The goal of this blog is to create a list of super facts. Important facts that are true with very high certainty and yet surprising, misunderstood, or disputed by many. This blog aims to be challenging, educational, and fun, without it being clickbait. I determine veracity using evidence, data from reputable sources and longstanding scientific consensus. Prepare to be challenged (I am). Intentionally seek the truth not confirmation of your belief.
Super fact 95 : More than 4-5 million people died in Congo Civil War 1998 to 2003 and its aftermath (until 2007). In addition, some of the other of the deadliest wars in the 21st century are not well known.
Below : 21st Century death toll from different wars, current and recent.
The number of deaths cited in the graphics above correspond to rough midpoint values of the estimates for the 21st century. K stands for one thousand deaths. The area of the circles corresponds to these midpoint estimates. See the list of conflicts under “Deadliest twenty first Century Wars”.
Notes on the graphics above:
The 16 wars included in the graphics above roughly correspond to the deadliest wars in the 21st century. However, a few of the wars included in the graphics are not among the 16 deadliest wars of the 21st century.
Some of the wars included above started in the 20th century. The number of deaths estimates corresponds to the portions that happened in the 21st century.
The number of deaths from the Congo Civil War include famine and plagues that resulted from the war.
I had a hard time finding an estimate for just the 21st century portion of the Congo Civil War. However, the vast majority of the deaths happened in the 21st century.
The death estimates for the Russo-Ukrainian War might be underestimated due to unreliable reporting by Russian authorities.
The Israeli-Gaza war may seem smaller than expected. However, media attention does not necessarily correspond to the size of the devastation of a conflict.
The estimate for the number of deaths for the current Iran-US-Israel war is between 3,600 to nearly 5,400 deaths, which is too small to be part of this post.
There are / were hundreds of wars and conflicts in the 21st century.
I consider this a super fact because it is true, an important history fact and despite the enormous losses in the Congo Civil War a lot of people do not know much about it and may not even have heard about it.
Chechen conflict 1994 to 2009. With an estimated 80 thousand to 230 thousand deaths, most of those, 150 thousand, happening in the 21st century.
Angolan Civil War 1975 to 2002. With an estimated 800 thousand deaths many in the final years of the conflict with an estimated 100 thousand in the 21st century.
Burundian Civil War 1993 to 2005. With an estimated 550 thousand to 800 thousand deaths, many in the final years of the conflict with an estimated 50 thousand deaths in the 21st century.
The Arab Israeli conflict 1948 to present (multiple wars) . With an estimated 200 thousand deaths in total with 80 thousand deaths happening in the 21st century.
The Israel Gaza conflict (2023 to present). With an estimated 75 thousand deaths.
Superfact 94: Light is electromagnetic radiation. The electromagnetic spectrum we deal with goes from long wave radiation at a frequency of 0.3 Giga Hertz to gamma rays at 30,000,000,000 Giga Hertz, and far beyond. Light that is visible to humans goes from around 428,000 Giga Hertz to 750,000 Giga Hertz. This is a very thin sliver in the electromagnetic spectrum. In addition, many animals can see beyond the spectrum visible to humans.
If you consider wavelength instead of frequency, the electromagnetic spectrum goes from gamma rays at a wavelength of 0.00000000001 meters to long waves at a wavelength of 1,000 meters. Visible light has a wavelength of 0.0000004 meters to 0.0000007 meters. Again, human vision corresponds to only a thin sliver of the electromagnetic spectrum.
The visible color spectrum. Sunlight wavelength and increasing frequency vector infographic illustration. Visible spectrum color range. Rainbow electromagnetic waves. Educational physics line. Shutterstock Asset id: 1933622132 by Shutterstock Asset id: 1933622132 WinWin artlab.The spectrum visible to humans highlighted on a spectrum going from long waves to gamma rays. Original: Penubag Vector: Victor Blacus, CC BY-SA 3.0 <https://creativecommons.org/licenses/by-sa/3.0>, via Wikimedia Commons
It should be noted that the spectra above go from long waves to gamma rays because that’s the range of the spectra we typically deal with. However, the electromagnetic spectrum continues far beyond that.
I consider “Human Vision Only Detects a Sliver of the EM Spectrum” a super fact because it is a well-known fact among those who have studied physics, and it is an important fact, and yet I believe it is a surprise to many.
The Spectrum Visible to Animals
A lot of animals can see beyond the spectrum visible to humans. For example, animals that can see UV light including reindeer, scorpions, butterflies, bees, salmon, hedgehogs, many birds, amphibians, and reptiles. Humans cannot see UV light. You can read about various animals that can see UV light here, here, and here.
It appears that dogs and cats can see UV light as well. The color vision of dogs is in general poor, at least on the red side of the visible spectrum. However, dog vision includes UV light that we cannot see.
Today is the second time I participate in Linda Hill’s streams of consciousness. To read about the rules and participate click here, or here. To read my previous entry (prompt was pre) click here. Today’s prompt for Stream of Consciousness Saturday April 4 is “hide”. Use it way you’d like. The first rule is: there should be minimal planning and no editing except typos. First thing that came to my mind was reindeer hide. Notice that this is not a super fact post, but just a general informational / factual post. In addition, to writing about my experiences with reindeer hides I also downloaded a few pictures from Wikipedia.
2019-2020 SoCS Badge by Shelley!
Reindeer Hide Memories
I grew up northern Sweden where there are a lot of reindeer and reindeer hides. When we drove around on the north Swedish countryside, we often saw reindeer at the side of the road. Sometimes there were flocks of reindeer blocking the road. Unlike many other animals reindeer tends to be a little bit stupid about traffic and quite often they walked right in front of the cars. The risk for collision was quite high.
Distribution of Rangifer tarandus (Caribou/Reindeer) Red – Reindeer (orange: introduced populations) Green – Caribou. TBjornstad 11:46, 31 October 2006 (UTC), Public domain, via Wikimedia Commons. Image is from this Wikipedia page.
A related interesting fact is that the in northern Sweden, Norway, Finland and parts of Russia there is an Aboriginal people referred to as Sami. They are / were traditionally a nomadic people who followed and herded the reindeer. Reindeer hide is a very important item for the Samis, and used for clothing, footwear, tents, drums and musical instruments, rugs and bedding. Reindeer hide is also an important cultural item for us “regular” north Swedes. When I was a kid, we used reindeer hide for bedding, decoration, wall ornaments, and for cover when sitting outside on the snow or the ice.
My kids here in Texas also have some experience with reindeer hides. When they were little, we visited the ice hotel in Jukkasjärvi in northern Sweden. The ice hotel in Jukkasjärvi is the original and largest ice hotel in the world. The beds in the ice hotel are made of ice and covered by reindeer hide to make it warm and soft. We also took a dog sled tour, and the sled was covered by reindeer hide for comfort. Below are some photos of us on a dogsled with reindeer hide and photos of ice beds with reindeer hide, including our room. I got these photos from my personal and very old family website.
We are going on a dogsled tour. The ice theater is in the background, and you can see part of the ice hotel on the right. Notice the reindeer hide on the sled.On the left is a kåta, a movable Sami structure (indigenous arctic Scandinavian people). Kåtas were traditionally made with reindeer hide.My dad Stig and his girlfriend Ulla came with us on the trip. Notice the reindeer hide on the ice bed.We are going to bed in our room. I think it was my wife Claudia who took the photo. Notice the reindeer hide on the ice bed.Another room with a snowy wall decoration. Notice the reindeer hide on the ice bed.Some of the rooms were really beautiful. Notice the reindeer hide on the bed.Some of the rooms had beautiful ice art. Notice the reindeer hide on the bed.You had to pay more for a big room. You paid the price of Hilton and got the comfort of camping in winter. Notice the reindeer hide on the bed.More ice art. Notice the reindeer hide on the bed.
Super fact 92 : College tuition and fees increased by 207% between 1997 and 2022, which corresponds to a tripling. Since 2022 it has continued to increase. Adjusted for inflation this corresponds to a 60% increase, more than any other major economic sector.
Students are sucked into a hole with money for college tuition. Shutterstock Asset id: 335014478 by alphaspirit.it.Data source: US Bureau of Labor Statistics (2026). OurWorldInData.org/technological-change. Note: Some services such as medical care are not adjusted for quality, some treatments have decreased in price rather than increased. To visit the original page for this graph click here or on the picture.
To read about the graph above in greater detail click here. To read more about the increase in college tuition click here, here, here, or here. To calculate the inflation for different periods of time click here (the inflation calculator).
It should be noted in the graphs above that Televisions have decreased in price by 98%, which seems implausible. However, it is not an April 1st joke. It is not April 1st in Texas yet (where I live). The reason for the large decline in price of Televisions is that quality is taken into account. 20 years ago, you could not easily buy the kind of TVs you can today and if you did you would cost you an enormous amount of money. You get a lot more for a few hundred bucks than you did 20 years ago.
This is a superfact because it is true, shocking, and important to how we live.
An example of heavy spending on amenities is college sports. I studied at a university called Uppsala University, which is a very large University founded in 1477. It is considered one of the world’s top Universities (top 50). However, they do not have any competitive teams associated with the University itself. In 1987 I was sent as an exchange student (electrical engineering) to Case Western Reserve University, in Cleveland, Ohio. Case Western Reserve University has some competitive teams associated with the University but only a small college football stadium.
However, I quickly came to realize that many American universities have huge college football and baseball stadiums. This is an added expense that you rarely encounter in the rest of the world. Universities are for studies and for learning. Sports teams, whether it is soccer, or American football is a separate issue.
BLOOMINGTON, US – Aug 04, 2025: Side-angle drone view of Indiana University Memorial Stadium with field, stands, and cityscape on a clear summer day. . – Shutterstock Asset id: 2700467103
Superfact 90: Large Language Models (LLMs) such as ChatGPT, Claude, Llama and Gemini are just one type of popular applications of Artificial Intelligence among hundreds of applications of Artificial Intelligence, and LLMs represents just one branch of Artificial Intelligence.
Artificial intelligence and research concept. Shutterstock Asset id: 2314449325 by Stock-Asso
LLMs are currently the most popular “viral” AI. We can all access LLMs in our browsers. This has created the common misconception that Artificial Intelligence is the same as Large Language Models. However, LLMs represent only one branch of narrow AI systems designed to perform specific tasks.
Applications of Artificial Intelligence other than what Large Language Models are used for include robotics, robot motion planning, advanced control systems using AI, self-driving cars, image processing, optical character recognition, classification, facial recognition systems, medical imaging diagnostics, game playing such as chess playing computers, financial fraud detection, cybersecurity, investment robots, route optimization, mathematical proof generation, recommendation algorithms, virtual assistants, programming code generation, smart home devices, drug discovery, and that is just for starters.
There are probably many applications and types of Artificial Intelligence that we have not yet invented.
Two Robots powered by Artificial Intelligence. Shutterstock Asset id: 558350728 by Willrow Hood.
LLMs use large neural networks with many hidden layers, so called deep learning algorithms, and they employ the Rumelhart backpropagation learning algorithm invented by David Rumelhart, Geoffrey Hinton, and Ronald Williams. Clearly neural networks with multiple hidden layers and using the Rumelhart backpropagation algorithm are incredibly successful but it is just one of many kinds of Artificial Intelligence algorithms, and who knows what we will see in the future. Related to this post is my previous post Artificial Intelligence is Not New. We have only just begun.
I consider this a super fact because it is true, kind of important, and I believe that the multitude of Artificial Intelligence algorithms and applications is a surprise to many.
The many Artificial Intelligence Algorithms
Shutterstock Asset id: 2645975149
Due to the great improvement and success of Neural Networks, they have become very popular and Large Language Models use very large Neural Networks with multiple hidden layers (employing the Rumelhart back propagation algorithm). You can read more about that here.
However, there are many other AI algorithms, hundreds, maybe thousands. One example is genetic algorithms. These are types of algorithms that mimics evolution. They iteratively select a set of the best candidate solutions, then combine them (crossover), and also add random changes (mutation) to generate new solutions. Then select the best solutions and then you do it again. Selecting the best solutions corresponds to natural selection. I tried out such algorithms at my work, and over many iterations / generations you can get some impressive results. It is easy to understand how a complex organ such as an eye can evolve in a similar way in nature.
One type of decision tree based machine learning algorithm that I used specifically for classification tasks at work was C4.5 and C5. More specifically I used this type of machine learning algorithm for evaluating the results from automatic mail sorting systems. Basically, how well can a result from a certain machine be trusted. I don’t remember exactly but my classes were something along the line of super reliable, pretty reliable, average, and this result probably sucks. Other examples of this type of machine learning are ID3, Random Forest, Gradient Boosting, and CART. These types of algorithms are still very popular.
One advantage of using decision tree based machine learning over neural networks for the same task is that when a decision has been made you can follow the decision tree backwards and see why a decision / classification was made. In fact, if you have less than 100 parameters you could likely do it over a lunch. When a neural network makes a decision all you have is a large bunch of numbers that were spit out by an algorithm that looped possibly thousands of times and changing all the numbers every time. You can’t backtrack and figure out exactly how a decision was made. You just have to trust the neural network. The advantage of a neural network in this situation is that if it is trained properly, it is likely to have a better result.
Another type of algorithm used in Artificial Intelligence is search algorithms. For robot motion planning I used an algorithm called A* or A-star, which is a very efficient pathfinding algorithm. It comes in dozens of variants and there are hundreds of other types of search algorithms.
These are just a few examples, but there’s also knowledge based agents, AI-agents with reinforcement learning algorithms, algorithms based on Bayes’ Theorem, Vector Machines, Markov Decision Processes, clustering algorithms, K-nearest neighbor (KNN) algorithm, simulated annealing, hill climbing, the ant colony optimization algorithm, and of course neural networks and there are also many types of neural networks. I used a relatively unknown form of artificial intelligence called reflex control for my robotics research. The point is, there is zoo of artificial intelligence algorithms out there. Deep learning neural networks are very popular AI algorithms but far from the only ones.
My Personal Experience with Artificial Intelligence
In 1986, when I was in college in Sweden, I took a class in the LISP programming language. LISP was the first Artificial Intelligence programming language, and it was invented in 1958. In 1987, as a university level exchange student, I took a class called Artificial Intelligence at Case Western Reserve University. That same year I also took a class called Pattern Recognition which introduced neural networks to me.
In 1986 a landmark paper was published by David Rumelhart, Geoffrey Hinton, and Ronald Williams introducing the Rumelhart backpropagation algorithm. Geoffrey Hinton received the Nobel Prize in physics in 2024. David Rumelhart and Ronald Williams were both dead and could therefore not receive the Nobel Prize. The Nobel Prize was also given to John J. Hopfield, another pioneer in neural networks. He invented the Hopfield network. You can read more about neural networks and the Nobel Prize in physics in 2024 here.
The Rumelhart backpropagation algorithm was a giant leap forward for neural networks and for Artificial Intelligence and it is the algorithm used by ChatGPT and the other large language models. Geoffrey Hinton is often interviewed in media and often presented as the father of Artificial Intelligence. He is not, but he us arguably partially responsible for the greatest leap forward in neural networks, as well as Artificial Intelligence.
In the pattern recognition class, we used the Rumelhart backpropagation algorithm on a simple neural network to read images with text. Later I did research in the field of Robotics where I implemented various Artificial Intelligence algorithms as mentioned above. I have a PhD in Applied Physics and Electrical Engineering with specialty in Robotics. Later I would use artificial intelligence algorithms in my professional career.
I used mostly the seven joint Robotics Research Corporation Robot for my robotics research. The robot was able to detect and avoid colliding with the objects surrounding it. I used echolocation for object detection.
The potential harm of AI is a related and important topic that I did not address. I don’t know much about this topic. However, Grant from “Grant at Tame Your Book” have written an excellent, well research and professional post about this issue called Don’t Confuse AI with a Benign Tool. Please check it out.
Super Facts 