How is it evolving what do we understand when we talk about some of these terms and yeah in today’s day and age the kind of use cases that are evolving for artificial intelligence and machine learning are evolving very rapidly so we’ll talk about that right.
I’ll give you a brief overview of what does it mean for all of us in terms of what kind of impact in terms of jobs creation in terms of the roles is it creating and then we’ll take I’ll take you through the program that we offer great learning very briefly towards the end hopefully everyone can hear me if not please drop a note and our team will work it out cool okay so so this is a question which I ask you know people.
I meet them for the first time and then you know when we’re talking about artificial intelligence is that what do they envision when they hear the word artificial intelligence right it’s a fairly broad term people are using it now MMM utilizing the storm for many things but what does it mean I’m usually the answers that I get is are let’s say the first chance that I get easily is in a major four machines which are super intelligent you know which kind of is similar to what we have been seeing in movies and like terminator etc a smart humanoid machines who can also think and make decisions like us.
So that’s one of the responses which I get late some of the responses which I get out of the driverless cars right and we have seen this technology evolve very rapidly in the past five-six years right it used to be something which you know people used to foresee would happen but the kind of progress it has made in the last three-four years has been tremendous right we have we are now seeing working prototypes and also production cars which are very autonomous right so in some and they’re only limited by regulations at the moment there are cars which can drive by themselves but it’s just that the regulations and some of the developed countries don’t allow a car to be fully autonomous.
That’s the reason they have you know the systems are built in where a human driver will have to be present and we’ll have to keep touching the steering wheel now and then to tell the Machine that it is to tell the car that a human driver is a present right but for all practical purposes we now have the technology we’re in a car can self-navigate then all of that so a wonderful example.
This is something that we but still it remains very popular we one of the things when you talk about the, is that we are our understanding of AI is kind of impacted by some of these popular themes right so if I need somebody who has a slightly more nuanced understanding of AI like the answer that I get is let’s say an example of alpha go right, so alpha Solo is a game which is mean practice for you know it’s a very complicated game and it has multiply it has like billions of combinations of moves which are possible.
Now we have an AI which has beaten a human player the human champion in the game of going right if you remember I think in the 90s there was an algorithm which beat which had beaten the chess players the leading chess players at that point of sign right, but there has been a sea change since then the kind of algorithm that was which you know had beaten the written in chess versus this algorithm which has now beaten players and go these two algorithms are very very different in chess the algorithm versus trying to predict the moves it was just trying to get to predict or think three or four moves ahead by looking at all permutations and combinations right.
It was a very different kind of algorithm with going what happens is and it was more algorithmic so when chess was it was more rule-based the chess algorithm right so it was human intelligence which was codified into a machine and then the machine had the power to just compute all the combination and looking more steps I had more moves ahead than a human player so it was a very different kind of algorithm going go the algorithms have changed completely Co is powered by deep learning is powered by artificial intelligence like what goes does is then Gotham here does what alpha does is it learn, so there are a lot of games which go into training this regarding.
So it learns from those games automatically there is no human intelligence which is provided to the silicon it will just play enough games and then understand from that what kind of patterns are emerging how should it play the game and from there then it learned and learned and then reached a point where it started beating you know the top players in this game, so this was this is a sea change in the way intelligence is defined I mean this also is is a good example to showcase how we have been defining intelligence right earlier it used to be something which we codify we take our intelligence and codify into a machine but now it is more about how we learn itself.
Now being codified hate again a good example a good analogy which I give here is you know how a human child learns right so some of you may have children right I also have a son right and he is only two years old but now and he started to speak now right but I never or we never you know went and told him about the grammar about how to form sentences it says that he observed from all the conversations which were happening in the household and then he learned automatically so he just looked for patterns and those patterns have given him the power to speak and that speech is going becoming better and better as we go along so similar is the algorithms that we have developed.
That’s the reason we talk about algorithms like neural networks right the whole neural network the term has come in because we have modeled algorithm in a way the human brain functions so so these are paver domes which are now very and that’s the kind of intelligence now which is developing notice when we say AI artificial intelligence these are the kind of algorithms that we are talking about okay.
Now let’s understand even further what people mean when we say AI and what are the stages of AI the first stage is narrow AI which is a machine that learns enough to do one specific task well enough right, so a good example is that say in let’s say recognition right so humans have been so one and the other thing which has developed is the cognitive ability for algorithms.
Earlier we used to talk about more task-oriented things and whatever we used to whatever was in the realm of cognitive understanding like for example looking at his photo and identifying which people are there in the photo that is a cognitive skill that we associate more with humans because it requires a lot of contexts it requires a lot of learning right but now, for example, Facebook has an algorithm called D to face which can look at a photo and INA fight which people are there in the photo so some of you who have been using Facebook for some years now if you remember if you go back to three years right you would remember that when we used to upload photos to Facebook to three years ago it would ask us to tag those photos with which people are presenting the photos right.