Now, one thing you may be thinking at this point is this article spinner doesn't seem to work that
well.
Of course, this should not be a surprise, given that we are only using simple programs without considering
parts of speech or other context.
Recall that other techniques, especially those involving deep learning, can be applied to the same
idea of replacing a middle word given surrounding words.
Unlike programs, deep learning techniques can account for more context, as well as potentially learn
ahead and structures such as grammar and parts of speech.
However, since we are not yet at that point in the course, you will just have to wait until you're
able to increase your knowledge.
That is, you must further gather the prerequisites you need in order to progress to those more advanced
techniques.
That being said, it's worth comparing these results to the screenshot of the article spinner program
I showed you earlier in the section.
In fact, what you have seen is on par with applications which people actually pay for.
Remember that these spinner programs don't just replace words automatically, they give you a list of
suggestions to choose from.
So even just knowing what you know so far, this is enough to build a product that people will happily
spend their money on.
And that brings us to the topic of this lecture, which is all about article spinning gone wrong.
So I won't be naming any names, but I'm sure if you watch machine learning videos on YouTube, you've
come across this story yourself.
So this story is about a pretty popular YouTuber who made a videos about machine learning.
Now it's important to realize that these weren't technical videos they just pretended to be.
For instance, he would make a video like Watch me build a stock trading bomb in five minutes.
But it was really code that he took from someone else's GitHub.
It was more for entertainment than it was about increasing your technical ability.
Of course, he would never be able to explain the code because he didn't write it himself, nor even
try to understand it.
It was not a machine learning expert, but rather only someone who played one on YouTube.
As a side note, whenever I make videos like common beginner mistakes when predicting stock prices without
stems.
These are some of the kinds of videos I'm referring to.
So anyway, it wasn't long until this fellow decided to publish his own paper on the topic of neural
qubits.
Sounds pretty advanced.
Unfortunately, the entire paper was plagiarized.
As mentioned, this was not an actual machine learning practitioner.
Only one who pretended to be on YouTube.
Sadly, some people still believe this YouTuber actually knows about machine learning.
Luckily, this paper wasn't published anywhere reputable, but rather just a weird website called the
VIX Rob, which is known for publishing works by crazy people.
So the main highlight of this paper is that it really makes no attempt to hide that it is completely
plagiarized.
Both the images and math formulas were simply screenshots of the original.
And this would be highly unusual if it were a true academic paper.
For the text portion, the basic strategy was to do something similar to what we described in the section,
simply replace random words with synonyms, hoping that they would still make sense in context.
So what was the result?
Well, there were some very strange replacements.
For example, quantum gate was changed to quantum door.
Of course, in everyday language, gate can certainly be a synonym for door.
The problem is that was not the case in this context, in this context.
Gate refers to a logic gate, for example, and or not XOR and so forth.
Another popular example is complex Hilbert space, which was changed to complicated Hilbert space.
And of course, in everyday language, complex can be a synonym too complicated.
For example, beginners who don't meet the prerequisites to my courses think of my courses are too complex.
This has the same meaning as beginners think my courses are too complicated.
But in this context, complex does not mean complicated, but rather it's referring to complex numbers.
So in fact, one downside to this approach is that it did not take context into account like we did
in this course.
Thus, one could say that if this YouTuber had used the techniques in this course, he wouldn't have
made such silly mistakes.
Now, my goal in this lecture is not to make fun of this YouTuber, although it is a very interesting
story, but there are many lessons here.
Firstly, notice that this method of content spinning really is in use in the real world.
It may sound like I've just made this up for the purpose of this course, but it really is a real world
practical use case.
Secondly, and most importantly, notice how it is not hard to detect.
So if you want to be taken seriously or to publish content that people actually enjoy and respect.
This is not the way to do it.
Not only was this easily detected by people, but it would be just as easy for machines as well.