Tag Archives: artificial intelligence

Four Reasons Why Differentiators Fade Away

I’ve talked ad nauseum about the need for a firm to differentiate itself from its competitors. If your firm engages in “me too” marketing, prospects have no reason to choose you.

But what about companies that DO differentiate themselves…and suddenly stop doing so?

There are four reasons why companies could stop differentiating themselves:

  1. The differentiator no longer exists.
  2. The differentiator is no longer important to prospects.
  3. The market has changed and the differentiator is no longer applicable.
  4. The differentiator still exists, but the company forgot about it.

Let’s look at these in turn.

The differentiator no longer exists

Sometimes companies gain a temporary competitive advantage that disappears as other firms catch up. But more often, the company only pursues the differentiator temporarily.

 In 1985, amid anxiety about trade deficits and the loss of American manufacturing jobs, Walton launched a “Made in America” campaign that committed Wal-Mart to buying American-made products if suppliers could get within 5 percent of the price of a foreign competitor. This may have compromised the bottom line in the short term, but Walton understood the long-term benefit of convincing employees and customers that the company had a conscience as well as a calculator. 

From https://reclaimdemocracy.org/brief-history-of-walmart/.

Now some of you may not remember Walmart’s “Made in America” banners, but I can assure you they were prevalent in many Walmarts in the 1980s and 1990s. Sam Walton’s autobiography even featured the phrase.

From https://www.biblio.com/book/sam-walton-made-america-my-story/d/1389503897.

But as time passed, Walmart stocked fewer and fewer “Made in America” items as customers valued low prices over everything else. And some of the “Made in America” banners in Walmarts in the 1990s shouldn’t have been there:

“Dateline NBC” produced an exposé on the company’s sourcing practices. Although Wal-Mart’s “Made in America” campaign was still nominally in effect, “Dateline” showed that store-level associates had posted “Made in America” signs over merchandise actually produced in far away sweatshops. This sort of exposure was new to a company that had been a press darling for many years, and Wal-Mart’s stock immediately declined by 3 percent. 

From https://reclaimdemocracy.org/brief-history-of-walmart/.

The decline was only temporary as Walmart stock bounced back. And 20 years later, the cycle would repeat as Walmart launched a similar “Made in USA” campaign in 2013, only to run into Federal Trade Commission (FTC) enforcement actions two years later.

The differentiator is no longer important

The Walmart domestic production episodes illustrate something else. If Walmart wanted to, it could have persevered and bought from domestic suppliers, even if the supplier price differential was greater than 5%.

But the buying customers didn’t really care.

Affordability was much more important to buyers than U.S. job creation.

From https://www.tasteofhome.com/collection/secrets-walmart-employees-wont-tell-you/.

So while labor leaders, politicians, and others may have complained about Walmart’s increasing reliance on Chinese goods, the company’s customers continued to do business with Walmart, bringing profitability to the company.

And before you decry the actions of consumers who act against their national self-interest…where was YOUR phone manufactured? China? Vietnam? Unless you own a Librem 5 USA, your phone isn’t from around here. We’re all Commies.

The market has changed

Sometimes the market changes and consumers look at things a little differently.

From https://www.calvert.co.uk/our-history/.

I’ve previously told the story of Mita, and its 1980s slogan “all we make are great copiers.” In essence, Mita had to adopt this slogan because, unlike its competitors, it did NOT have a diversified portfolio.

This worked for a while…until the “document solutions” industry (copiers and everything else) embraced digital technologies. Well, Fuji-Xerox, Ricoh and Konica did. Mita didn’t, and went bankrupt.

The former Mita is now part of Kyocera Document Solutions.

And stand-alone copiers aren’t even offered.

The company forgot

Before Walmart emphasized “Made in America” products, former (and present) stand-up comedian Steve Martin was dispensing tax advice.

“Steve.. how can I be a millionaire.. and never pay taxes?” First.. get a million dollars. Now.. you say, “Steve.. what do I say to the tax man when he comes to my door and says, ‘You.. have never paid taxes’?” Two simple words. Two simple words in the English language: “I forgot!”

From https://tonynovak.com/how-to-be-a-millionaire-and-not-pay-any-taxes/.

While the IRS will not accept this defense, there are times when people, and companies, forget things.

  • I know of one company that had a clear differentiator over most of its competition: the fact that a key component of its solution was self-authored, rather than being sourced from a third party.
  • For a time, the company strongly emphasized this differentiator, casting fear, uncertainty, and doubt against its competitors who depended upon third parties for this key component.
  • But time passes, priorities change, and the company’s website now buries this differentiator on a back page…making the company sound like all its competitors.

But the company has an impressive array of features, so there’s that.

Restore your differentiators

If your differentiators have faded away, or your former differentiators are no longer important, perhaps it’s time to re-emphasize them so that your prospects have a reason to choose you.

Ask yourself questions about why your firm is great, why all the other firms suck, and what benefits (not features) your customers enjoy that the competition’s customers don’t. Only THEN can you create content (or have your content creator do it for you).

Read the book: https://bredemarket.com/7Qs/.

A little postscript: originally I was only going to list three items in this post, but Hana LaRock counsels against this because bots default to three-item lists (see her item 4).

What is B2B Writing?

Business-to-business (B2B) writing isn’t as complex as some people say it is. It may be hard, but it’s not complex.

Why do I care about what B2B writing is?

Neil Patel (or, more accurately, his Ubersuggest service) um, suggested that I say something about B2B writing.

From https://app.neilpatel.com/.

And then he (or it) suggested that I use generative artificial intelligence (AI) to write the piece.

From https://app.neilpatel.com/.

I had a feeling the result was going to suck, but I clicked the “Write For Me” button anyway.

From https://app.neilpatel.com/.

Um, thanks but no thanks. When the first sentence doesn’t even bother to define the acronym “B2B,” you know the content isn’t useful to explain the topic “what is B2B writing.”

And this, my friends, is why I never let generative AI write the first draft of a piece.

So, what IS B2B writing?

Before I explain what B2B writing is, maybe I’d better explain what “B2B” is. And two related acronyms.

  • B2B stands for business to business. Bredemarket, for example, is a business that sells to other businesses. In my case, marketing and writing services.
  • B2G stands for business to government. Kinda sorta like B2B, but government folks are a little different. For example, these folks mourned the death of Mike Causey. (I lived outside of Washington DC early in Causey’s career. He was a big deal.) A B2G company, for example, could sell driver’s license products and services to state motor vehicle agencies.
  • B2C stands for business to consumer. Many businesses create products and services that are intended for consumers and marketed directly to them, not to intermediate businesses. Promotion of a fast food sandwich is an example of a B2C marketing effort.

I included the “B2G” acronym because most of my years in identity and biometrics were devoted to local, state, federal, and international government sales. My B2G experience is much deeper than my B2B experience, and way deeper than my B2C expertise.

Let’s NOT make this complicated

I’m sure that Ubersuggest could spin out a whole bunch of long-winded paragraphs that explain the critical differences between the three marketing efforts above. But let’s keep it simple and limit ourselves to two truths and no lies.

TRUTH ONE: When you market B2B or B2G products or services, you have FEWER customers than when you market B2C products or services.

That’s pretty much it in terms of differences. I’ll give you an example.

  • If Bredemarket promoted its marketing and writing services to all of the identity verification companies, I would target less than 200 customers.
  • If IDEMIA or Thales or GET Group or CBN promoted their driver’s license products and services to all of the state, provincial, and territorial motor vehicle agencies in the United States and Canada, they would target less than 100 customers.
  • If McDonald’s resurrects and promotes its McRib sandwich, it would target hundreds of millions of customers in the United States alone.
From https://www.mcdonalds.com/us/en-us/product/mcrib.html.

The sheer scale of B2C marketing vs. B2B/B2G marketing is tremendous and affects how the company markets its products and services.

But one thing is similar among all three types of writing.

TRUTH TWO: B2B writing, B2G writing, and B2C writing are all addressed to PEOPLE.

Well, until we program the bots to read stuff for us.

This is something we often forget. We think that we are addressing a blog post or a proposal to an impersonal “company.” Um, who works in companies? People.

(Again, until we program the bots.)

Whether you’re marketing a business blog post writing service, a government software system, or a pseudo rib sandwich, you’re pitching it to a person. A person with problems and needs that you can potentially solve.

So solve their needs.

Don’t make it complex.

But what IS B2B writing?

Let’s return to the original question. Sorry, I got off on a bit of a tangent. (But at least I didn’t trail off into musings about “the dynamic and competitive world.”)

When I write something for a business:

  • I must focus on that business and not myself (customer focus). The business doesn’t want to hear my talk about myself. The business wants to hear what I can do for it.
  • I must acknowledge the business’ needs and explain the benefits of my solution to meet the business needs. A feature list without any benefits is just a list of cool things; you still have to explain how the cool things will benefit the business by solving its problem.
  • My writing must address one, or more, different types of people who are hungry for my solution to their problem. (This is what Ubersuggest and others call a “target audience,” because I guess Ubersuggest aims lasers at the assembled anonymous crowd.)
From https://www.hinklefuncenter.com/attractions/laser-tag/.

Again, this is hard, but not complex.

It’s possible to make this MUCH MORE complex and create a 96 step plan to author B2B content.

But why?

So now I’ve answered the question “What is B2B writing?”

Can Bredemarket write for your business? If so, contact me.

Today’s Acronym is RAG (Retrieval-Augmented Generation)

Today’s acronym comes from Maira Ladeira Tanke of Amazon Web Services, who focuses her work on generative AI.

From https://aws.amazon.com/generative-ai/.

She delivered a Thursday presentation entitled “Customizing generative AI applications for your business using your data.” The tool that Tanke uses for customization is Amazon Bedrock, which supports Retrieval-Augmented Generation, or RAG.

Retrieval-Augmented Generation (RAG) is the process of optimizing the output of a large language model, so it references an authoritative knowledge base outside of its training data sources before generating a response. Large Language Models (LLMs) are trained on vast volumes of data and use billions of parameters to generate original output for tasks like answering questions, translating languages, and completing sentences. RAG extends the already powerful capabilities of LLMs to specific domains or an organization’s internal knowledge base, all without the need to retrain the model. It is a cost-effective approach to improving LLM output so it remains relevant, accurate, and useful in various contexts.

From https://aws.amazon.com/what-is/retrieval-augmented-generation/.

Because Amazon has obviously referred to my seven questions—OK, maybe they didn’t—the RAG page devotes time to the “why” question and the “benefits” question.

Amazon identified two problems with large language models, or LLMs (not to be confused with LMMs):

  • LLM responses are unpredictable.
  • LLM data is static.

So what happens when you use LLMs WITHOUT retrieval-augmented generation?

You can think of the Large Language Model as an over-enthusiastic new employee who refuses to stay informed with current events but will always answer every question with absolute confidence.

From https://aws.amazon.com/what-is/retrieval-augmented-generation/.
From https://sourcesofinsight.com/know-it-alls/.

Ouch.

How does RAG solve these problems? It “redirects the LLM to retrieve relevant information from authoritative, pre-determined knowledge sources.” RAG allows you to introduce more current information to the LLM which reduces cost, increases accuracy (and attributes sources), and supports better testing and improvements.

For more technical information, see “What is RAG?” and “Knowledge Bases for Amazon Bedrock.”

From https://aws.amazon.com/what-is/retrieval-augmented-generation/.

(Image sources: Amazon, SourcesOfInsight.com)

Addressing “How” and “Why” in That Order

This is my last chance to squeeze in a Bredemarket blog post before the end of the month, so I’ll just recycle some thoughts that I previously posted on LinkedIn.

Based on some thoughts originally shared by Taylor “Taz” Rodriguez about the perils of “me-too” marketing.

Let’s all be unique

Steve Martin on stage in the 1970s. (And yes I used the “let’s get small” version of this image.) By Jim Summaria – WP:Contact us – Licensing, CC BY-SA 3.0, https://commons.wikimedia.org/w/index.php?curid=5578555

Years ago, Steve Martin had a routine in which he encouraged his audience to say, in unison, that they promise to be different and they promise to be unique.

Get it?

Apparently some present-day marketers don’t, according to Rodriguez.

If you want to SERIOUSLY grow a service-based company, you need to STOP with the generic social media captions!

We see it all day long, even on paid ads which is sad…

❌ “We help our clients stand out from the crowd!”

❌ “Our experienced team of _____ help to elevate your business!”

From https://www.linkedin.com/posts/madebytaz_marketingandadvertising-paidadvertising-socialmediamarketing-activity-7168953109514280960-9H1N/.

No, repeating the canned phrase about standing out from the crowd does NOT make you stand out from the crowd.

But wait. It gets worse.

The authenticity bot

When I reshared Rodriguez’s post, I wanted to illustrate it with an image that showed how many people use the phrase “stand out from the crowd.”

But while I couldn’t get that exact number on my smartphone search (a subsequent laptop search revealed 477 million search results), I got something else: Google Gemini’s experimental generative AI response to the question, bereft of irony just like everything else we’ve encountered in this exercise.

You see, according to Gemini, one way to stand out from the crowd is to “be authentic.”

Yes, Google Gemini really said that.

Google search results, including generative AI results.

Now I don’t know about a bot telling me to “be authentic.”

Rodriguez addresses “how” and “why”

Going back to Taylor “Taz” Rodriguez’s post, he had a better suggestion for marketers. Instead of using canned phrases, we should instead create original answers to these two questions:

HOW do you help your clients stand apart from the competition?

WHY have your past & current clientele chosen to work with you?

From https://www.linkedin.com/posts/madebytaz_marketingandadvertising-paidadvertising-socialmediamarketing-activity-7168953109514280960-9H1N/.

Why not “why” and “how”?

Now I know what my Bredemarket groupies are saying at this point.

Only one of these three groupies will survive. (And I shudder to think about what Bredemarket groupies would wear.) By Mike – Flickr: DSC_0657, CC BY 2.0, https://commons.wikimedia.org/w/index.php?curid=26475397

(There aren’t any Bredemarket groupies, but pretend for the moment that there are.)

Taz, “You’re Doing It Wrong™.” Because Simon Sinek insists that “why” is the most important question, “why” should take precedence over “how.”

From https://blog.hubspot.com/customers/3-takeaways-from-start-with-why

To which I respond:

Sinek Schninek.

BOTH questions are important, both need to be addressed, and it really doesn’t matter which one you address first.

In fact, there are some very good reasons to start with the “how” question in this case. It’s wonderful for the marketer to focus on the question of how they stand apart from the competition.

And as a wildebeest lover who grasps a keyboard with my cold dead hands, and with an onboarding process that ensures Bredemarket’s content is the right content for my customers, I can certainly agree with this focus.

Even if my onboarding process does start with “why.”

My “seven questions” as of January 18, 2024. To see the latest version of the e-book on my seven questions, visit https://bredemarket.com/7qs/.

But hey, if you want to address my first two questions in reverse order, go for it.

Find out more here.

The Double Loop Podcast Discusses Research From the Self-Styled “Inventor of Cross-Fingerprint Recognition”

(Part of the biometric product marketing expert series)

Apologies in advance, but if you’re NOT interested in fingerprints, you’ll want to skip over this Bredemarket identity/biometrics post, my THIRD one about fingerprint uniqueness and/or similarity or whatever because the difference between uniqueness and similarity really isn’t important, is it?

Yes, one more post about the study whose principal author was Gabe Guo, the self-styled “inventor of cross-fingerprint recognition.”

From https://www.linkedin.com/in/gabeguo/.

In case you missed it

In case you missed my previous writings on this topic:

But don’t miss this

Well, two other people have weighed in on the paper: Glenn Langenburg and Eric Ray, co-presenters on the Double Loop Podcast. (“Double loop” is a fingerprint thing.)

From https://doublelooppodcast.com/about-us/.

So who are Langenburg and Ray? You can read their full biographies here, but both of them are certified latent print examiners. This certification, administered by the International Association for Identification, is designed to ensure that the certified person is knowledgeable about both latent (crime scene) fingerprints and known fingerprints, and how to determine whether or not two prints come from the same person. If someone is going to testify in court about fingerprint comparison, this certification is recognized as a way to designate someone as an expert on the subject, as opposed to a college undergraduate. (As of today, the list of IAI certified latent print examiners as of December 2023 can be found here in PDF form.)

Podcast episode 264 dives into the Columbia study in detail, including what the study said, what it didn’t say, and what the publicity for the study said that doesn’t match the study.

Eric and Glenn respond to the recent allegations that a computer science undergraduate at Columbia University, using Artificial Intelligence, has “proven that fingerprints aren’t unique” or at least…that’s how the media is mischaracterizing a new published paper by Guo, et al. The guys dissect the actual publication (“Unveiling intra-person fingerprint similarity via deep contrastive learning” in Science Advances, 2024 by Gabe Guo, et al.). They state very clearly what the paper actually does show, which is a far cry from the headlines and even public dissemination originating from Columbia University and the author. The guys talk about some of the important limitations of the study and how limited the application is to real forensic investigations. They then explore some of the media and social media outlets that have clearly misunderstood this paper and seem to have little understanding of forensic science. Finally, Eric and Glenn look at some quotes and comments from knowledgeable sources who also have recognized the flaws in the paper, the authors’ exaggerations, and lack of understanding of the value of their findings.

From https://doublelooppodcast.com/2024/01/fingerprints-proven-by-ai-to-not-be-unique-episode-264/.

Yes, the episode is over an hour long, but if you want to hear a good discussion of the paper that goes beyond the headlines, I strongly recommend that you listen to it.

TL;DR

If you’re in a TL;DR frame of mind, I’ll just offer one tidbit: “uniqueness” and “similarity” are not identical. Frankly, they’re not even similar.

Will Ferrell and Chad Smith, or maybe vice versa. Fair use. From https://www.billboard.com/music/music-news/will-ferrell-chad-smith-red-hot-benefit-chili-peppers-6898348/, originally from NBC.

Intelligently Writing About Biometrics

Let’s say that your identity/biometric firm has decided that silence ISN’T golden, and that perhaps your firm needs to talk about its products and services.

Silence is not an optimal communication strategy. By Lorelei7, CC BY-SA 3.0, https://commons.wikimedia.org/w/index.php?curid=3164780

For example, let’s say that your firm fights crooks who try to fraudulently use synthetic identities, and you want to talk about your solution.

So you turn to your favorite generative AI tool to write something that will represent your company in front of everyone. What could go wrong?

Battling synthetic identities requires a multi-pronged approach. Layering advanced technology is key: robust identity verification using government-issued IDs and biometrics to confirm a person’s existence, data enrichment and validation from diverse sources to check for inconsistencies, and machine learning algorithms to identify suspicious patterns and red flags. Collaboration is crucial too, from financial institutions sharing watchlists to governments strengthening regulations and consumers practicing good cyber hygiene. Ultimately, vigilance and a layered defense are the best weapons against these ever-evolving digital phantoms.

From Google Bard.

Great. You’re done, and you saved a lot of money by NOT hiring an identity blog writing expert. The text makes a lot of important points, so I’m sure that your prospects will be inspired by it.

Bot-speak is not an optimal communication strategy either. Generated at craiyon.com.

Well…

…until your prospects ask what YOU do and how you are better than every other identity firm out there. If you’re the same as all the other “me too” solutions, then your prospects will just go with the lowest price provider.

So how do you go about intelligently writing about biometrics?

No-siree.

Intelligently writing about biometrics requires that you put all of this information together AND effectively communicate your message…

…including why your identity/biometrics firm is great and why all the other identity/biometric firms are NOT great.

If you’re doing this on your own, be sure to ask yourself a lot of questions so that you get started on the right track.

If you’re asking Bredemarket to help you create your identity/biometric content by intelligently writing about biometrics, I’ll take care of the questions.

Drive content results with Bredemarket Identity Firm Services.

Oh, and one more thing: if you noted my use of the word “no siree” earlier in this post, it was taken from the Talking Heads song “The Big Country.” Here’s an independent video of that song, especially recommended for people outside of North America who may not realize that the United States and Canada are…well, big countries.

From https://www.youtube.com/watch?v=cvua6zPIi7c.

I’m tired of looking out the window of the airplane
I’m tired of traveling, I want to be somewhere

From https://genius.com/Talking-heads-the-big-country-lyrics.

Did the Columbia Study “Discover” Fingerprint Patterns?

As you may have seen elsewhere, I’ve been wondering whether the widely-publicized Columbia University study on the uniqueness of fingerprints isn’t any more than a simple “discovery” of fingerprint patterns, which we’ve known about for years. But to prove or refute my suspicions, I had to read the study first.

My initial exposure to the Columbia study

I’ve been meaning to delve into the minutiae of the Columbia University fingerprint study ever since I initially wrote about it last Thursday.

(And yes, that’s a joke. The so-called experts say that the word “delve” is a mark of AI-generated content. And “minutiae”…well, you know.)

If you missed my previous post, “Claimed AI-detected Similarity in Fingerprints From the Same Person: Are Forensic Examiners Truly ‘Doing It Wrong’,” I discussed a widely-publicized study by a team led by Columbia University School of Engineering and Applied Science undergraduate senior Gabe Guo. Columbia Engineering itself publicized the study with the attention-grabbing headline “AI Discovers That Not Every Fingerprint Is Unique,” coupled with the sub-head “we’ve been comparing fingerprints the wrong way!”

There are three ways to react to the article:

  1. Gabe Guo, who freely admits that he knows nothing about forensic science, is an idiot. For decades we have known that fingerprints ARE unique, and the original forensic journals were correct in not publishing this drivel.
  2. The brave new world of artificial intelligence is fundamentally disproving previously sacred assumptions, and anyone who resists these assumptions is denying scientific knowledge and should go back to their caves.
  3. Well, let’s see what the study actually SAYS.

Until today, I hadn’t had a chance to read the study. But I wanted to do this, because a paragraph in the article that described the study got me thinking. I needed to see the study itself to confirm my suspicions.

“The AI was not using ‘minutiae,’ which are the branchings and endpoints in fingerprint ridges – the patterns used in traditional fingerprint comparison,” said Guo, who began the study as a first-year student at Columbia Engineering in 2021. “Instead, it was using something else, related to the angles and curvatures of the swirls and loops in the center of the fingerprint.” 

From https://www.newswise.com/articles/ai-discovers-that-not-every-fingerprint-is-unique

Hmm. Are you thinking what I am thinking?

What were you thinking?

I’ll preface this by saying that while I have worked with fingerprints for 29 years, I am nowhere near a forensic expert. I know enough to cause trouble.

But I know who the real forensic experts are, so I’m going to refer to a page on onin.com, the site created by Ed German. German, who is talented at explaining fingerprint concepts to lay people, created a page to explain “Level 1, 2 and 3 Details.” (It also explains ACE-V, for people interested in that term.)

Here are German’s quick explanations of Level 1, 2, and 3 detail. These are illustrated at the original page, but I’m just putting the textual definitions here.

  • “Level 1 includes the general ridge flow and pattern configuration.  Level 1 detail is not sufficient for individualization, but can be used for exclusion.  Level 1 detail may include information enabling orientation, core and delta location, and distinction of finger versus palm.” 
  • “Level 2 detail includes formations, defined as a ridge ending, bifurcation, dot, or combinations thereof.   The relationship of Level 2 detail enables individualization.” 
  • “Level 3 detail includes all dimensional attributes of a ridge, such as ridge path deviation, width, shape, pores, edge contour, incipient ridges, breaks, creases, scars and other permanent details.” 

We’re not going to get into Level 3 in this post. But if you look at German’s summary of Level 2, you’ll see that he is discussing the aforementioned MINUTIAE (which, according to German, “enables individualization”). And if you look at German’s summary of Level 1, he’s discussing RIDGE FLOW, or perhaps “the angles and curvatures of the swirls and loops in the center of the fingerprint” (which, according to German, “is not sufficient for individualization”).

Did Gabe Guo simply “discover” fingerprint patterns? On a separate onin.com page, common fingerprint patterns are cited (arch, loop, whorl). Is this the same thing that Guo (who possibly has never heard of loops and whorls in his life) is talking about?

From Antheus Technology page, from NIST’s Appendix B to the FpVTE 2003 test document. I remember that test very well.

I needed to read the original study to see what Guo actually said, and to determine if AI discovered something novel beyond what forensic scientists consider the information “in the center of the fingerprint.”

So let’s look at the study

I finally took the time to read the study, “Unveiling intra-person fingerprint similarity via deep contrastive learning,” as published in Science Advances on January 12. While there is a lot to read here, I’m going to skip to Guo et al’s description of the fingerprint comparison method used by AI. Central to this comparison is the concept of a “feature map.”

Figure 2A shows that all the feature maps exhibit a statistically significant ability to distinguish between pairs of distinct fingerprints from the same person and different people. However, some are clearly better than others. In general, the more fingerprint-like a feature map looks, the more strongly it shows the similarity. We highlight that the binarized images performed almost as well as the original images, meaning that the similarity is due mostly to inherent ridge patterns, rather than spurious characteristics (e.g., image brightness, image background noise, and pressure applied by the user when providing the sample). Furthermore, it is very interesting that ridge orientation maps perform almost as well as the binarized and original images—this suggests that most of the cross-finger similarity can actually be explained by ridge orientation.

From https://www.science.org/doi/10.1126/sciadv.adi0329.

(The implied reversal from the forensic order of things is interesting. Specifically, ridge orientation, which yields a bunch of rich data, is considered more authoritative than mere minutiae points, which are just teeny little dots that don’t look like a fingerprint. Forensic examiners consider the minutiae more authoritative than the ridge detail.)

Based upon the initial findings, Guo et al delved deeper. (Sorry, couldn’t help myself.) Specifically, they interrogated the feature maps.

We observe a trend in the filter visualizations going from the beginning to the end of the network: filters in earlier layers exhibit simpler ridge/minutia patterns, the middle layers show more complex multidirectional patterns, and filters in the last layer display high-level patterns that look much like fingerprints—this increasing complexity is expected of deep neural networks that process images. Furthermore, the ridge patterns in the filter visualizations are all generally the same shade of gray, meaning that we can rule out image brightness as a source of similarity. Overall, each of these visualizations resembles recognizable parts of fingerprint patterns (rather than random noise or background patterns), bolstering our confidence that the similarity learned by our deep models is due to genuine fingerprint patterns, and not spurious similarities.

From https://www.science.org/doi/10.1126/sciadv.adi0329.

So what’s the conclusion?

(W)e show above 99.99% confidence that fingerprints from different fingers of the same person share very strong similarities. 

From https://www.science.org/doi/10.1126/sciadv.adi0329.

And what are Guo et al’s derived ramifications? I’ll skip to the most eye-opening one, related to digital authentication.

In addition, our work can be useful in digital authentication scenarios. Using our fingerprint processing pipeline, a person can enroll into their device’s fingerprint scanner with one finger (e.g., left index) and unlock it with any other finger (e.g., right pinky). This increases convenience, and it is also useful in scenarios where the original finger a person enrolled with becomes temporarily or permanently unreadable (e.g., occluded by bandages or dirt, ridge patterns have been rubbed off due to traumatic event), as they can still access their device with their other fingers.

From https://www.science.org/doi/10.1126/sciadv.adi0329.

However, the researchers caution that (as any good researcher would say when angling for funds) more research is needed. Their biggest concern was the small sample size they used in their experiments (60,000 prints), coupled with the fact that the prints were full and not partial fingerprints.

What is unanswered?

So let’s assume that the study shows a strong similarity between the ridges of fingerprints from the same person. Is this enough to show:

  • that the prints from two fingers on the same person ARE THE SAME, and
  • that the prints from two fingers on the same person are more alike than a print from ANY OTHER PERSON?

Or to use a specific example, if we have Mike French’s fingers 2 (right index) and 7 (left index), are those demonstrably from the same person, while my own finger 2 is demonstrably NOT from Mike French?

And what happens if my finger 2 has the same ridge pattern as French’s finger 2, yet is different from French’s finger 7? Does that mean that my finger 2 and French’s finger 2 are from the same person?

If this happens, then the digital authentication example above wouldn’t work, because I could use my finger 2 to get access to French’s data.

This could get messy.

More research IS needed, and here’s what it should be

If you have an innovative idea for a way to build an automobile, is it best to never talk to an existing automobile expert at all?

Fair use. From https://interestingengineering.com/transportation/bizarre-concept-cars-you-never-even-knew-existed.

Same with fingerprints. Don’t just leave the study with the AI folks. Bring the forensic people on board.

And the doctors also.

Initiate a conversation between the people who found this new AI technique, the forensic people who have used similar techniques to classify prints as arches, loops, whorls, etc., and the medical people who understand how the ridges are formed in the womb in the first place.

By National Museum of Health and Medicine – http://nmhm.washingtondc.museum/exhibits/single_cell/imgs/14_Fetus_3_months.jpg, Public Domain, https://commons.wikimedia.org/w/index.php?curid=9998241

If you get all the involved parties in one room, then perhaps they can work together to decide whether the technique can truly be used to identify people.

I don’t expect that this discussion will settle once and for all whether every fingerprint is unique. At least not to the satisfaction of scientists.

But bringing the parties together is better than not listening to critical stakeholders at all.

By Lorelei7, CC BY-SA 3.0, https://commons.wikimedia.org/w/index.php?curid=3164780

You Can’t Make a Silk Purse Out of an AI-generated Sow’s Ear

By Rictor Norton & David Allen from London, United Kingdom – Show Pig, CC BY 2.0, https://commons.wikimedia.org/w/index.php?curid=43222404

I’m sure that you’ve heard the saying that “you can’t make a silk purse out of a sow’s ear.” Alternative phrases are “putting lipstick on a pig” or “polishing a turd.”

In other words, if something is crappy, you can’t completely transform it into something worthwhile.

Yet we persist on starting with crappy stuff anyway…such as surrendering our writing to generative AI and then trying to fix the resulting crap later.

Which is why I’ve said that a human should ALWAYS write the first draft.

The questionable job description

Mike Harris found a job post asking for a human copyeditor to rework AI-generated content. See the details here.

I’m sure that the unnamed company thought it was a great idea to have AI generate the content…until they saw what AI generated.

Rather than fix the source of the problem, the company has apparently elected to hire someone to rework the stuff.

A human should always write the first draft

Why not have a human write the stuff in the first place..as I recommended last June? Let me borrow what I said before…

I’m going to stick with the old fashioned method of writing the first draft myself. And I suggest that you do the same. Doing this lets me:

  • Satisfy my inflated ego. I’ve been writing for years and take pride in my ability to outline and compose a piece of text. I’ve created thousands upon thousands of pieces of content over my lifetime, so I feel I know what I’m doing.
  • Iterate on my work to make it better. Yes, your favorite generative AI tool can crank out a block of text in a minute. But when I’m using my own hands on a keyboard to write something, I can zoom up and down throughout the text, tweaking things, adding stuff, removing stuff, and sometimes copying everything to a brand new draft and hacking half of it away. It takes a lot longer, but in my view all of this iterative activity makes the first draft much better, which makes the final version even better still.
  • Control the tone of my writing. One current drawback of generative AI is that, unless properly prompted, it often delivers bland, boring text. Creating and iterating the text myself lets me dictate the tone of voice. Do I want to present the content as coming from a knowledgeable Sage? Does the text need the tone of a Revolutionary? I want to get that into the first draft, rather than having to rewrite the whole thing later to change it.

I made a couple of other points in that original LinkedIn article, but I’m…um…iterating. I predict that there’s a time when I WON’T be able to sleep on my text any more, and these days the “generated text” flag has been replaced by HUMAN detection of stuff that was obviously written by a bot.

And that’s more dangerous than any flag.

But if you insist on going the cheap route and outsourcing your writing to a bot…you get what you pay for.

If you want your text to be right the FIRST time…

Drive content results with Bredemarket Technology Firm Services.

Claimed AI-detected Similarity in Fingerprints From the Same Person: Are Forensic Examiners Truly “Doing It Wrong”?

I shared some fingerprint-related information on my LinkedIn feed and other places, and I thought I’d share it here.

Along with an update.

You’re doing it wrong

From https://www.newswise.com/articles/ai-discovers-that-not-every-fingerprint-is-unique.

Forensic examiners, YOU’RE DOING IT WRONG based on this bold claim:

“Columbia engineers have built a new AI that shatters a long-held belief in forensics–that fingerprints from different fingers of the same person are unique. It turns out they are similar, only we’ve been comparing fingerprints the wrong way!” (From Newswise)

Couple that claim with the initial rejection of the paper by multiple forensic journals because “it is well known that every fingerprint is unique” (apparently the reviewer never read the NAS report), and you have the makings of a sexy story.

Or do you?

And what is the paper’s basis for the claim that fingerprints from the same person are NOT unique?

““The AI was not using ‘minutiae,’ which are the branchings and endpoints in fingerprint ridges – the patterns used in traditional fingerprint comparison,” said Guo, who began the study as a first-year student at Columbia Engineering in 2021. “Instead, it was using something else, related to the angles and curvatures of the swirls and loops in the center of the fingerprint.”” (From Newswise)

Perhaps there are similarities in the patterns of the fingers at the center of a print, but that doesn’t negate the uniqueness of the bifurcations and ridge ending locations throughout the print. Guo’s method uses less of the distal fingerprint than traditional minutiae analysis.

But maybe there are forensic applications for this alternate print comparison technique, at least as an investigative lead. (Let me repeat that again: “investigative lead.”) Courtroom use will be limited because there is no AI equivalent to explain to the court how the comparison was made, and if any other expert AI algorithm would yield the same results.

Thoughts?

https://www.newswise.com/articles/ai-discovers-that-not-every-fingerprint-is-unique

The update

As I said, I shared the piece above to several places, including one frequented by forensic experts. One commenter in a private area offered the following observation, in part:

What was the validation process? Did they have a qualified latent print examiner confirm their data?

From a private source.

Before you dismiss the comment as reflecting a stick-in-the-mud forensic old fogey who does not recognize the great wisdom of our AI overlords, remember (as I noted above) that forensic experts are required to testify in court about things like this. If artificial intelligence is claimed to identify relationships between fingers from the same person, you’d better make really sure that this is true before someone is put to death.

I hate to repeat the phrase used by scientific study authors in search of more funding, but…

…more research is needed.

(Pizza Stories) Is Your Firm Hungry for Awareness?

Leftover pizza is the best pizza. Preparation credit: Pizza N Such, Claremont, California. Can I earn free pizza as a powerful influencer? Probably not, but I’ll disclose on the 0.00001% chance that I do.

I wrote a post about pizza that concluded as follows:

Tal’s lead was hungry for ghostwriting services, and when they saw that Tal offered such a service, they contacted him.

What does this mean? I’ll go into that in a separate post.

From (Pizza Stories) The Worst Time to READ a Pizza Post on Social Media.

Now that it’s time to write the “separate post,” I really don’t want to get into the mechanics of how posts that attract prospects (hungry people, target audience) increase awareness and help you convert prospects for your products and services.

So forget that. I’m going to tell a story instead about two executives at a fictional company that has a real problem. The executives’ names are Jones and Smith.

The story

Jones was troubled. Sales weren’t increasing, prospects weren’t appearing, and if this malaise continued the company would have to conduct a second round of layoffs. Jones knew that “rightsizing” would be disastrous, so the company needed another solution.

So Jones videoconferenced Smith and asked, “How can we make 2024 better than 2023?”

Smith replied, “Increasing sales calls could help, and ads could help, but there’s another way to increase our awareness with our prospects. We could create content on our website and on our social channels that spreads knowledge of our products and services.”

Jones exclaimed, “That’s great! We could get generative AI to create content for us!”

“No, not that!” Smith replied. “Generative AI text sounds like a bot wrote it, and makes us sound boring, just like everyone else using generative AI text. Do we want to sound like that and put our prospects to sleep?”

By Ilya Repin – Tretyakov Gallery, Moscow, Public Domain, https://commons.wikimedia.org/w/index.php?curid=60387757

“So we need a human writer,” Jones realized, “one who can describe all of the features of our products.”

“Absolutely not,” Smith emphasized. “Customers don’t care about our features. They care about the benefits we can provide to them. If we just list a bunch of features, they’ll say, ‘So what?'”

By Mindaugas Danys from Vilnius, Lithuania, Lithuania – scream and shout, CC BY 2.0, https://commons.wikimedia.org/w/index.php?curid=44907034

“OK, we’ll go with benefits,” said Jones. “But why is content so important?”

“Take blogging,” replied Smith. “The average company that blogs generates 55% more website visitors. B2B marketers that use blogs get 67% more leads than those who do not. Marketers who have prioritized blogging are 13x more likely to enjoy positive ROI. And 92% of companies who blog multiple times per day have acquired a customer from their blog.”

Data source: Daily Infographic, https://www.dailyinfographic.com/state-of-blogging-industry

“Wow.” Jones was silent for a moment. “How do you know all of this stuff, Smith?”

“Because of the content that I’ve read online from a marketing and writing services company called Bredemarket. The company creates content to urge others to create content. Bredemarket eats its own wildebeest food.”

“Wildebeest?” Jones eyed Smith quizzically.

Black wildebeest. By derekkeats – Flickr: IMG_4955_facebook, CC BY-SA 2.0, https://commons.wikimedia.org/w/index.php?curid=14620744

“Never mind. The important thing is that Bredemarket’s marketing and writing services could help us increase awareness, and vault us over the companies that have blogs but don’t bother to post to them. In one industry, about one-third of the companies with blogs HAVEN’T SAID A SINGLE THING to their prospects and customers in the last two months. If we were in that industry, we could leapfrog over the silent companies.”

By Lorelei7, CC BY-SA 3.0, https://commons.wikimedia.org/w/index.php?curid=3164780

“That sounds great,” said Jones. “Let’s contact Bredemarket today.”

“Wonderful idea, Jones. By the way, I hear that Bredemarket excels at repurposing content also.”

The excited Jones asked Smith to contact Bredemarket, and then walked to a nearby venue and sang a song.

From https://www.youtube.com/watch?v=ifhcWeXIOZs