Rewrite in conversational style but without compromising on the technical discussion, 'Like 20 years, probably. Tell me a little bit about your career, how you got started in this field. Where did you of course, I know. In institute. You were dabbling around with image processing and all those things. So what was your journey from there to starting your company? Yeah, so just tell me a little bit about that. Yeah, this is going to take a while to answer. We have all the time. I think, as you know, some parts of it. Right. So I think in second year one. Yeah. So second year, third semester, winter break. Right. I think in December I stayed back in the college only. So I don't know why exactly I stayed back, but one was in the third semester, I was expecting to get not so good grades in some of the electrical courses. Right. So maybe it's just one way I thought that I'll make up for it. And I went and spoke to Professor Jalihal know Jelly Hall? Right. So he was our hostel warden or something was there. So saying that you are our warden, I just walked into his room and I told that, can you give me something that I can work on? So I think the first thing that he asked me to do was, can you take a color image and convert it into black and white? Right. And after that, he said, can you also convert a black and white image into a color image? Right. So this was a time where I had no clue anything about images or no clue about anything at all. So he could have asked me to do something in communications or signal processing or something else, but this is what he asked me to do. So if he had asked you to something in communication, your career would be completely different. Yeah, quite possible. Because I think even before I chose my master's thesis, this was definitely two years back. Right. And in those two years done, I've done quite a lot of small, small projects within image processing. So by then, it was like, pretty much I knew what I'll be doing for my master's thesis. Right. Yeah. So yeah. So converting black and white sorry, color into black and white. I think that was the easy part. I was able to figure out which tool to use, how to read an image and how to convert it. I think that was fun. But then black. I think back then, we used to use MATLAB, right. Yes. Student version or some freely available version of MATLAB. Everybody was using that. Probably some pirated. Version. Yes. Quite likely. Quite likely. So that was one of the high level languages available to us right. Where you just write one line and you get a lot of functionality from just one line of code in MATLAB. Right. And who are not really from proper coding background. It's like a godsend. Yeah. Today, probably, Python has probably replaced all that. Right. So back then, it was not there. Yeah. I still feel that if not for Python, I would have.  I I still feel that if not for Python, I would have I I would have gone out of technology long time back. So by the way.
Relative I thought it's like the cheat code for non CS people to do some interesting things. Even I use Python a lot even though my job has nothing to do with.
Right. I think it's very less intimidating, right? Maybe probably the least intimidating language where you can get started very quickly and start seeing some results and that'll motivate you to do more, right? I think that's.
And also the more part is also there right? Because.
You get a lot like after you get past the initial one, you can do almost everything in there, so that's not so great.
Yeah, yeah, yeah, yeah, definitely right. I think. I feel like it's it's the minimum that you need to write to get the maximum output out of it by then, right?
OK, coming back to Matlab, yeah.
Yeah, so, yeah, so.
Another image, black and white to color, yes.
So color to black and white, it's those, you know, barely maybe some 5-6 lines of code in in MATLAB to read an image and to convert it into a area of numbers and then taking average of those numbers and then printing back the image, right? I think that's what it is to convert a color into black and white, but black and white to color. Mathematically it's not possible because you are.
Going from one number to three numbers. So from one grayscale number, you have to come up with one green 1 red number and one blue number, right? So mathematically, it's not really possible. That's what I went back and told him that, hey, you know, this is just not possible what you're asking me to do. Was he just testing you? Even he was curious, back in the day aiming to do something like this was unheard of. Nowadays it’s a piece of cake… training an AI on grayscale and color images - it comes up from the kind of structure and texture, we decide the color. If its a face, then human skin color. Anyway that was the project, looked at other functions on MATLAB what - one more thing I liked was the results are visual in nature - like remove noise, increase pixels, add mathematical dimensions- you can see the results - visually made a lot of sense - so interested in image processing early on. I then did a couple of internship in Giovernment Research Institute -we used to get remote sensing data - satellite image capturing data - fpr each point on the ground you can get 8-9 numbers - we were able to do a bunch of things - classify what kind of vegetation was there - spectrum for deciduous and coniferous forests is different - in RGB there seems to be no difference in the color. Even water bodies have different spectral characteristics.. Interest in image processing went more - internship in SIEMENS and Microsoft .. presented a paper - I did my Msatsers in background subtraction - worked on SIEMENS for more than 5 years - medical imaging and survellieance. Even within medical imaging - regsitering CT scans and MRIs - CT scans have structural information of the tissues an bones, MRI has functional information - where more glucose intake is happening - putting these two together you get insights. After SIEMENS (2007-2012), image processing and computer processing in early stages. constrained problems - customers walking in showroom - no fixed way where people can come, people can come in clothes people can walk anyhow - 2007-2008 - that was a time where none of these unconstrained problems had any commercial applications, unlike today. Medical imaging and machine vision where constrained problems. At that time compute was expensive, neural networks excited as a concept but not as a solution, quality of data is low. Cloud adoption also started increasing. 

I can go through two different scenarios - we were building some replicating some work in face detection, we came up with a scheme extract a million of features from a small image - vertically, horizontal, different scales, and which features represent facial pattern - new piece of work. Differences between how easy or difficult it was back then. Nowadays, all good research you will get open source code available - you can clone on a project on GITHUB can replicate the result. Majority of time in research back then used to go in replicating past results and then the present before publishing. All reputed researchers put out their code as well. If I look at the AI space as of now, I feel availability of models and new ideas and diff architectures is free, anybody can search and get new pretrained models online - the problem is now on distribution side and making it work for a specific client. It’s not like there are no good face detection models available or people detection models - understanding the constraints of the client ecosystem and making it work in the production setup and distributing the solution that has been built - that’s where the bottom line lies. 

A small dataset of 5000 models were available - spend a lot of time to create a large repository of data, be mindful of any negative datasets - now if you search you will find 1000s of datasets of different varieties. Even training a model back then was a 1 month effort  because of the compute because not even having high level tools to ensure the training is happening properly - the same computation takes only a day now. Even of the dataset availability asnd computing makes a difference. If you look at almamater, multiple groups focusing on AI, every department, chemical, mechanical. Groups across dpets coming together and building AI solutions - this level of interest was not there back in the day.

Some of the specifics are not useful anymore - I used to do code with C++ back then, but now we can build production models without going into low level language like C. Some concepts of C++ are still valid, you are in middle of training, you want to run into a subject of data, run iterations and then run on large datasets. Back in the day we were really careful to run data without errors to not waste time, to finish one interation it would take 6 hours, but now its faster. 

We are going to higher and higher levels of abstraction. At any point of time, there would be a set of people working on low level language - but majority dont have to do that anymore. Right now we are comikng with the right prompt design for GPT 3, not writing code but thinking of what kind of prompts to give. 

Lot of specifics on the languages we now in the past and now its only python. Even research is done is different. The kind of focus now and work awcorss departments is different as compared to before. Many companies are requesting for AI research, spearheaded by industries than academia, the industries have awccess to compute clusters, most of the promising latest research is coming from companies. 

How to learn itself - one statistic - for anybody - guided learning is what you start off with, slowly self learning replaces, you become the guide yourself with maturity and confidence. Iwas heavily guided for IIT coaching but post IIT once you are in a job, you become a self learner, the style of learning evolves. Now there are 4 year degrees in AI - from reputed institutes. When we are hiring we dont look at the degree, we give a set of assignments after which they are good to go. Don’t have an answer CS or AI/ML degree is better - AI/ML degree candidates work well. One thing is for sure - number of people graduating with guided data science degree is growing a lot. 

Even within SIEMENS, I started off with surveillance side, tracking people and last 2-3 years worked in medical imagining - only 3 companies were working - mainly SIEMENS and Philips - I felt like I am doing something niche - I felt like I am narrowing my options - I mopved to Visury - Indian Adtech startup - pretty big change for me. Vizury was showing relevant display advertisements to internet users - who should see what advertisement, how much you must bid to win a slot - you come up with an improved algorithm, you see the revenues of the companies change drastically. If someone pays after clicking on the ad - then Vizury gets paid - very interesting and dynamic and got interested in the startup culture. 

Even computer vision, back then much was not happening - I was doing other consultancy gigs and then I got to know a lot of action was happening - it was matter of time before I started working in computer vision - advertising data, structure data was interesting - gave some diversity in terms of experience I had - solutions that I am building at Akaike Technologies is also diverse. I worked in Target- I was a lead data scientist - I had the flexibility to work on different things - worked on vision and advertising data - leveraged both of them in Target - part of ad recommendation team and also instore cameras - suddenly 15 people entered at 10am - then we know we need to increase the number of people at checkout - staff optimisation is one of the use cases. 

Vizury being a small data science company - only 2 data science probs, who should see what product ad and how much we must bid to win an ad slot. We came up with different algorithms and dynamically increase ad traffic with those algorithms. Target had many other problems. I was itching for high growth that was possible in a small company - I worked in Accenture - I got back to working in startup - consulting for few companies under north west umbrella - after getting that experience - I can step out and consult with any company - when I was consulting and writing code and coming up with ideas on product - 2017. AFter one year, felt why stick to these companies when I can do the same with others - 4 companies I was consulting - Northwest partner - healthcare, lab creation, streamlining communications etc. AI adoption was lot more in other verticals compared to healthcare - started consulting independently - 2018 - Akaike Technologies. 

When I came back to Vision, some amount of learning in terms of latest technologies - some of them were edge solutions - we have to run teh algo within the compute that was available in the store - so we couldnt use elaborate deep learning models over there, only older models. A lot more learning happened through independent consulting. Over a decade we struggled with sub-optimal results in computer visions, computing and libvraries were advanced, really pushed me - something magically is happening - building a company around it is justified. Quote , ‘Any technology that improves on the previous feels like magic.’ Somebody graduating and playing with these tools they wont appreciate how advanced these models are and dont break often. One big breakthrough in computer vision is Voyalla and Johns paper - before that detecting faceaccuracy is only 60% but after this paper - its now 99.99% -one big jump - flurry of papers for people detection also. '