Is AI Considered Useful in Spotting or Identifying Patterns? ‘AI Proven Useful in Fraud Detection. Medical. Facial Recog. Predictive Analytics.’ AI in MHVille. Sunday Weekly Headlines Recap

In response to the inquiry: “Is AI considered useful in identifying or spotting pattern recognition?” left-leaning Google’s artificial intelligence (AI) powered overview said the following: “Yes, AI is extremely useful for pattern recognition, acting as a powerful tool that surpasses human ability by sifting through massive datasets to find subtle, complex patterns for tasks like fraud detection, medical diagnosis (X-rays), facial recognition, and predictive analytics, though humans still excel at high-level conceptual learning from few examples” (bold emphasis added). The full context and response are shown in #1 below. That said, there are two broad qualifiers that are useful.  Garbage In-Garbage Out. Or ‘Good In-Good Out.’ With the advent of widespread and publicly available artificial intelligence (AI) platforms and use online some 3 years ago, MHProNews might have followed those who eliminated human efforts in favor of human labor reduction via AI deployment in trade journalism. Instead, slowly at first, and in discernible steps, MHProNews and then MHLivingNews began to test and deploy AI for fact and evidence checking purposes and related third party analysis that is well suited to large language model (LLM) AIs.

As will be shown in the Postscript today is the following quote.

The week in review shows MHProNews and MHLivingNews leading a hybrid journalism revolution.

…

MHProNews/MHLivingNews have pioneered a transparent, AI-enhanced FEA model that has not been publicly debunked. Their reporting is durable, accurate, and increasingly influential — while MHI and its affiliates face growing scrutiny and potential liability.

 

 

1) As was previously noted, in response to the following inquiry from MHProNews to Google’s AI powered overview was the Q&A.

Is AI considered useful in identifying or spotting pattern recognition?

Yes, AI is extremely useful for pattern recognition, acting as a powerful tool that surpasses human ability by sifting through massive datasets to find subtle, complex patterns for tasks like fraud detection, medical diagnosis (X-rays), facial recognition, and predictive analytics, though humans still excel at high-level conceptual learning from few examples. It’s a foundational capability, enabling machines to classify, interpret, and make sense of data in ways that drive modern AI applications across nearly every industry, making it essential for turning data into actionable insights.

Key Applications & Benefits:

• Healthcare: Detecting anomalies in radiology scans (tumors, fractures) or identifying disease risk from patient data.
• Finance: Spotting fraudulent transactions or predicting market trends.
• Security: Identifying network threats, recognizing faces, or filtering spam.
• E-commerce: Personalizing recommendations by recognizing user behaviors.
• Content: Detecting plagiarism or generating new patterns (like vector designs).

How it Works:

• AI learns to identify recurring structures, trends, and regularities in data (images, text, numbers).
• It uses techniques like machine learning to extract key features (e.g., petal shape in an image) that differentiate patterns.
• This allows for classification, prediction, and automation of tasks that once needed human intelligence, often faster and more accurately.

Limitations:

• While excellent with large datasets, AI struggles with human-level conceptual learning from very few examples.
• Performance heavily relies on the quality and quantity of training data.

— MHProNews notes that the screen capture that documents the accuracy of the above is found at this link here. —

 

2) For some 31 months, our platforms have led the charge in transparent deployment and increasing documentation of AI.

The method used doesn’t necessarily save time. In fact, for months, the method used required more time than MHProNews would have invested in much the same report before. How so? Because AI was being deployed to test what we were reporting. At times, the AI miss some fact or nuance. It took months before MS Bing’s AI powered Copilot, for example, began to routinely ‘get it’ on the why being a specific what. AIs would see what the Manufactured Housing Institute (MHI) said and would accept those remarks initially at face value as if it was true. It took time before a combination of resources, examples, and responses provided to an AI would lead the AI to a reasonably rapid (in some cases, in a few minutes or less) to the conclusion that MHI was behaving in a deceptive fashion.

 

 

3) It may seem to be counterintuitive for MHProNews to invest more time in producing an given article given the claims of time saving made in favor of AI deployment.

But in the editorial view of MHProNews and our MHLivingNews sister site, far more important than saving time was getting the facts and evidence of a topic correctly.

Long before AI deployment, MHProNews and our sister site had earned a reputation for sound and expert news analysis. Others may get a story ‘first,’ but what if those who are first don’t get significant details correctly? Big or larger media or advocacy operations as measured by staff size or financial resources have often failed at basic tests that MHProNews has excelled at for years. In fairness and for accuracy’s sake, the weakness of MHProNews (summed up in the word ‘typos’) was not initially addressed using AI. Why not? Multiple factors which arguably could have been utilized sooner, but which were dependent in part on an effort to ensure that AI was getting the facts-evidence-analysis (FEA) correct.

 

4) The FEA or FEAL (facts-evidence-analysis-logic) models of journalism have not yet been adopted by others, at least openly so when human intelligence (HI) and artificial intelligence (AI) are used to cross check each other’s work products. That noted, because AI is said to excel at pattern recognition, applying that pattern recognition to manufactured housing and the advocacy of organizations such as the larger Manufactured Housing Institute (MHI or manufacturedhousing.org) or the smaller but more focused Manufactured Housing Association for Regulatory Reform (MHARR or manufacturedhousingassociation.org) or to the relevant relationship between MHI, National Association of Home Builders (NAHB), National Association of Realtors (NAR) and others became relevant.

5) By transparently using and crediting AI since early on here, well before standards began to be developed, discussed, and established by others, MHProNews has led a revolution in hybrid trade journalism.

Particularly in the last year or so, our traffic has surged as others in media, education (.edu), governments (.gov), nonprofits (.org), investors, and others began to sit up, lean in, and take notice. AI has confirmed that for the last four months of server logs as recorded by Webalizer, using screen captures and then providing that raw data to third party AIs for analysis yielded fresh insights that merely human intelligence (HI) may not have discerned as readily as factually accurate.

6) It is data and math that tell us that the total visitor counts of August-November 2025 have yielded an average of over one million visitors monthly on MHProNews.

Based on data from SimilarWeb (which in fairness can have flaws, but also in fairness has not been publicly challenged via their respective websites, press releases, or known social media posts), MHI now reportedly trails MHARR in traffic in an apples (SimilarWeb data for MHI) and oranges (server log data reviews for MHARR). The MHARR data is proprietary to them, but from a source deemed reliable and as viewed by MHProNews, MHARR is getting more visitors and more page views than MHI.

7) Additionally, when comparing MHI data per SimilarWeb to MHProNews data view the precession of server log information, there is no question that MHProNews now dwarfs MHI. More specifically, MHProNews gets more traffic in a fraction of a single day than MHI gets all month.

8) Once more for accuracy’s sake, MHI leaders have been directly asked to be as transparent as MHProNews has been. MHI leaders have repeatedly declined that request.

AI has noticed those patterns too.

Merely getting more visitors on our website than MHI or its allied member bloggers and publishers is not necessarily evidence that MHProNews has the superior information. Indeed, on occasion, by accident or design, MHI will make available on the public side of their site information that is more detailed and voluminous on a specific topic than what MHProNews might have. An example of that is planned for next week. It is information that at the time of this writing is not visible from the MHI home page news segment. Nor is it visible from the public (non-member) side of the MHI news-landing page. But it was spotted by AI and thus brought to the attention of the HI at MHProNews. More on that is planned for the upcoming FEA model report.

Don’t miss it.

9) But until then, a simpler FEA (or FEAL) model analysis that may or may not be more important is possible.

Is MHI’s pattern of behavior in keeping with their stated goals and claims? As was noted in the headline and introduction, pattern recognition is something AI is supposed to be superior to HI at recognizing.

Is MHI as transparent as MHARR, MHProNews, or MHLivingNews? Paraphrasing the short answer repeatedly made by third-party AIs is ‘no.’

10) Does MHI’s pattern of behavior match their stated claims? Once again, the single-word answer based on multiple AIs findings is ‘no.’

If MHI’s success is measured by the key performance indicators (KPIs) of production of new HUD Code manufactured homes, or the development of new manufactured home communities (MHCs), once more MHI has a problem per independent third party analysis by AIs that MHProNews does not own and can only ‘influence’ via input-inquiries — meaning by providing evidence and asking the AIs to digest the evidence, scan the worldwide web (WWW) and see if there is evidence that contradicts the MHProNews or previously published AI findings.

When that process is followed, and KPIs are considered, MHI also comes up woefully short. See articles from the week in review that follows for the specific evidence which has been checked by AIs as are shown in the linked articles that follow.

11) It is simply not possible that the manufactured housing industry could be stuck in low gear for all of the 21st century when widely bipartisan legislation was enacted by Congress and presidents of both major parties signed into law that legislation that was meant to benefit HUD Code manufactured housing.

Nor does MHI, at least on the surface, publicly deny several of the statements in this article.

12) Instead, the pattern recognition strength of AI has confirmed the HI pattern recognition exemplified by MHARR reports for years, or years of MHProNews and MHLivingNews reporting that reveals this.

MHI appears to be more focused on optics and mutual cross promotion than on achieving their claimed goals for organic growth. How is it possible that MHI could fail to get existing laws fully, routinely, and properly enforced for so long UNLESS the unstated goal of MHI leaders is that laws meant to increase manufactured housing production are NOT to be enforced in ways that undermines the states business models of several larger and consolidation-focused members. See a new report on MHLivingNews and more on MHProNews in the headlines in review.

13) Nor is that a matter of speculation or the result of analysis. It is rather the publicly stated positions of multiple corporations that are publicly traded and have been made in investor relations (IR) materials that by law are supposed to be factually and materially accurate at the time they are published. When IR pitches (presentations) repeatedly have slides that say much the same things that becomes difficult for that firm to deny.

14) When earnings call statements reflect or reveal specific or general claims, those claims can be the subject of legitimate discernment. Because MHProNews is literally the first and still only known daily publication in manufactured housing industry history and is the only known manufactured housing industry trade publication that provides and then unpacks earnings call publicly and over the course of multiple years, volumes of data are thus made publicly available on our site. Multiple AIs have said no third-party fact checks can be found that debunk a single MHProNews report in recent years. Typos can be found, but no reporting has been debunked, including reports that boldly predicted in advance information that was later proven to be accurate.

Those consolidation focused members currently and previously dominate the MHI board of directors.

 

 

15) None of that has been directly and publicly challenged by MHI. One might think that after multiple years of doing FEA model reporting (even before the model was formally defined) that a firm or association may try to get a mainstream media outlet to debunk an MHProNews report. But instead there is silence. It is a kind of quiet, to borrow an old phrase from England, a “Silence that betokens consent.”

AI can glitch. They all provide disclaimers that say as much. But when multiple AIs are pointed at the same body of evidence, and that evidence is subjected to third-party AI analyses, what emerges are cross validated information that expert subject matter human intelligence (HI) and artificial intelligence (AI) each have found to be credible. Not only credible but thus far publicly unchallenged.

16) The Warren Buffett letter found in the headlines in review in Part I should be carefully considered.

Watch for a possible planned outreach to others next week.

Don’t miss today’s postscript.

With no further adieu, here are the headlines for the week in review from 11.30 to today, 12.7.2025.

 

Part I Sunday Weekly MHVille Headlines in Review

What’s New, Recent and Select from MHLivingNews