Rewrite in how then vs now format, 'So, let me tell you about my career journey, which started around 20 years ago when I got interested in image processing. It all began during my second year at college when I was trying to improve my grades, as I was expecting not-so-good results in a few electrical courses. I decided to speak with my hostel warden, Professor Jalihal, and asked him if there was any work I could do to help my academics.

Professor Jalihal came up with an interesting project for me – converting a color image to black and white and then the other way around. Back then, we used MATLAB to handle this task. Using MATLAB, I managed to convert the color image to black and white easily, although converting black and white to color wasn’t mathematically possible.

That project really got me into image processing, and I went on to work on more projects and internships in the field. At the time, we were using tools like MATLAB and, eventually, Python became a popular alternative. If not for Python, I might have left the technology field a long time ago.

Over time, I gained more experience by working with different companies and eventually started my own company. Looking back, it’s fascinating to see how the tools and methods of learning have evolved throughout the years. Today, there are degree programs in AI/ML and a stronger emphasis on self-learning and problem-solving.

Overall, it’s been an incredible journey, and I’m grateful for the opportunities and advancements in the fields of image processing, computer vision, and AI that have made it possible to develop effective solutions for complex challenges we once considered impossible to solve.

You know what I realized? Python is like a cheat code for non-CS folks to do really cool things. Even though my job doesn’t involve coding, I still use it all the time. It’s such an approachable language that you can start seeing results quickly and that motivates you to keep going. Plus, you can do almost anything with it once you get the hang of it.

Speaking of programming languages, let’s talk about Matlab. Remember that image conversion project? It was easy to convert a color image to black and white in Matlab - just a few lines of code. But going from black and white to color is a totally different ball game. It’s mathematically impossible to go from one number (grayscale) to three (RGB) - trust me, I tried. My colleague back then thought it could be done, but he was just testing me. Nowadays, you can train an AI to do that kind of thing, but back then it was unheard of.

Anyway, I always loved working with images and graphics, which is why I did a couple of internships at a government research institute. We got to play around with all sorts of satellite and remote sensing data, like capturing images of different types of vegetation. It was fascinating to see how different spectrums could indicate different ways that plants grew. Even water bodies had different characteristics! From there, I moved on to work with medical imaging and surveillance at Siemens and Microsoft, where I did a ton of research on background subtraction and registering CT scans and MRIs. It was so cool to see how combining functional and structural information could give us deeper insights.

But back in those early days, we had to deal with very specific problems, like tracking customers walking into a showroom. There was no one “right” way for them to enter, which made it a tricky (and expensive) problem to solve. Neural networks were just a concept, and cloud adoption was just starting to become a thing. It’s amazing to see how far image processing and computer vision have come since then!

So, there are two different scenarios I can talk about. The first one is when we were working on replicating some facial detection work. We were able to come up with a new scheme where we could extract a million features from a small image. It was pretty cool because we could identify vertical and horizontal patterns at different scales, which helped us figure out the facial pattern. We did it all from scratch, which was a bit challenging!

But nowadays, things are a bit easier because you can find open source code for almost anything. This means that you can clone a project on GITHUB and replicate it. Before, a lot of the research time went into just trying to replicate past results, but now you have access to all the code. Reputed researchers even put out their own code.

In the AI space, the availability of models and new ideas with different architectures is amazing. You can search and get new pre-trained models online. But the problem isn’t finding a good face detection model or people detection model. It’s really about understanding the constraints of the client’s ecosystem and then making it work in the production setup. Once you have a solution, distributing it to the right people is what really matters. That’s the bottom line.

Previously, a small dataset of only 5000 models was available and it required a lot of time to create a large repository of data. However, now there are thousands of datasets available for different varieties. Back then, training a model would take a month due to the lack of high-level tools and the limited computing ability. Today, the same computation can be done in just a day. The availability of datasets and advances in computing have made a huge difference. In comparison to the past, there is now a much higher level of interest in AI across departments, with groups from different areas coming together to build AI solutions.

While in the past, coding was done mainly in C++, to build production models, it is no longer necessary to use low-level languages like C. The focus has shifted towards higher levels of abstraction, and there are now four-year degrees in AI from reputable institutes. While guided learning may be required when starting off, self-learning soon replaces it, and individuals become their own guide with maturity and confidence. While there is no clear answer as to whether CS or AI/ML degrees are better, the number of people graduating with guided data science degrees is increasing significantly.

There has been a lot of change in terms of the companies and sectors that were previously involved in AI research. Many companies have now spearheaded AI research, and the most promising latest research is coming from them. Even within companies, there has been a diversification of work, with a lot more focus on computer vision and advertising data.

One significant development in computer vision is the Voyalla and Johns paper, which has increased the detection accuracy of faces from 60% to 99.99%. There has been a flurry of papers published on people detection since then. Overall, advances in technology have made building companies around AI justified, and there is a lot of excitement about the possibilities.'