Rewrite, '
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. '