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.
Grant from “Grant at Tame Your Book” have written an excellent and well researched post about the dark side of Artificial Intelligence. It has nearly a hundred references and it is very professionally written. It is called Don’t Confuse AI with a Benign Tool. With this post I just wanted to highlight this important post. Please check it out.
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.
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.
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.
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.
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.
My previous posts on Artificial Intelligence, “Artificial Intelligence is Not New”, and “The Nobel Prize in Physics and Neural Networks”, describe how neural networks work in greater detail.
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 fact 66 : Bots make up more than half of all internet traffic surpassing human activity for the first time in 2024. The 2025 Imperva Bad Bot Report found that bots accounted for 51% of all web traffic. Human activity accounted for 49% of all internet traffic, malicious “bad bots” accounted for 37%, and 14% of traffic comes from “good bots,” such as search engine crawlers.
What made me look up this information is that I’ve recently seen a lot of idiotic and inflammatory comments, as well as inappropriate laughing emojis on social media pages and posts. The pages that seem to be targeted the most by this abuse seems to be pages related to things like climate change, clean energy, EV cars, evolution, vaccines, modern medicine, modern physics, geopolitics, information on political issues, etc. However, those topics might reflect my interests. Perhaps all topics are targeted by this growing strange abuse.
It looks like those among us who slept through the science classes in high school now think they are the real experts and have declared war on all science nerds. The question that arose in my mind is, are these keyboard warriors humans or are they bots? It is true that Artificial Intelligence does not (yet) demonstrate true independent intelligence, but that is true for many people as well. So, how do you know the difference? Anyway, that is the background to why I investigated this issue.
I looked it up and found that bot traffic has been increasing and is now the majority of all internet traffic according to, for example, the 2025 Imperva Bad Bot Report. You can read more here. Facebook is not an exception. 40% of all posts are machine generated. These bots/fake accounts spread a lot of misinformation, inflammatory comments, and some are scams.
Many people are also spreading false information and some of them are scammers, but the fact that machines do it as well add to the problem. It is also very common for bots and fake accounts to leave reactions on Facebook posts, which might be what I saw, but I am not sure. What is certain is that I have come across a lot of false information on Facebook, as well as scams and deep fakes, and Meta/Facebook is obviously not able to clean it out. There are also bots that are remotely controlled ransomware, computer viruses, spyware, and other malware.
Why I consider this a super fact is because it appears to me that people underestimate the influence of malicious bots. If you had asked me before I looked this up how common bot traffic was, I might have said a few percent. After all streaming, youTube, gaming, etc., require a lot of bandwidth. Considering all the fake stuff and nonsense that is spreading partially with the help of bots, this is dangerous. We know the bots make up more than half of all internet traffic, and bot traffic is growing faster than human traffic, it is important information, and I think it is surprising information to a lot of people, thus making it a super fact.
This section is not directly tied to the super fact above, but it concerns a related topic and is based on my personal experience with the social media platform that I have used the most, Facebook. Instagram seems to be even worse, but I am not using it as much. Why I am bringing this up is because increased bot traffic and the increased presence of fake accounts and deep fakes on social media can make this a lot worse. Combined with our gullibility and lack of critical thinking as well as the failure of social media platforms to keep after this, we are facing a serious threat.
It is well known that there are a lot of nonsense posts on Facebook (and elsewhere). The fact that we so easily fall for it and don’t check with reliable sources is a big problem. When I see something fake, I often post corrections, for example, using sites like snopes. Sometimes people are grateful, sometimes they get angry, and I’ve even been blocked and lost friends just by posting a snopes link. A lot of the fake stuff is posted by people, but a lot of posts, comments and reactions are posted by bots, and this is becoming more common. With increased malicious bot traffic, AI and deep fakes, we must improve our critical thinking skills.
Did you read that viral article on Facebook claiming that they found 20 feet humanoid skeletons in Turkey? The article stated that archeologists think that they might be fossilized Nephilim, the giants mentioned in the Old Testament. If so, did you doubt the accuracy of the article? If you did, you did good. It was based on an article in a satirical website called World News Daily Report. However, judging from the comment section, including the comments of some of my friends, most people didn’t doubt the article’s accuracy.
How about the story from a purported science magazine that scientists had just discovered that the Easter Island statues/heads have bodies/torsos below the ground. The article stated that this was a revolution in archeology that forced a reevaluation of history. The commentors were amazed over this discovery and some pointed out that not realizing this sooner was a big failure on the part of archeologists and scientists. Well, that the Easter Island statues/heads have bodies/torsos below the ground has been known all along.
How about the story about the lunch lady named Aileen G. Ainuse who poisoned the water supply at Sunnydale High School in Goobersville, Indiana, killing over 300 students and staff. It was accompanied by a scary photo of a starving lady. The readers were shocked and appalled, but not many bothered to verify the story, for example, with the help of snopes. The story was false.
Another article stated that the fact that there were no stars in the black sky in a photo allegedly taken on the moon was proof that the photo was fake and that the astronauts were never on the moon. First of all, it was day, the sun was out. When the sun is out it is very difficult to see the stars because the sun’s light is a million times brighter than the light from the stars and in addition the bright sunlight reflected off the surface of the moon dims the stars. In addition, the cameras used had short shutter speeds for picking up the bright light, not faint stars. Seeing stars in a daytime photo taken on the moon is not something you should expect. Several commentors pointed this out but most other commentors didn’t pay attention and were fooled.
I’ve also seen the opposite, people refusing to believe a true story because they fundamentally misunderstand something. Below is a youTube video showing an animation composed of actual satellite photos by NASA. Many commentors seeing this video insisted that it was a hoax because the back side of the moon is dark. But it is not. When the side of the moon that is turned towards us (the near side) is dark (a new moon) the back side reflects the sun’s light (like a full moon). The backside (far side) of the moon also looks different from the side turned towards us. In the video below the sun is behind the camera and shines on earth as well as the backside of the moon.
A final example is a deep fake Ad featuring Meryl Streep and Dr. Sanjay Gupta promoting an Alzheimer’s cure. I saw it on Facebook several times over a period of several weeks. It looked very real to me, but something felt off, so I fact checked. It turned out that Meryl Streep and Sanjay Gupta had nothing to do with the video. They were AI generated likenesses promoting a scam product. The video used all the typical polemic tricks such as “a cure that the billion dollar companies don’t want you to know about”, “buy now before they take our website down”…. We need to get better at protecting ourselves and believing 20 feet skeletons on Facebook are real is not the way to do it.
Super Facts 