Fragments 1 of 3 – This Is How We Know Things Before They Happen

I get this question a lot, “How do we know things before they happen?”, for example, it was known that Donald Trump would be president way back in the year 2000. Think about it, that’s 16 years before it happened. How could we possibly know so far ahead?

To make you aware of how we knew, I could just blurt out a simple short sentence with as few as 5 or so words, you would hear it, and that would effectively end the conversation because those 5 words, although correct, would be lacking one very important factor…… “Context”.

con·text    ˈkäntekst/
the circumstances that form the setting for an event, statement, or idea, and in terms of which it can be fully understood and assessed.

When it comes to unstructured information, “Context” is king, but the majority of people look at information from a very different perspective, “Bias”. Most people in the world filter information through pathways of bias, making unstructured data impossible for them to even be aware of, let alone understood once seen or heard.

  • Examples of restrictive bias:
    • Who is the source?
      • Democrat or Republican
      • Fox or MSNBC
      • Priest or Monk
      • Local or Foreign
      • Elite or Street

As you can see above people have built-in, pre-defined biases that they rely on to judge whether or not to even listen or review new information. Some people will walk away based on who provided the information and others may turn away if the information doesn’t support a religious or political viewpoint. So, a large portion of information is not only ignored by most people but to make matters worse since most people only look to certain sources for information, those sources themselves have biases that restrict what information they share and propagate to the masses.

Unstructured data doesn’t follow any of the above mentioned common practices for engaging new information. Unstructured data follows a completely different process known as “Pattern Recognition”. This method eliminates bias as a filter in its entirety and relies solely on confirmation of past successful ties within associated data. In other words, the source is only a source when it has previously supplied information that later turns out to be true and amplifies its viability as a source for any additional information that also proves to be true.

  • Examples:
    • How many correct hits?
      • Did the source provide correct information?
      • How many times was the source correct?
      • How accurate was the information in relation to the event?
    • How many correlating pings?
      • Did any other source have similar information?
      • Are there clusters of events popping up from multiple validated sources?
      • Have we seen this pattern before?

Keep in mind that unstructured data is not restricted to traditional sources, unstructured data is hidden in plain sight, embedded openly within a source not necessarily focused on the topic at hand. It is often presented as a form of fiction, art, poetry or even a reoccurring pattern between multiple media outlets.

This is a difficult concept to accept so we are going to provide you with several examples to review and process. I recommend that you take your time with each one and try to fully understand the implications of each occurrence.

Let’s start with something easy and straightforward, a fictional story called “The Titan”. This is a fictional story written by a man named Morgan Robertson in 1898 called “The Titan”. It’s important to note that this book was written more than a decade before the Titanic disaster. Let’s review the details.


Four key points here; The “Titan”, “Hit an Iceberg”, and “Sank in April”, “Near Midnight”. All four of these hits are obvious direct connections to the Titanic but keep in mind that in 1898 there was no Titanic. What you are seeing on that side by side comparison is a newspaper worthy front page story. A report of what happened to the Titanic 14 years before it happened, years before it was created and years before anyone even had a plan to make the ship. There are actual police reports of crimes with less information than this used to find perpetrators.

This is a fantastic amount of detail to have so far in advance. Since this is not a traditional source of information and is listed as fictional it gets very little, if any, attention. It does not fall within the guidelines of trusted information sources. Think about it, if it had been a trusted source and you had access to it would you have gotten on the Titanic?

Let’s try another example: In 1838 Edgar Allan Poe wrote a fictional Novel called “The Narrative of Arthur Gordon Pym of Nantucket”. He tells a story of 4 men who are stranded out to sea and find themselves without adequate food or water, starving and dehydrated. To survive they begin to drink urine and the blood of a captured sea turtle. To avoid death, “Richard Parker” suggests that they draw straws to decide which one of them should be killed for the others to consume and survive. “Richard Parker” picks the short straw and is cannibalized by his fellow survivors.

46 Years later, four real men traveling from England to Australia found themselves fighting to survive aboard a lifeboat after their yacht was capsized by a huge wave. To survive they begin to drink urine and the blood of a captured sea turtle. To avoid death by starvation they chose to draw straws to decide who should be sacrificed and consumed for the sake of the crew. The crew member who had the shortest straw was a man named “Richard Parker”. He was cannibalized by his fellow survivors.

  • Both: Have 4 crew members
  • Both: Drink urine and the blood of a captured sea turtle
  • Both: Drew straws and then cannibalized a man named Richard Parker

The key point here is “Richard Parker”, Edgar Allan Poe, 46 years earlier, correctly provides details of a horrific situation and the full name of the individual cannibalized within it. This later becomes a world known court case for the survivors and sets nautical morality regulations around the world.

The last example before we get to Donald Trump: A music band is created in the 90’s named “I Am the World Trade Center”. They released a song named “September” on track 11 in the album only a few months before 9/11:

In July 2001, we can literally see the information “September 11th World Trade Center”. Keep in mind this is a music band releasing songs, not predictions.

These are but a few examples I selected out of MANY others that are just as worthy to be in this post. Let’s take a much simpler, honest and straightforward approach by treating the information the same way a machine or computer would treat it, as just data. This will allow us to see ahead of the event and position ourselves in a way that benefits us.

We do this by finding a pattern of success and then expanding the pattern out to see what else it can provide. For instance, we found that a fictional book can provide credible information about future events, so we started looking at other fictional books to see if more connections could be made, and they were. We also started looking at more writings from authors who have previously provided accurate information to see if they had additional direct hits, and they did. We then started looking for information that had not yet come into realization and waited to see if it would one day occur, and it has.

In this final example, we are going to look at Donald Trump winning the election and see if we can articulate how we knew in advance.

To do this we are going to use a popular TV show called “The Simpsons”. Let’s take a close look at the unstructured data and see if there is a pattern that can be recognized.

Simpson Episode In 1994, a fake product showing a logo of an apple with a worm having trouble understanding the voice directions of its owner.

Not only were The Simpsons right about this happening in the future when Siri was first released but they even indicated that Apple would be the company to provide the product.

In the 1990 episode “Two Cars in Every Garage and Three Eyes on Every Fish”, the local nuclear power plant runoff water causes fish to mutate with 3 eyes in the local lake.

In 2011, a three-eyed fish was pulled from a reservoir in Argentina. The reservoir in question was fed by water from a nuclear plant in the province of Córdoba.

Simpson Episode In 1993, Gunter and Ernst were attacked by one of their tigers on stage during an entertainment act.

In 2003 Siegfried and Roy attacked by one of their tigers on stage during an entertainment act.

In an episode in 1997, Marge is trying to cheer up Bart as he lies on his bed. “Would you like to read a book?” she asks as she holds up a book called Curious George and the Ebola virus.
“I already did,” Bart says as he points to an apocalyptic drawing on the wall showing a pile of dead bodies.

In December 2013, an outbreak of the Ebola virus started in Guinea and quickly spread to Liberia and Sierra Leone.
Over the next few years, there were 28,657 reported cases and more than 11,300 deaths.

The episode in 1998, S10 E2 ‘The Wizard of Evergreen Terrace’, Homer Simpson predicted the mass of the Higgs Boson particle

14 Years later, On 4 July 2012, CERN’s Large Hadron Collider announced they had each observed a new particle in the mass region around 126 GeV. The Higgs boson.

The episode in 2012, S23 E10 ‘Politically Inept’, A ticker on a rolling news station that Homer appears on reads “Europe puts Greece on eBay”.

Three years later the government debt meltdown in Greece saw it become the first country to default on an IMF loan repayment and go bankrupt.

Episode: ‘The Simpsons Movie’ A chance remark from Marge chastising Lisa for worrying about spies while on the run sparks an alarm in an NSA building. The building contains thousands of workers listening to private conversations across the country and Marge’s loose lips lead to the family’s arrest.

Six years before Edward Snowden blew the whistle on the true extent of the NSA’s spying on American citizens, with an extensive surveillance network of the National Security Agency. Up until that moment, this was at best a conspiracy to US citizens.

Episode: S25 E16 ‘You Don’t Have to Live Like a Referee’
Homer is visited by the executive vice president of the “world football federation” who wants him to be a referee in the upcoming World Cup. He’s promptly arrested by American authorities for corruption and carted off.

(A Double Hit in One) A year before the football world was turned upside when a series of high-ranking FIFA officials were arrested by the FBI on charges of corruption. The episode even predicted Germany as the winners of that year’s tournament.

In 2005 episode: “The Simpsons” apparently predicted the Super Bowl XLVIII matchup between the Denver Broncos and the Seattle Seahawks.

In 2013, Years later was an American football game between the Conference (AFC) champion Denver Broncos and (NFC) champion Seattle Seahawks to decide the (NFL) champion for the 2013 season.

From a 2009 episode that doesn’t just predict a new Star Wars movie, but it’s competition at the same time. An image in the episode shows two movie posters, both of which were later hanging in movie theaters around the country, showed a new Star Wars movie competing against a new Chipmunks film.

We use the term competing loosely of course, as “The Force Awakens” has set nearly every opening weekend record. Of all the things, it’s an interesting pair of movies to put next to each other. By 2009, the prequel trilogy had been over for years, and there was little discussion beyond fanboy dreams that there would be any future Star Wars films.

The pattern we found in this unstructured data is “Precise One Off’s”. Each bit of data focuses on just a single event in the future. Each event seems somewhat implausible, random or unlikely to transpire but does anyway.

Now that we see the pattern and know how it works let’s look at this hit below.

Donald TrumpThe episode in 2000 S11 E17 ‘Bart To The Future’
Lisa has become President and, in a scene where she addresses her inner circle, she says: “We’ve inherited quite the budget crunch from President Trump.” They then go on to talk about Donald Trump as president multiple more times and the country now being completely broke.
Donald Trump becomes president in 2016

Using the recognized pattern, we have locked onto the moment in the episode that meets the requirements for the correct context to track unstructured information from this source.

Now any good news reporter or analyst would ask very reasonable questions like,

  • “Where are they getting the information”
  • “How did they know”

At some point, you are also going to have people saying things like:

  • “It’s just a coincidence”
  • “Given enough time anyone can get something right”

This is most likely going to be the most important concept that I can share with you on how unstructured data benefits you and will also be the most difficult for most people to follow.

  • “Nothing matters but the data.”
  • “The who and why is irrelevant.”

To help you understand, answer the following question: What is the most relevant bit of information in the following 3 lines in respect to yourself?

  • The lottery numbers for next week will come from a source in Ohio.
  • The lottery numbers can be determined at random given enough tries.
  • The lottery numbers are 199-36-82-12-114

As you can see in the example having the numbers to the next lottery can be extremely advantageous whereas knowing where they come from or the name of the source has no effect on your direct financial future. This is the method we use to pursue unstructured data. We simply identify a valid confirmed source though pattern recognition and then we use that information to get ahead of the event.

I will be posting more information on this site for anyone who wants to take a deeper dive into the world of unstructured data. Let me know if you have any questions and thanks for reading.

Side Note: That episode with the Simpsons about Trump had more information on the impact of his presidency….. I recommend you watch the episode.

Also, note:

  • This is one source with multiple hits that prove to be valid for individually separate subjects or events.
  • Other patterns can be multiple sources, not validated,  but all focused on a single subject or event.
  • Or even a mix of the two patterns.

Categories: My Thoughts Or Experience, Precognitive Unstructured Information, Unstructured DataTags: , ,


  1. Have you ever heard of or read anything by Swiss psychologist Carl Jung?
    Memories, Dreams, Reflections is a partially autobiographical book. I highly recommend!
    Here is a short clip of an interview by BBC:

    Liked by 2 people

  2. Very interesting, he talked about how the only thing we should be worried about is mankind since this is the source of all evil. That is a very interesting way to look at our human condition. I will most definitely be looking into him, thanks.

    Liked by 2 people

  3. I had forgotten that I had previously posted about Carl Jung. Today I read your poem about ghosts which prompted me to share the dream I had. The term, “confirmation bias”, surfacing to my consciousness from time to time without context, has been vouchsafed to me from a source I cannot identify. I’m doing a little bit of study on it now.

    Liked by 1 person

    • Strange that you posted this just as I was working on Part 3 which goes into detail about where the information fragments are coming from. Part 3 is going to provide proof of the source in a way that’s easily understandable and possibly even reachable for the reader.

      It will be a very strange and exciting post.

      Liked by 2 people

  4. That is quite interesting. I had heard of the Simpsons episode predicting a president called Trump before. But, I didn’t know that they talked about all the other stuff before it happened as I don’t watch it.

    Liked by 2 people

  5. Things like this often turned out to be true.
    Nikola Tesla once said that human can talk without any use of mouth if we think at high frequency.
    For ex. He said that one night in sleep he saw many angels are coming down to earth, one of them was his mother and she talked to him.
    Next day he knew, his mother was dead. So ,this proved that it was possible.
    Things like this always exists but we are not at that level to reach that one so quick.

    Liked by 3 people

    • When my dad was in the hospital, the last night of his life – I woke at 3 am and new he needed me. It was February and freezing, I lived a few blocks from the hospital so I got up and dressed up and walked there in the dark and 20 below – UNHEARD of for me to go out in the dark alone in this city at 3 am! When I got there he was in severe agony. I could hear him crying out from down the hall. I glared at the nurses as I passed – they were more than a bit surprised to see me at this hour and I was not impressed that 3 of them were sitting there discussing weight watchers while I could hear my fathers agony down the hall – I knew it was him. I got into the room and started trying to figure out was wrong. The nurse came in behind me and told me it was Sundowner Syndrome or some stupid thing that means old people cry out in agony and distress at night for no reason. WRONG! I found the reason in 2 minutes – his tube from his catheter was wrapped around his thigh and cutting off his circulation. When I released it it screamed in pain as the blood began to flow. I have no doubt he reached out to me in my sleep and called for help. I trust there is a special hell for those charged with caring for others who don’t. He died the next day and while my pain was immense – it was less than the pain of knowing how he had been left in agony on the last night of this earth. We absolutely can communicate across time and space without words or direct communication.

      Liked by 2 people

  6. I had heard about those two books before. I can’t help but think of “The Hunger Games” trilogy. After I read them, I heard a video by David Icke (whom I don’t listen to anymore). Now I wonder if the Hunger Games trilogy was written as a warning.

    Liked by 1 person

  7. Fascinating!! A thought, a statement, a dream or premonition are potential sparks that light future realities or are the future realities already in play and we hear whispers in advance of that which hasn’t been.

    Liked by 2 people

  8. This is an interesting one I ran across a few weeks ago. It has an alter ending though a lot of similarities

    Liked by 1 person

  9. Sometimes life imitates art. So my question is: what distinguishes a prediction from a lucky guess or coincidence? And what ratio of hits to misses in detail is required to establish the former and eliminate the latter?

    1) The Titan:

    – had a displacement of 75,000 tons (The Titanic had a displacement of 52,000 tons)
    – had three small engines and three large ones (Titanic: only had three main engines)
    – had 80 furnaces and 12 boilers (Titanic: 159 furnaces and 29 boilers)
    – had a total output of 75,000 horsepower (Titanic: 46,000 hp)
    – averaged 25 knots on her trial run (Titanic: 18 knots avg. and 23 knots max)
    – used two sails to enhance propulsion (Titanic: had no sails)
    – had 24 lifeboats (Titanic: 20) that could hold 500 people (Titanic: ~1180)
    – could endure the breach of nine of its 19 watertight compartments (Titanic: four of its 16)
    – sunk on her fourth return trip (Titanic: sunk on her maiden voyage)
    – struck another ship the day before hitting the iceberg (Titanic only hit an iceberg)
    – received no advance warnings of drifting ice (Titanic: received advance warnings of drifting ice)
    – hull was punctured by the engines and boilers (Titanic: hull breached by the ice alone)

    So, IMHO, there are more than enough misses to disqualify it as anything other than a circumstantial happenstance. Plus, when asked about it, the author himself admitted to making no real life predictions.

    2) Richard is/was a very common given name. And Parker was (and still is) a very common English surname. So there’s a high probably of many people–past, present and future–being named Richard Parker.

    3) Seeing “September” as the 11th track on an album released in 2001 by a band named “I Am The World Trade Center” might be unsettling in hindsight, but it does not constitute a prediction. And from what I can make out, the lyrics do not speak of impending disaster.

    4) The Simpsons writers joked about Trump becoming president and it just so happens that he became president in real life (notwithstanding the fact that he always held out the possibility of running for president one day). But by way of comparison, has anyone compiled a list of all the Simpsons jokess that did not play out in real life?

    My standard for what qualifies as a legitimate prediction are quite stringent, in that it must forecast an event beyond human control in exacting detail.

    eg. A magnitude 6.9 earthquake will strike Japan 35 km east-southeast of Namie, Fukushima Prefecture at 05:59 JST on 22 November 2016 (20:59 Nov 21 UTC) at a depth of 11.4 km and create a tsunami of 1.4 metres at Sendai, the capital city of Mithagi Prefecture, injuring 15 people and briefly shut down a cooling system at the Fukushima Daini Nuclear Power Plant.

    Liked by 1 person

    • You Asked — “What distinguishes a prediction from a lucky guess or coincidence?”

      My Answer — The definitions.

      You Asked — “And what ratio of hits to misses in detail is required to establish the former and eliminate the latter?”

      My Answer — The amount the observer feels more comfortable with.

      My Response — I understand your response but it doesn’t relate to my post in anyway.

      I deal in unstructured data and algorithms. The ability for machine learning to find useful patterns in unstructured data. The possibility to expand that into AI to find patterns in the financial markets, insurance industry or even a trip out of town (you name it).

      The source of the data is irrelevant. You can give data any name you want (Prediction, Lucky Guess or even Coincidence), but none of those terms mean anything. They are only useful in a human context. Machine learning has already proven that people follow patterns that they are not aware of. We have made huge leaps in the stock market based on unstructured data. Leaps, from a human perspective, that don’t make any sense. We are re-learning what “free choice” is based on data that suggests we follow a set pattern.

      The power of information and technology exceeds your understanding of the human condition.

      The data in human creativity unlocks advantages related to predictability and probability. It doesn’t matter what label you give it… in the end it’s just data.

      How you gain access to the data and how you use it has more value than contemplating it’s origin or even for some, it’s morality.


      • Alright, let’s start with the definitions.

        Data is a a set of raw, unorganized facts, whereas information is a set of organized, structured data presented in a context so as to make it useful.

        So with that in mind, explain how any of the fictional works presented satisfy either of the above definitions from a statistical perspective. Because you cannot make useful predictions from a sample size of one.

        Liked by 1 person

        • You Stated — “Data is a a set of raw, unorganized facts”

          My Response — It is not. Data is simply bits of information that form a pattern. Data is a wave form. Facts are a human interpretation of data, in reality there are no facts just your opinion born from perspective in a mind that is limited to what it knows.

          You Stated — “information is a set of organized, structured data presented in a context so as to make it useful”

          My Response — It is not. Information is an observation of data that is compared to an understanding in the hopes of proving or disproving probable facts. Some people may find it useful while other may not, it all depends on the observer.

          You Stated — “explain how any of the fictional works presented satisfy either of the above definitions from a statistical perspective”

          My Response — Fictional works do not satisfy either definition you provided, especially if you involve statistics.

          Unstructured data does not follow any known set of statistical probabilities, it is simply background noise. It has always been believed that it’s useless data… that is.. until more advanced computers began to mine useless data. The discovery of Big Data has taken us down a road to unstructured data or the understanding that there is no such thing as useless data because there are patterns in the data. Patterns that lead to information that can then be used to predict outcomes.

          Any new science seems like magic but it’s still just data.

          Why? Dunno

          …but I don’t really need to know why it works, I just seek what it provides.


          • Then we’re off to a bad start, because my definitions come directly from hand-written notes taken during a computer science course a few decades ago. And I note that several online sources say more or less the same thing.

            Either way, I think my point stands: one book, or film, or cartoon, or musical recording does not provide a sufficient sample size from which to make a reliable prediction.

            Liked by 1 person

            • You Stated — “Then we’re off to a bad start”

              My Response — A difference of opinion or understanding has no value past learning or rejecting. This is to say, it isn’t good or bad for me, just a conversation.

              You Stated — “my definitions come directly from hand-written notes taken during a computer science course a few decades ago”

              My Response — Update your definitions with one from Forbes Oct 16, 2019

              “Think about any kind of data that doesn’t have a recognizable structure and you have identified an example of unstructured data.” — Forbes

              You Stated — “Either way, I think my point stands: one book, or film, or cartoon, or musical recording does not provide a sufficient sample size from which to make a reliable prediction.”

              My Response — Agreed and I would also note that my post didn’t say that it did. The very nature of unstructured data requires a pool of sources to form a recognizable pattern and thus could not generate from one source (book, song, etc). Your point is interesting but not reflective of anything that I talked about in my post.

              Unstructured data exists, there is no way to deny that fact. Our ability to use machine learning to find useful patterns in unstructured data is also a fact. We do this daily now across the globe. Our ability to make predictions with unstructured data is also a fact due to the fuel of Big Data. The more data we feed it the more useful it becomes.

              Where this becomes an issue for older ways of viewing our reality as consumers or even dreamers is when our useless data like “Fictional Books”, starts to produce useful predictive information.

              At this point in time we don’t know why, but it seems to be just a math problem, one beyond a team of even the best people to solve. The machines mining this data do so on a scale millions of times faster than the entire human race combined.

              We are simply starting to see reality from a more pure math perspective and it seems to be predictable. It would seem math doesn’t care if you believe it or not.


              • By bad start I meant that if we hold to different definitions of the same words we’ll end up talking at cross-purposes and the conversation is guaranteed to falter.

                So let’s begin there. Is there a compelling reason to adopt the Forbes Oct 16, 2019 definition?

                Liked by 1 person

                • You Asked — “. Is there a compelling reason to adopt the Forbes Oct 16, 2019 definition?”

                  My Answer — It matches the same understanding of unstructured data used by Big Data. In respect to machine learning it also lines up.

                  Unstructured Data doesn’t have any complexity in its definition so either of those should suffice.

                  Keep in mind that “ Unstructured Data” that’s mined from “Big Data” by “Data Scientists” is a fact of daily life at this point.

                  We are simply moving into the age of high predictability. You are possibly viewing my post as some kind of magic or psychic power report but in reality it’s more of a math or pattern type post.

                  Your possible assumption is that knowing something before it happens is based on a superhuman power of some type but in reality it could just be patterns of thought that repeat over time. They may be networked or even embedded. Free will that generates new ideas might have less to do with individuality and more to do with how particles pair. (Hard to say) but it’s most likely just math.

                  Perhaps if you told me more precisely what part of the post you are finding most disruptive to your current understanding.


                  • What I object to is your claim that these creative works had any predictive value. Some elements matched, while others did not. Counting the hits while ignoring the misses constitutes confirmation bias.

                    If you disagree, name a condition that would disprove your contention that unstructured data has predictive capabilities.

                    Liked by 1 person

                    • Unstructured data doesn’t focus on hits and misses so that may be where you are having an issue with the algorithms.

                      Unstructured data compares similar patterns and then extends the pattern out further.

                      Patterns produce predictable outcomes but they do not use single points to offer any logical conclusions.

                      Think of it this way, you can find a pattern of behavior but it doesn’t tell you why it’s happening.


                      We use entangled particles now in advanced microscopes but we have no idea how entangled particles work.

                      They are using these algorithms to predict crimes before they happen in some cities but they don’t know who is going to commit the crimes or why. We are predictable to machines but we are not sure why yet. I can write programs to find the encoded data based on comparisons but it doesn’t make sense as to why.


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