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.
Category: Technology
Important but surprising facts regarding Technology
Superfact 19: An account impersonating you on Facebook does not mean you have been hacked. When someone using your name and photo starts sending friend requests to your friends on Facebook, they are most likely just copying your information. You have not been hacked.
I am considering this a super-fact because almost every time I see this the person being impersonated states “….I have been hacked”. Most likely they have not been hacked. They don’t need to change their password or take special precautions related to their account or password. It is not the problem.
Facebook is the world’s largest social network with over 3 billion users and few people understand this common Facebook problem, which is why I am calling it super-fact. All that happened is that someone downloaded their photo, copied some information, and started sending out friend requests to their friends. It is so easy to do that. Any 10-year-old can do it and there’s no hacking required. If you think about it for a minute, I am sure you all could do it.
However, it is not appropriate behavior and Facebook can delete your account and ban you if you resort to this behavior.
WP AI generated image
So, what can you do to reduce the chance of being impersonated? You can go to Settings & Privacy > Privacy Settings and set your profile to private by setting “Who can see your posts?” to friends only, but if you want visibility and don’t want to go that far you can set the “Who can see your friends list?” to “only me”. You can also set “Who can see posts you’re tagged in?” to “Friends” or “Only me”. You can “Limit Who can see your profile picture and cover photo?” to “Friends.”
Additional things you can do are regularly search your name on Facebook to check for fake profiles and avoid oversharing.
The actions above will greatly reduce the chance that someone will impersonate you but if it happens anyway, you can report the offender by going to the fake profile and click on the three dots (…) on their cover photo, select “Find support” or “report profile” and choose “Pretending to Be Someone” and follow the instructions to report the account. Encourage your friends to do the same.
“The Nobel Prize in Physics and Neural Networks” is not a super-fact but just what I consider interesting information
The Nobel Prizes are in the process of being announced. The Nobel Prize in Physiology or Medicine, Chemistry, Physics and Literature have been announced and the Nobel Prize in Peace will be coming up at any minute. The Nobel Prize in Economics will be announced October 14.
The Nobel Prize in Peace tends to get the most attention but personally I focus more on the Nobel Prizes in the sciences. That may be because of my biases, but those prizes also tend to be more clearcut and rarely politized. Nobel Prize in Peace is announced and given in Oslo, Norway, and all the other prizes are announced and given in Stockholm, Sweden.
Nobel Prize In Physics
What I wanted to talk about here is the Nobel Prize in Physics given to John J. Hopfield and Geoffrey J. Hinton. They made a number of important discoveries in the field of Artificial Intelligence, more specifically neural networks. This is really computer science, not physics. However, they used tools and models from physics to create their networks and algorithms, which is why the Nobel committee deemed it fit to give them the Nobel Prize in Physics.
Perhaps we need another Nobel Prize for computer science. It is also of interest to me because I’ve created and used various Neural Networks myself. It was not part of my research or part of my job, so I am not an expert. For all of you who are interested in ChatGPT, it consists of a so-called deep learning neural network (multiple hidden layers) containing 176 billion neurons. By the way that is more than the 100 billion neurons in the human brain. But OK, they aren’t real neurons.
So, what is an artificial neural network?
A simple old-style 1950’s Neural Network (my drawing)
The first neural networks created by Frank Rosenblatt in 1957 looked like the one above. You had input neurons and output neurons connected via weights that you adjusted using an algorithm. In the case above you have three inputs (2, 0, 3) and these inputs are multiplied by the weights to the outputs. 3 X 0.2 +0 + 2 X -0.25 = 0.1 and 3 X 0.4 + 0 + 2 X 0.1 = 1.4 and then each output node has a threshold function yielding outputs 0 and 1.
To train the network you create a set of inputs and the output that you want for each input. You pick some random weights and then you can calculate the total error you get, and you use the error to calculate a new set of weights. You do this over and over until you get the output you want for the different inputs. The amazing thing is that now the neural network will often also give you the desired output for an input that you have not used in the training. Unfortunately, these neural networks weren’t very good, and they often failed and could not even be trained.
In 1985/1986, Geoffrey Hinton, David Rumelhart and Ronald J. Williams presented an algorithm applied to a neural network featuring a hidden layer that was very successful. It was effective and guaranteed to learn patterns that were possible to learn. It set off a revolution in Neural Networks. The next year, in 1987, when I was a college student, I used that algorithm on a neural network featuring a hidden layer to do simple OCR (optical character recognition).
Note that a computer reading an image with a letter is very different from someone typing it on a keyboard. In the case of the image, you must use OCR, a complicated and smart algorithm for the computer to know which letter it is.
A multiple layer neural network with one hidden layer. This set-up and the associated backpropagation algorithm set off the neural network revolution. My drawing.
In the network above you use the errors in a similar fashion to the above to adjust the weights to get the output you want, but the algorithm, the backpropagation algorithm is very successful.
Below I am showing two 10 X 10 pixel images containing the letter F. The neural network I created had 100 inputs, one for each pixel, a hidden layer and then output neurons corresponding to each letter I wanted to read. I think I used about 10 or 20 versions of each letter during training, by which I mean running the algorithm to adjust the weights to minimize the error until it is almost gone.
Now if I used an image with a letter that I had never used before, the neural network typically got it right even though the image was new. Note, my experiment took place in 1987. OCR has come a long way since then.
Two examples of the letter F in a 10 X 10 image. You can use these images (100 input neurons) to train a neural network to recognize the letters F.
At first, it was believed that adding more than one hidden layer did not add much. That was until it was discovered that by applying the backpropagation algorithm differently to different layers created a better / smarter neural network and so at the beginning of this century the deep learning neural network was born (or just deep learning AI). Our Nobel Prize winner Geoffrey J. Hinton was a pioneer in deep learning neural networks.
My drawing of a deep learning neural network (deep learning AI). There are three hidden layers.
I should mention that there are many styles of neural networks, not just the ones I’ve shown here. Below is a network called a Hopfield network (it was certainly not the only thing he discovered).
In a Hopfield network all neurons are input, and output neurons and they are all connected to each other.
For your information, ChatGPT-3.5 is a deep learning neural network like the one in my colorful picture above, but instead of 3 hidden layers it has 96 hidden layers in its neural network and instead of 19 neurons it has a total of 176 billion neurons. Congratulations to John J. Hopfield and Geoffrey J. Hinton.
Super Facts 