(Spoiler: The first part of this article contains information about my education life and technical data.)
Even though I was born in the 80s, a period when technological developments were not very rapid and almost everything on a daily basis was still quite manual and analogue, my interest in technology started at an early age, I think primarily due to my interest in mathematics. I tried to follow the technology in accordance with the conditions of the period. However, no matter how interested I was in science, my interest in engineering and technology actually started to take shape at university. First of all, I got into the industrial engineering department. Over the course of two years, I took a lot of courses, including departmental courses such as cost analysis and manufacturing. In the meantime, I started taking the computer programming course that all engineering students have to take.
C language was my first introduction to programming and my first taste. I confess, I had a hard time. Those who are interested and experienced know that C is one of the languages with the highest learning curve, that is, one of the most difficult. It is not a relatively new, object-oriented language like Java, which collects its own garbage (Garbage Collector – memory management tool). The first time we are introduced to pointers. Function calling by reference, complex data structures, etc. This is provided by pointers. Yes, it was difficult, my first exam went so badly and I got such a low grade that I was nervous. Because I was studying with a scholarship and I did not have the luxury of getting low scores in the exams. But there was something. I found that I enjoyed programming more than industrial engineering courses. Yes, it was, even though I wasn’t successful yet. For the next exam, I studied every resource I could find and practiced a lot. This time my exam went so well that the professor teaching the course was also surprised. Then I realized that programming is a matter of dedication and requires a lot of practice. I won’t cut to the chase, what happens next is clear. I transferred to the Computer Engineering department. I studied my department with great pleasure and graduated with honors. When I was in the 4th grade, my biggest dream was to stay in the academy. This is how I charted my path. Years have passed in 3 continents, including master’s degree, doctorate, post-doctoral and academic faculty membership…
Thanks to my job, I had the chance to closely follow all the technological developments that have left their mark in recent years. First we saw the hardware improvements. While we were trying to keep data in KB on square floppy disks, we realized that we were keeping, moving and processing TB and PB data. There is also such a thing as a cloud system. One of the most beautiful sub-blessings of the internet blessing. We moved from the age of playing arcade games to consoles. Our graphics processors got better and faster day by day. So much so that we stopped looking at games that didn’t look photorealistic and weren’t fast enough. As if that wasn’t enough, we added virtual reality and augmented reality. While desktops were not available in homes in the past, there are now two or three laptops in every home. First of all, we met with mobile phones, they evolved and became smarter. Access to information has become easier. Data visualization made easy. We started doing 3D printing. Everyone started talking about artificial intelligence and quantum as if that wasn’t enough. Just as everyone became a VR and AR expert during the Meta craze, we have seen a thousand times more in artificial intelligence. Let me give you an example. My PhD field and therefore my area of expertise is graphics algorithms and simulation systems. I was educated about the rules of mathematics and physics in which all the things we see on our computer screens (texts, pictures, movies, games) are drawn and how we can carry out all these processes in the most optimized way.
You can probably understand how much the software side of Virtual Reality technology is related to graphics, but still, me and my colleagues came across this Metaverse period when someone founded a new company and bought a headset and tried to market the program that came with it to others as if they had made it themselves. Now, we were insulted by hearing “this is not the way you know it, you don’t understand it”. We realized that everyone was now a Metaverse expert. Something similar and worse happened in the field of Artificial Intelligence.
It turns out that everyone has so many ideas about everything and how knowledgeable they are about artificial intelligence. I observe such a trend occurring in the world. As soon as a new technological development occurs, many people who have no idea about the subject and gather information through hearsay begin to express their opinions.
So Let’s Explore A Little
I will not mention its name for confidentiality reasons, but senior executives from several of the world’s leading technology companies were invited to the meeting of the Board of Advisors of the National College Board of America AP CS A Test, which I attended a few months ago. So much so that the majority of our students work incredibly hard to get into those companies and prepare themselves for a great race. Because these companies are leading the future and there is a lot of competition. When we asked what they expected from us educators and universities in the future, apart from the existing system, there were two issues they particularly emphasized, and this was just a repetition of a topic talked about all over the world: Critical Thinking and Machine Learning.
Critical Thinking and Machine Learning (ML)
Now, these two concepts are separate concepts and need to be addressed separately. Critical thinking, unfortunately, has become an increasingly blunt concept today. What is critical thinking? In its simplest sense, reasoning includes the processes of on-site analysis and evaluation of events. It requires questioning. It is an analytical way of thinking that involves collecting evidence, asking logical questions based on the data you have, and then finding realistic and rational conclusions. Ironically, even the world’s largest companies agree that this is still the most important skill in new graduates. Today’s technological developments have turned many people into individuals who absorb and consume more information faster and, in turn, do not produce anything. But critical thinking is actually humanity’s greatest trump card. In a time when artificial intelligence is rapidly developing, at a time when everyone is thinking ‘will I be fired because of AI?’, there is no greater blessing than this trump card in your hand. This has always been one of the main points I have tried to explain to my students, and I have been emphasizing it even more especially in the last few years. Before both technological developments and recent AI became so widespread, reading rates, reading and understanding the problem well and producing solutions were much higher. In the current situation, we need to return to that axis immediately. We need this more than ever. In my personal opinion, this is the period in which the human brain will become even more groundbreaking, that is, the Artificial Intelligence period.
Now, let’s talk about why they tell us, ‘Universities should definitely graduate their students by teaching Machine Learning at the basic level.’ It would not be wrong to say that Machine Learning is one of the sub-fields of Artificial Intelligence and perhaps the most fundamental field. We are talking about smart structures that process the data they receive, learn from this data and make system optimization accordingly. We are talking about a structure built on advanced statistical models that can make generalizations about the data it processes and the data it does not receive or process, without the need for explicit commands. There is almost no one working in the field of Computer Engineering who has not taken a look at this field or tried to use it in his own field. After all, we all received similar mathematics, statistics and programming training, and it is a matter of curiosity for every researcher to see what kind of optimization such models can cause in our field. However, ours is more for integration purposes. For example, I can say that I personally see the benefits of machine learning in improving the advanced graphics and game design processes in games. I still think it should be used more widely in the field of physics-based simulations and photorealistic physics-based graphics. In fact, at one point, just out of curiosity, we did a project with my students to process music data via Spotify and make personalized venue selection. But all this does not make me an Artificial Intelligence or Machine Learning expert. Real experts in this field either do their master’s and/or doctoral education in this field or have been working as researchers in this field for years. They have been doing R&D for years, not on integration like us, but on developing more advanced, faster and more effective ML algorithms or on their usability in new work areas. One of my favorite areas of use is the use of ML in the field of healthcare. Especially in brain scans for types of cancer and dementia. This being the case, instead of being afraid of machine learning, the issue of how to process data in the most optimized way and make it useful has become one of the most interesting topics today. When combined with the Critical Thinking style mentioned above, instead of being afraid of it, perhaps we should enjoy it as its creator and user.
Now, if we close the subject, in these days when everyone is an artificial intelligence expert, for the sake of real AI-ML experts who are really trying to do this job properly, perhaps those who are not closely related to this job and have no idea on either the technical or the philosophical side of the subject should keep quiet. Shall I tell you a secret? Most people who make judgments about this and similar issues everywhere (in the Metaverse as well) are not experts in this field. Because real experts are always busy doing their job instead of talking.