on a wide range of data.” The company’s Web site is https://www.hpe.com/in/en/home.html. One product name jumped out: Ezmeral which maybe a made up word.

InData Labs lists data science, AI, AI driven mobile app development, computer vision, machine learning, data capture and optical character recognition, and big data solutions as its services. Its products include face recognition and natural language processing. Perhaps it is the NLP product which equates to text analytics? The firm’s Web site is https://indatalabs.com/ The company was founded in 2014 and operates from Belarus and has a San Francisco presence.

Kapiche, founded in 2016, focuses on “customer insights”. Customer feedback yields insight with “no set up, no manual coding, and results you can trust,” according to the company. The text analytics snaps into services like Survey Monkey and Google Forms, among others. Clients include Target and Toyota. The company is based in Australia with an office in Denver, Colorado. The firm’s Web site is https://www.kapiche.com. The firm offers applied text analytics.

Lexalytics, founded in 2003, was one of the first standalone text analytics vendors. The company’s system allows customers to “tell powerful stories from complex text data.” DarkCyber prefers to learn “stories” from the data, however. In the last 17 years, the company has not gone public nor been acquired. The firm’s Web site is https://www.lexalytics.com/.

MindGap. The MindGap identified in the article is in the business of providing “AI for business.” the company appears to be a mash up of artificial intelligence and “top tier strategy consulting:. That may be true, but we did not spot text analytics among the core competencies. The firm’s clients include Mail.ru, Gazprom, Yandex, and Huawei. The firm’s Web site is https://www.mindgap.dev/. The firm lists two employees on LinkedIn.

Primer has ingested about $60 million in venture funding since it was founded  in 2015. The company ingests text and outputs reports. The company was founded by the individual who set up Quid, another analytics company. Government and business analysts consume the outputs of the Primer system. The company’s Web site is https://primer.ai.

Semeon Analytics, now a unit of Datametrex, provides “custom language and sentiment ontology” services. Indexing and entity extraction, among other NLP modules, allows the system to deliver “insight analysis, rapid insights, and sentiment of the highest precision on the market today.” The Semeon Web site is still online at https://semeon.com.

ThoughtTrace appears to focus on analysis of text in contracts. The firm’s Web site says that its software can “find critical contract facts and opportunities.” Text analytics? Possibly, but the wording suggests search and retrieval. The company has a focus on oil and gas and other verticals. The firm’s Web site is https://www.thoughttrace.com/. (Note that the design of the Web site creates some challenges for a person looking for information.) The company, according to Crunchbase, was founded in 1999, and has three employees.

Three companies are what DarkCyber would consider text analytics firms: Aylien, Lexalytics, and Primer. The other firms mash up artificial intelligence, machine learning, and text analytics to deliver solutions which are essentially indexing and workflow tools.

Other observations include:

  1. The list is not a reliable place to locate flagship vendors; specifically, only three of the 10 companies cited in the article could be considered contenders in this sector.
  2. The text analytics capabilities and applications are scattered. A person looking for a system which is designed to handle email would have to examine the 10 listings and work from a single pointer, Alkymi.
  3. The selection of vendors confuses technical disciplines; for example, AI, machine learning, NLP, etc.

The list appears to have been generated in a short Zoom meeting, not via a rigorous selection and analysis process. Perhaps one of the vendors’ text analytics systems could have been used. Primer’s system comes to mind as one possibility. But that, of course, is work for a real journalist today.

Stephen E Arnold, October 16, 2020

Natural Language Processing: Useful Papers Selected by an Informed Human

July 28, 2020

Nope, no artificial intelligence involved in this curated list of papers from a recent natural language conference. Ten papers are available with a mouse click. Quick takeaway: Adversarial methods seem to be a hot ticket. Navigate to “The Ten Must Read NLP/NLU Papers from the ICLR 2020 Conference.” Useful editorial effort and a clear, adult presentation of the bibliographic information. Kudos to jakubczakon.

Stephen E Arnold, July 27, 2020

Cambridge Analytica: Maybe a New Name and Some of the Old Methods?

December 29, 2019

DarkCyber spotted an interesting factoid in “HH Plans to Work with the Re-Branded Cambridge Analytica to Influence 2021 Elections.”

The new company, Auspex International, will keep former Cambridge Analytica director Mark Turnbull at the helm.

Who is HH? He is President Hakainde Hichilema, serving at this time in Zambia.

The business focus of Auspex is, according to the write up:

We’re not a data company, we’re not a political consultancy, we’re not a research company and we’re not necessarily just a communications company. We’re a combination of all four.—Ahmad *Al-Khatib, a Cairo born investor

You can obtain some information about Auspex at this url: https://www.auspex.ai/.

DarkCyber noted the use of the “ai” domain. See the firm’s “What We Believe” information at this link. It is good to have a reason to get out of bed in the morning.

Stephen E Arnold, December 29, 2019

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