“Data is not information, information is not knowledge, knowledge is not understanding, understanding is not wisdom.”

— Clifford Stoll

“Getting information off the Internet is like taking a drink from a fire hydrant.”

— Mitch Kapor

“We are drowning in information but starved for knowledge.”

— John Naisbitt

“Information is the resolution of uncertainty.”

— Claude Shannon

“True genius resides in the capacity for evaluation of uncertain, hazardous, and conflicting information.”

— Winston Churchill

“The control of information is something the elite always does, particularly in a despotic form of government. Information, knowledge, is power. If you can control information, you can control people.”

— Tom Clancy

“Everybody gets so much information all day long that they lose their common sense.”

— Gertrude Stein

“Technology is so much fun but we can drown in our technology. The fog of information can drive out knowledge.”

— Daniel J. Boorstin

“An educated person is one who has learned that information almost always turns out to be at best incomplete and very often false, misleading, fictitious, mendacious – just dead wrong.”

— Russell Baker

“Journalism has changed tremendously because of the democratization of information. Anybody can put something up on the Internet. It’s harder and harder to find what the truth is.”

— Robert Redford



Back in the good old days before computers, getting information was hard. Books, libraries, newspapers, magazines, seminars, radio, even TV were primary sources. Today, a cell phone can deliver more information in a few minutes than can be digested and used in days. But much of what is available is false or misleading. Can AI help, or just make things worse?

Having so much information available on almost everything is a real blessing. Rather than spending huge amounts of time finding source material and reproducing it, we can now spend very little time on collecting information and devote the bulk of our time to understanding it and using it. Doesn’t get any better than this, yes?

As always in reality, whatever and wherever this might be, a new difficulty has emerged to replace the former:

> Too much information – mostly of unknown reliability; hard to extract the best information or even to determine what “best” means

> Misinformation – false or inaccurate information; getting the facts wrong (errors); relying upon “experts” for validation

> Disinformation – false information deliberately intended to mislead; intentionally misstating the facts

What we need, pretty clearly, is some kind of smart machinery that can handle the nasty job of sorting out the good and useful from the bad and damaging. Smart machinery that we now have! Artificial intelligence (AI) to the rescue! We just knew that AI had to be good for something.

But before getting into the role of AI in handling our current information over-mis-dis problem, it might be worth trying to be clear on just what “information” is and is not.

Data – mistaken for, or pretending to be – information

What is “information”? My working definition is that data becomes information when I can put it to some use. Information is useful data.

But, data that may be useful to me, and therefore information, may not be useful to others. For them, it remains just data.

This means that information is highly subjective. But the distinction here goes much deeper. It also depends upon beliefs, and often on a person’s existing knowledge.

I may see a certain set of data as specifically useful – as information – in a particular situation or use. You may see the same data as questionable – as not-information – based on your beliefs, or prior knowledge, or intended use. Another person may see the data as confirming what they already believe, or know, or think they know. All looking at the same set of data.

This is certainly  inconvenient, yes?

Managing information means managing data

Information overload may well be in many cases simply a result of weak information management. We allow receipt of too much data that is not useful to us for some reason. Our incoming data flow, in which we might find both useful and useless data sets, is not examined sufficiently but mostly accepted. Well, at least until one’s head explodes, as happens much too often these days.

In practical terms, one’s “head exploding” results in decision-making impairment, or even temporary decision-prevention entirely. We may be unable to act as needed. But trying to examine data flows that have become torrents and cloudbursts is far from easy. It is often very hard work. Who needs more hard work.

So, we turn to our collection of trusted people and sources for guidance and assistance in choosing what data to accept and what to reject or ignore. This can often mislead us, but we may decide the risk is worth the hassle-reduction obtained. Or we may simply get onboard with what these trusted people and sources are pitching. We may become true believers in the process.

Who has the time, patience, or inclination to go through the burdensome process of figuring out what pieces of the incoming data deluge, for each of us, may be potentially useful. Mostly analysts and the like who have a reasonably clear understanding of what they are looking for and how they intend to use it. Others may simply remain perpetually overwhelmed and poorly informed.

Today’s huge overabundance of incoming data (with any information that may be useful for us typically obscured) opens the process to a whole host of misuses and disuses, intentional and otherwise. The variety of misuses and disuses grows by the day. But most troubling is the increasing sophistication of these by the usual suspects.

Edward Bernays really got the ball rolling here with propaganda

While misuse and disuse of information has been with us since we humans were invented, their reach and power was limited by lack of technology. Paper and voice have severe limitations when you want to reach great numbers of people, quickly.

Bernays (1891-1995), an American, was a pioneer in the field of public relations and propaganda, and was labelled as “the father of public relations”. During his lifetime, the technology of communications developed explosively. Electronics, computing, and new transmission media allowed creative folks like Bernays to turn information into powerful means of persuasion and control for purposes both good and bad. His mother Anna Freud was psychoanalysis inventor Sigmund Freud’s sister, in case that bit of information might explain anything useful for you here.

Propaganda is communication that is primarily used to influence or persuade an audience to further an agenda, which may not be objective and may be selectively presenting facts to encourage a particular synthesis or perception, or using loaded language to produce an emotional rather than a rational response to the information that is being presented.” — Wikipedia

Although propaganda uses of information have always existed, Bernays turbocharged and expanded the process. So much of our information overload comes from sources of both good and bad propaganda. The same information may be regarded by some recipients as useful and good, while others may see it as harmful and bad.

Information itself is neutral. Only its uses, and consequently its creators and users, can make it into forms that are good, bad, or simply unwanted overload.

Edward Bernays, from his 1928 book Propaganda
Edward Bernays, from his 1928 book Propaganda

A few types of information based on sources and uses

It appears that information, while being neutral in itself, gains its nature or character from its sources and uses. Here are a few examples:

Trusted sources.
Because “trust” is personal and largely subjective, trusted sources are simply information sources that each of us trusts enough to believe (in some manner) and to use. Sources become trusted in general through repeated use and evaluation of results. Like all kinds of trust, trust once betrayed can rarely be recovered.

Experts claim.
Here, the source is typically unnamed but given the label “expert” to make us think that there is a real expert source who actually knows something important. We are asked implicitly to “trust” the information conveyed, without questioning either the information or the real source. All to often, there are no credible experts and the information itself is disinformation, bad.

Confirmation.
Much information today is designed to confirm beliefs rather than to inform. Sources are generally agenda-driven, making the information essentially propaganda, disinformation. This technique probably accounts for a great deal of the repetition that creates and aggravates our information overload.

Examples.
Presenting agenda points, which may be either good or bad depending on our beliefs and needs, as examples in a reporting context helps establish links with those being persuaded. If a well-known or authority figure says or does something that supports the agenda point, it can be reported as “truth” or “fact” even if the source is simply reading a script. Again, a matter of establishing a “trusted source” with as many recipients as possible.

Rants.
Social media as well as much of the media generally features what can only be called “rants” by popular figures. These are highly emotional, and often illogical, verbiage and images intended to connect with others who may feel angry, frustrated, or depressed by whatever may be going on in their worlds. Recipients may not trust the source to any extent, but simply relate to the rant source’s emotion. The emotion, not the information, is what is being communicated.

Points of reference.
These are selected examples addressing a common subject from a variety of sources and views for purposes of discovering where any “truth” may lie.

Bounding boxes and linear spectrum versions.
This is my preferred approach to figuring out, if at all possible, whatever might be going on in a particular situation. The points of reference tend to form either a bounding box, which hopefully contains the “truth”, or more often a simple spectrum (line) between two extremes. Some number of points of reference define the endpoints, or extremes. Truth, if it exists, lies between these. Hopefully.

Tracking changes.
A third piece of my approach to validating, or at least understanding, situation information is to track changes in the points of reference (sources). In many cases, the bounding box or spectrum points of reference move in ways that can provide at least some confirmation of situation hypotheses and dynamics.

At last, the joys of AI in the information wars can be addressed.

Alas, AI seems to be part of the information problem

An example here might be useful. Think about ChatGPT and kin that are “large language models (LLM)”. What’s an LLM? From Wikipedia:

“A large language model (LLM) is a language model notable for its ability to achieve general-purpose language generation. LLMs acquire these abilities by learning statistical relationships from text documents during a computationally intensive self-supervised and semi-supervised training process. LLMs are artificial neural networks, with the largest and most capable LLMs (such as ChatGPT) built with a transformer-based architecture, although other architectures, such as recurrent neural network variants and Mamba (a state space model) are used as well.”

“LLMs can be used for text generation, a form of generative AI, by taking an input text and repeatedly predicting the next token or word. Up to 2020, fine tuning was the only way a model could be adapted to be able to accomplish specific tasks. Larger sized models, such as GPT-3, however, can be prompt-engineered to achieve similar results. They are thought to acquire knowledge about syntax, semantics and ‘ontology’ inherent in human language corpora, but also inaccuracies and biases present in the corpora.”

Got that? No? Well, it gets even worse. What’s a “language model”? Wikipedia again:

“A language model is a probabilistic model of a natural language. In 1980, the first significant statistical language model was proposed … in which potential sources for language modeling improvement were identified by observing and analyzing the performance of human subjects in predicting or correcting text.”

“Language models are useful for a variety of tasks, including speech recognition (helping prevent predictions of low-probability (e.g. nonsense) sequences), machine translation, natural language generation (generating more human-like text), optical character recognition, handwriting recognition, grammar induction,[6] and information retrieval.”

“Large language models, currently their most advanced form, are a combination of larger datasets (frequently using scraped words from the public internet), feedforward neural networks, and transformers. They have superseded recurrent neural network-based models, which had previously superseded the pure statistical models, such as word n-gram language model.”

In other words, ones that even I can understand, LLMs predict word sequences based on how each word has been used by real people in writing real text. LLMs are word-connecting machines, about as far from “intelligence” (even artificial) as you can get. Bah, humbug, as they say somewhere.

OpenAI, which Elon Musk helped to co-found back in 2015, is the San Francisco-based startup that created ChatGPT. The company opened ChatGPT up for public testing in November 2022.
OpenAI, which Elon Musk helped to co-found back in 2015, is the San Francisco-based startup that created ChatGPT. The company opened ChatGPT up for public testing in November 2022.

LLM problems

LLM machinery involves “training” in the following sequence: (1) Pre-Training, (2) Instruction Fine-Tuning, (3) Reinforcement from Human Feedback. They are trained until the model can produce something that appears sensible based on whatever information is used in training.

Huge problem: If the training input and tweaking process uses text taken from, shall we say, sources such as agenda-driven material, the generated text will reflect the source agenda and biases. Big surprise here, yes?

These models also require huge amounts of computing power to create, update, and operate. Perhaps quantum computing machinery will fix this problem in time. Perhaps.

Deepfakes generated by AI machinery also mess with information

Information reliability is now being challenged by merging video images of “experts” and “authorities” with statements and information generated for deceptive purposes. See this post for some examples.

Here is where AI seems to be capable of some seriously nasty and powerful deception. AI can create and manipulate video images of trusted people but show them saying whatever the source wants you to hear. Truly scary stuff.

A couple of examples if you aren’t familiar with this new AI-plus-nasty-folk technology:

“> Actor Morgan Freeman via Diep Nep “This is not Morgan Freeman – A Deepfake Singularity” in a 50-second clip:

Concept and deepfake by Bob de Jong. Freeman’s voice is imitated by the incredibly talented Boet Schouwink.”

The real Morgan Freeman, or at least his real face. I think maybe.
The real Morgan Freeman, or at least his real face. I think maybe.

“> Joanna Stern via The Wall Street Journal provides a little more detail on how this works: “I Challenged My AI Clone to Replace Me for 24 Hours”:
https://www.youtube.com/watch?v=t52Bi-ZUZjA&t=0s

The real Joanna Stern, or at least either her clone or her reality.
The real Joanna Stern, or at least either her clone or her reality.

The message here regarding information is that you can no longer trust information from a familiar face in video or image format. The face may be familiar and trusted, but the message spoken may well be deceptive or worse.

So what might one do about managing information in terms of its likely validity and/or trustworthiness?

The answer, at least for me, can quickly get way too messy and time-consuming. I need something simple and fast enough to use daily. My research here turned up nothing much more than “ … yada-yada …” – or something equally useless.

Here is one technique that I use fairly often:

Actions speak louder than words

For almost every piece of information we receive, it is vitally important to check on the match between words and actions. Actions in many cases reflect the true intent of the source, whereas words from that same source may be intended to deceive, misinform, confuse, and persuade.

Not to say that actions can’t have similar mischief involved, but actions of real significance may have serious unintended consequences for both action-source and acted-upon folks. The red flag, if one exists, may lie most likely in a major conflict between actions and words.

If words and actions from a source mostly or completely disagree, then the actions are likely to reflect real intent and its underlying message. Fortunately, many important sources routinely obscure their machinations with misleading or distracting words. Politicians in general of mainly of this persuasion. Also government sources, which are usually strongly political as well. Today, the list of such perpetrators is very long.

Sometimes words have no visible association with actions taken by either the source or the source subject. The words may simply be propaganda, misinformation from error or intent, or disinformation from source biases or deceptive intent. In such cases, the source itself should be considered suspect until it has proven to be both credible and reliable.

Note that this “credible plus reliable” criterion may be used with good and bad information sources. As the eminent philosopher George Carlin stated:

“I’ve set my own rules to live by. The first one is: ‘Never believe anything the government says.’”

His view of government generally is that it both credibly and reliably lies, which is usually bad for normal folks. Sadly, this situation seems far too often to be the case.

My use of this information test is so far pretty much limited to sources that I regard as bad and/or deceptive. My goal is to see what they may be up to in practice – actions – and use that as a replacement or qualifying message. Applications? Politicians generally. The UN, WHO, WEF, and other associated globalists. Government generally.

How might this work? Here are three examples …

Example #1: Government – Employment Situation

January 2024 employment information has just been released by the U.S. Bureau of Labor (BLS). Great news officially: a gain of 353,000 jobs against a consensus forecast of 185,000. Almost doubled. Doesn’t get any better than this, right?

“On the surface, it was an blockbuster jobs report, certainly one which nobody expected. Starting at the top, the BLS reported that in January the US unexpectedly added 353K ‘jobs’ – the most since January 2023 (when the print was 482K compared to 131K) , double the consensus forecast of 185K and more than the highest Wall Street estimate (300K from Natixis). In fact, this was a 4-sigma beat to estimate, unheard of in the past year.”

“Seasonal adjustments” that are mostly fudged, as I wrote about a while back, turned the unadjusted numbers into highlighted numbers that look great. Worse yet, the real numbers show a huge decline in job quality: part-time vs. full-time. Full-time jobs dropped by 97,000, while part-time jobs exploded by 870,000.

Since when is a part-time job anywhere near as good as a full-time job?
Since when is a part-time job anywhere near as good as a full-time job?

So, how does this fit into the topic of managing information? You will likely look at this employment data much more kindly than I do because I have been following the BLS (and BLS-BS) machinations for years:

> I have learned not to trust BLS headline reports. The real story is in the detail but this requires serious digging to find. Zero Hedge among several other sources is one that I have learned to trust.

> I learned long ago not to trust official reports from any government source on employment. Figures are routinely fudged for political purposes.

> This being a major election year, I would expect pretty much all government data on employment (along with a great deal else) to be unreliable and politically driven.

Why should this matter to me? Couldn’t I just ignore such data and spend time on more productive activities? This matters a great deal to me because it tells me that the real economic story is seriously bad. If it were not, the government would not have had to stretch so far to produce a rosy picture. No surprise here, yes?

Example #2: Politics – Presidential Candidates

This situation is so awful that I hesitated very much before deciding to include it here. The importance of what happens in November 2024 could hardly be greater. What follows is my take on things. Yours will almost certainly be very different.

> We have what I regard as a bottom-of-the barrel candidate list. Voting for any of them seems to be a matter of selecting the least-worst. Even this is not an easy choice. They are both well-qualified to be worst-worst.

Biden and Trump.

> With the incredible 2020 election mess in mind, how much worse is 2024 likely to be? Well, it could be a lot worse – even to the extent of having no election at all. Uncivil war kind of situation.

> Why even bother to vote then? Maybe this will be just another selection-election in which votes are conveniently arranged.

Why should this matter to me? As a practical matter, it doesn’t. Whoever the powers-that-be want to win, will win. My vote here won’t change anything. However, there are many down-ticket candidates for all kinds of potentially important positions that I should and will vote for.

Digging into these is a bit of a chore but worth doing in my mind. I don’t very often pick winners, but my vote is more likely to do some good regardless.

Example #3: WHO – Pandemic Treaty

The World Health Organization (WHO) appears, to me at least, to be up to no good with this global and binding “treaty”. A recent post dealt specifically with this, so I won’t repeat the background here. The picture appears to be one of a globalist effort to create a world ruling body of some kind under the WHO’s UN parent.

WHO.

The Pandemic Treaty “… will remove human rights protections currently embedded in the International Health Regulations (IHR), enforce censorship and digital passports, get rid of freedom of speech, require governments to push a single ‘official’ narrative and dictate which drugs should be prescribed in every country. We’re undergoing a soft coup.” — Dr. Meryl Nass, Board-certified internist and biological warfare epidemiologist [Source]

WHO is an agency of the United Nations (UN), which is a leading proponent of One World Everything. Klaus Schwab’s World Economic Forum (WEF) is an active, aggressive partner in these efforts.

> While global coordination of some things for some valid and good reasons can be beneficial generally, these folks are pushing way too hard. They have an especially strong focus on digital IDs for everyone and digital currencies (CBDCs).  

> The WEF’s involvement, if not leadership here, is especially concerning to me. These are the “you will own nothing and be happy” folks, who are quite open about their plans and ambitions.

> The Treaty has a May 2024 deadline for nations to withdraw or file major objections. After this point, the Treaty becomes binding on all remaining UN members (i.e., virtually every nation).

Why should this matter to me? Especially since I can do absolutely nothing about it. The reason I am following this one closely is that it seems likely to prevail, at least for a time – time enough to cause enormous damage, pain, and suffering. And in the quite near-term, such as 2024.

Bottom line:

Information management lessons, for me at least, are basically these three:

#1. Prioritize: I can’t possibly check deeply and regularly into more than a handful of such concerns. This means that I need to be very careful about my information acceptance priorities. These will change over time, but the list will remain short and will include only top-of-concern topics.

#2. Focus on trusted sources:  I depend heavily upon my personal list of “trusted sources”. As these sources continue to prove or improve their reliability, I’ll gain confidence in using what they pass along to replace a good part of my own digging.

#3. Assume all other sources as suspect: AI-generated information mischief in particular is going to get much worse, making increasing numbers of information sources suspect at the very least. Apart from my list of trusted sources, I will probably consider almost every other source as suspect or worse until credibly and reliably proven otherwise.

Related Reading

“The ‘Disease X’ Fear Campaign and the Pandemic Treaty.
There is vast literature on the Pandemic Treaty and its likely consequences. “

“The Pandemic Treaty consists in creating  a global health entity under WHO auspices. It’s the avenue towards ‘Global Governance’ whereby the entire World population of 8 billion would be digitized, integrated into a global digital data bank.”

“All your personal information would be contained in this data bank, leading to the derogation of fundamental human rights as well as the subordination of national governments to dominant financial establishment. “

“The Pandemic Treaty would be tied into the creation of a Worldwide digital ID system. According to David Scripac:”

 “A worldwide digital ID system is in the making. [The aim] of the WEF—and of all the central banks [is] to implement a global system in which everyone’s personal data will be incorporated into the Central Bank Digital Currency (CBDC) network.”

“Peter Koenig describes the underlying process as :
‘an all-electronic ID – linking everything to everything of each individual (records of health, banking, personal and private, etc.).’”

“This scientific breakdown is only the first in a series of highly consequential disclosures made by an industry deep insider who is currently working as a credentialed Radiofrequency/Microwave/Millimeter-Wave Engineer (for 25-plus years).  Hence, this excellent introduction to 5G science is as accurate as the graphic depiction of the effects of 5G technology on the human body which follows.”

“All anti-5G activists are also highly encouraged to read this very simple explanation from an Integrative Health Consultant who treats many clients for EHS (Electro-Hypersensitivity Syndrome): ‘Adverse Effects of Wireless Technology on the Human Bio-electrical Field’”:

“5G propagates into your mouth and nostrils, down your throat and into your lungs. It will also propagate down your ear canals and excite your inner ear, the nerves and that entire region of your brain that is in close proximity to your inner ear.”

“The change that’s now going on is very strongly concentrated in the Arctic. In fact in three respects, it’s not global, which I think is very important. First of all, it is mainly in the Arctic. Secondly, it’s mainly in the winter rather than summer. And thirdly, it’s mainly in the night rather than at the daytime. In all three respects, the warming is happening where it is cold, not where it is hot.”

“The people in Greenland love it. They tell you it’s made their lives a lot easier. They hope it continues. I am not saying none of these consequences are happening. I am just questioning whether they are harmful.”

“A lot of these things are not anything to do with human activities. Take the shrinking of glaciers, which certainly has been going on for 300 years and has been well documented. So it certainly wasn’t due to human activities, most of the time. There’s been a very strong warming, in fact, ever since the Little Ice Age, which was most intense in the 17th century. That certainly was not due to human activity.”

Trusted Source: Tucker Carlson: Tucker Carlson’s Exclusive Interview with Vladimir Putin in Moscow – Full Transcript. February 6, 2024. Source
Trusted Source: Tucker Carlson: Tucker Carlson’s Exclusive Interview with Vladimir Putin in Moscow – Full Transcript. February 6, 2024. Source.