What method should the universities adapt to make the students competent as managers in utilizing the current technologies like AI and not simply users of the technical operators of the scene?
I: Very good question! The whole industry is struggling with this, right? And there should be a bigger partnership between academia and industry. If you go and ask, a lot of people coming in as MBA or engineering graduates, many of their skills are not utilized. Some of the courses should be relooked upon. Courses on AI, machine learning, NLP should be introduced. Some of the courses, if there is no direct scope, should be made optional or additional courses during summer vacations. Students should be sent on advanced training in machine learning in the industry. There should be offline training project contents instead of having in-campus projects. Institutes should introduce industrial projects, wherein people like us could outsource some of the projects to the students. These are the ways students would be able to use technology in a better and more employable way, rather than just being a mere user of the technology. So, become the creator of technology or create the use cases which can positively utilize the technology rather than just saying that- “yeah! I know this technology!”
“AI is more dangerous than nukes” that is what Elon Musk had said. So, at what point should we stop developing in the area of AI?
I: I wish I knew the answer. We saw what happened with Facebook. So, one of the speakers were talking about supervised and unsupervised learning. Supervised learning is when every time we tell the bot that this is the question you did not get and this is the intent of the question and then we make them. Unsupervised learning is – if it could not get the question and then if it asks the question in a different way, then it also infers that the last question was asked by the user in this way. And that could be dangerous or it goes through hundreds of articles on the internet and the example is when you go to Facebook and it says you might be in this photo, nobody has tagged you, but it says you might be in this photo just with the facial recognition technology. Now, it is going to the internet and getting that data, so that kind of thing, at times it could be a blunder. Think of this, if it is making decisions based on that and it goes wrong, like what facebook messenger had done. The same things could be happening in critical things like government decision making, diplomatic decision-making.
That is what Elon Musk is referring to. This is one extent, again there could be other extents. Again, I do not know at what point we should stop, If you ask me, we should not stop until AI powered systems provides me with the outcomes like a human being does.
Thank you for your insights on the questions we had, we are really grateful that you gave us your time.
I: Thank you, appreciate it. Take care!
Interviewee (I) - Mr. Kapil Mahajan
How would you describe your experience of coming to XIMB and addressing the students here?
I: I am always excited to address the students. When you guys reached out to me, I was pretty excited. You managed it very well, the coordination and communication, was pretty good. This is my second visit to the campus, I had come here long back for campus recruitment for IBM, and it’s been a good visit this time around. From the few students that I have interacted with, it has been a positive experience.
Sir, the newspapers today are flooded with articles of Artificial intelligence. So, we wanted to know from you that how difficult is it for companies to implement the same?
I: I think that there are a few companies that are doting around the idea of AI. I think what is important for AI to work is data thereby underlying the importance of data, if you don’t have data AI will not typically work for you. If you have a lot of unstructured data and you want to mine it and get some insights out of it, could be a major game changer for you not only from an operational benefits standpoint but also form a comparative standpoint also. So, the idea of using AI is again a very diverse field. Most companies start small and when they feel that they gain enough and makes betterment with small POCs and when they feel that they are starting to get results, they start exploring much bigger use cases. I think that is the strategy to follow. We are a little far ahead in terms of maturity with respect to AI, because our smaller experiments worked pretty well for us.
Now we will be looking at how we can use AI in every part of our business operations. So, it would complement every business process that we have. In decision making, so, that is where we talk about the humans and machines co-existing and complementing each other. I think, if executed properly the strategy could really pay off.
Thank you sir, for sparing your time, it was a pleasure interacting with you.
I: Thank you, the pleasure was mine.
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