What is the connection between artificial intelligence & machine learning?

Applications in these areas and they’re getting better over time super is futuristic sooji right now only exists in Hollywood movies and fiction novels and the fact one of the quotes that I really like about you know this whole debate about whether AI will take over the planet in their arguments would be delegated as slaves were given the Andrew ng one of the foremost thinkers on this area and he mentioned it that he worries about soup earlier in the same way that he should worry about overpopulation in Mars right.

It is that far Thornton right but let that not distract you from the application and the reality of AI that surrounds all of us today on that note let me take you one level ahead one level deeper right and let me try and explain to you three terms that are often used interchangeably and often quoted out of context right but one of the things that I thought I should try and do is also demystify some of these since we all right.

So we’ve spoken about a yeah that what does AI really is essentially you’re talking about developing algorithms computing systems and machines in such a way that they can understand context understand data and give output whether that is inside whether that is information any of the output that the system is supposed to give at a rate that is comparable to humans or at an accuracy rate its competitive with comparable to humans or slightly better than humans right so that is the broad new world of AI now there are two other associated terms one is machine learning and the other is deep learning first.

Let’s talk about machine learning so machine the science of machine learning’s it’s a complete field of computer science right so it’s an entire field of study and that is something that we all want you to appreciate right it’s a very vast and deep field the field of machine learning is all about creating algorithms using certain techniques in such a way that you can train these algorithms over the period of time as more data gets ingested right so the algorithms start learning and start becoming smarter thereby their output starts improving over a period of time and machine learning and AI are very iterative because machine learning is one way of implement in Ai

Mlis a set of techniques to implement AI right.

The connection between the two so obviously the question that may come up in your mind is that can you do AI without a machine gun and the answer the theoretical answer to that is yes you can write it you can implement it without machine learning but implement AI without machine learning the effort and time and investment that’s going to go in you know commune maintaining millions of lines of code and constantly updating it and you know deploying it is a lot of lot more than that is why it is not feasible which is why the only practical way of implementing AI is through machine learning right.

You really cannot advance an AI unless you know machine learning because that is the only way that hey I will get implemented in a scalable and sustainable fashion right because as I said what you’re trying but the systems that you’re building the engines that you’re building by using machine learning are in such a room okay that they improve beautiful times let your effort does not increase non linearly as the scale of data increases then it’s not sustainable right which is my machine learning is important to machine learning then further breaks down into multiple categories whether it is suitable earning unsupervised learning reinforcement learning all these are a bucket of technique is depending upon the problem you’re trying to solve but all of these are means to an end and the end is essentially implementing in a burrow di solution all right so what is important for you to understand is that machine learning is a way or a means of implementing AI.

What is deep learning is an alternate set of techniques that you will deploy at some point in time when you want to take the accuracy of your system even higher when machine learning models can only take it to a certain scale and then you deploy deep learning techniques to increase it even further and I’ll explain that with an example but the broad difference here is that deep learning the whole science and field of deep learning is all about creating algorithms of creating systems in such a way that they mimic the human brain right and obviously there is a lot of science behind that like when you think about the human brain you know that its capacity to analyze to consume information to give insights is practically limitless.

It can do all of this gathering the context that exists around it right so deep learning is when you are actually trying to build solutions right when you’re trying to build recruiting system in such a way that you can enhance what machine learning has already achieved and you’re trying to take it even further some for every problem that you face it is not as if you need to build a solution using Duplin but it really depends upon the level of accuracy and the complexity of the problem the level of accuracy that you want to achieve and the complexity of the problem that you’re trying to solve in today’s world there are a far there are far more applications of machine learning and that’s why there are so many jobs in machine learning and deep learning is something that is now beginning to kick in because for a lot of these organizations that are implementing machine learning solutions.

They’re one of their focus areas is also how do you enhance on top of that and that is when you know areas such as deep learning come into prominence let me go back to the example that I was explaining to you which is image recognition you know the deep freeze algorithm by Facebook right so image recognition is typically about the first you know examples or case studies or walkthroughs that you will come across when you begin your journey in learning machine learning it’s a classic machine learning problem so when Facebook.

Now if he came back about the journey that you know the machine learning engineers in the air engineers would have made at Facebook to bring the face to life obviously what is the problem that they’re trying to solve the problem that you’re trying to solve is that they have to build an algorithm that recognizes human beings based on pictures that they have right now typically when you try to solve such a problem it is not as if we immediately jump into one solution and tribal to start building it right.

You will first start building a model on a smaller data set that gives out a certain set of results in a certain at certain predictability right now go back in time and all the times when they were actually tagging people manually what you were doing was that you were training the algorithm the data you were saying that with this picture belongs to this friend of mine who’s by this name right and this other picture also belongs to this friend of mine who’s by the snake right.

What is driving this whole world towards an AI?

The first talk about what really is AI and you know what is driving this whole world towards an AI revolution as everybody keeps saying like what are the trends to it what does it mean for professions like to and also try and attempt at demystifying some of the terms or jargons that you may have heard where there are many of you may not be very clear where what the distinctions are so terms such as artificial intelligence machine learning deep playing data science and so on.

I’ll attempt to try and explain to you in very simple terms in terms of what – each of these mean and also give some practical examples and walkthroughs so that the distinction can be clear your mind the second half of this webinar shall focus on the program the postgraduate program and AI and ml audition intelligence and machine learning by bay TX and Stewart School of Business why is it such a unique program what really is its benefits you know what are you likely to learn what can be your learning outcomes and you know more features about program towards the end all right.

On that note let’s get started so I’m sure that you know you all have heard artificial intelligence this is a term right but let me ask you a question what do you really envision when you hear artificial intelligence what’s the first thought that comes to your mind and I’ve been doing this you know pop quizzes with various audiences across the past one year and you know here are some of the most common responses that I get and I’m sure that you know how many of you there would be the first image that comes to your mind for many of them and many of those people when they think about AI or intelligently minds immediately races towards some super-intelligent humanoid robot the kind that you see in sci-fi movies alright a crisis of the machines.

So forth another image that really comes up to all of us is the driverless car a self-driving car right again it’s been in the news if experiments are happening there pilot is happening so that that is a very distinct reality is not so far future over to the mold enhanced you know prospects and folks who’ve been reading about this a lot I’m sure alpha goo is an image that comes in the mind comes to mind we’re in you know for the first time a machine actually defeated a human consistently in a game of go but there is one common aspect to all these images right which is that there we are looking at a world where machines or computing systems.

If I may call them in more simplistic terms you know computing systems are actually making decisions like human beings – and that really is the crux of artificial intelligence right if we remove all the fluff around it if you remove all the jargons around it what AI really at the core of it means is how do you make machines behave and take positions like human beings with better accuracy and better predictability alright so that is what all systems are trying to achieve today as we speak and that is the world that we are moving into brought clean when you think of AI how will you think of artificial intelligence there are broadly three categories of you know that these fall into the first is what is known as narrow.

AI narrow AI is where you know you have developed an algorithm or your machine or computing system is extremely capable of doing one task and performing it at an accuracy like humans are better than humans right but it is only for that one task right so a very common example that you may all relate to here is you know image recognition so there was so one of the pioneers of one of the companies that have done really very well when it comes to image recognition based recognition is Facebook and I’m sure all of you if not most of you are Facebook users so you guys know what I’m talking about you know when you upload pictures on Facebook nowadays you know the Facebook algorithm doesn’t really ask you to tag them.

It can identify who these people are there was a time until about 12 months back when you had to actively tag people right all there were suggestions that were coming and nowadays for most cases that do not happen right so that is an AI algorithm that they built which they call as deep face and the deep faced accuracy of recognizing faces, by the way, is 97 percent it is currently higher than human beings have an average accuracy for 92% the deepest algorithm has a killed 50 97 percent so that is a classic case of narrow AI where that one algorithm right the only task it has to do is recognize faces obviously it’s easier said than done but that is one task that it has to do and that it performs at comparable or overhead better accuracy than human beings right.

The other broad category of AI is generally I eye is where there is also context it is not just a task but it’s about a series of adjacent tasks and now the machine can perform those tasks right to relay that to an example a perfect example would be any of your voice assistants you know whether it is Google assistant or whether it is City or whether it is Alexa right in all of these cases it is not as if you’re restricting your query you only do one aspect right it’s not as if you li ask Alex of whether to today right or you only ask Google assistant about you know what’s my schedule for the day right you are asking these systems these machines a bunch of questions a bunch of adjacent questions.

Where there is context and the algorithm is working and learning in such a way that it is it is aspiring to improve its accuracy and responses overtime right and for any of you who are active users of any of the voice assistance or chat boards you would see that over a period of time the accuracy improves over a period of time the system starts learning what you really mean right and starts giving you better this one so essentially in many ways you are training the machine and that is what he is all about but the machine is learning right to respond better so this since this is a classic example of generality I were the use case is not restricted to one specific task or one specific area but there are adjacent areas and there is a context around it.

The algorithm of the machine has to be trained enough to pick up that context and thereby give responses or give insights or answers that are more meaningful the third category of AI is super AI as the world calls it so the soup when you talk of super AI it basically you know you talk hang about the world that has built machines that are just much smarter than human beings and everything that humans – all right so imagine you know we like matrix coming to life, for example, right so it it is a world like that and obviously this today isn’t the realm of fiction whether it will happen and when it will happen is a different matter altogether and I don’t think that is what we want to talk about today but this is far from reality right now Natalie I am generally I am where a lot of work is happening a lot of evidence is already pulled out and you already have systems.

What is AI NFL?

I think the LM ml techniques are fairly different right and I describe that BL is very different you know when you have to go from 70 percent to 99 percent that’s where you start using the L techniques okay it’s machine learning helpful whatever price yes why not actually a lot of our alumni come from this background and we have seen that all of these guys have been able to either have a better role in their own companies or have been they have been able to make those career positions for themselves yeah I spend with holy 21 is DDoS specific to India.

No, I think it was worldwide what is the ability of this program is a means of the market as I was saying in all major companies right our alumina are working in all major companies and what fear realizes, first, I mean having there is definitely an understanding in the industry the business on the program that we are running right but above all what is has started happening is the focus on the certificate and focus on this more focus now on the learning.

If you can demonstrate to anybody that you know about these techniques that is what companies are looking for so definitely there is recognition for our certificate right definitely it does help you get that foot in the door and that conversation going but at the end of it what it boils down to is showcasing what have you learned right and learning outcomes are very very important the bunch of questions around from different fields switching to AI NFL I think this what also there is that for all domains AI NFL techniques are going to disrupt a lot of domains were of companies almost all right.

I mean things like taxis hotels right contend all of these domains have already been disrupted by a and ml right and their banking etcetera now companies are working hard to destruct some of these traditional strongholds of highly regulated industry sectors right where it’s not really easy to make inroads so as we go along this will disrupt almost every sector and company and then it’s from whatever background you’re coming from right you can always marry this understanding with that background you can always position yourself for a better role right and a better career outcome is the online time-specific the way we run our online courses.

I said it is coupled directly with so it is enabled with personalized mentoring so what we do is there is content which is shared with you every week we’re supposed to the only discipline that we need from you is to consume the two to three hours of content video content you have to consume and then there’s a week on the weekend there is a mentoring session you have to come prepared for that and that so then it is basically on in that week it’s all flexible for you how do you want to structure it how do you want to consume the two to three hours of content right mentorship sessions happen at particular time slots but then again choose a time slot in then get according to your availability in terms of online course do we just watch the videos and not know.

Furthermore, I think the way it is different is that there is personalized mentorship so every week you get to meet a mentor and that mentor it’s only a group of 10 10 people to one mentor right so you have a very one-on-one come and then you meet the same mender Vikas we can be for the whole year so so then the mentor starts understanding what kind of things that you need that you write em any way you’re getting stuck you can be sort of the mentor so so that’s the reason having that support system that unique support system in terms of that mental being there and obviously as a team we are great learning in terms of technical experts who are there to help you.

Furthermore, I think that is what we an argue makes a lot of difference in learning outcomes and that’s the reason it is the course that we offer is not comparable to any online course online courses as I said have completion rates of only 20 20 to 30 percent our courses have completion rates of 90 percent plus which is staggeringly it’s very different from how the other place in industry work maybe some kind let me just see there’s any other in distinguishing other than the other theme of questions is more on hey I have 10 plus years of experience have 12 plus years of experience would this course help me on how what kind of career outcomes can I look at.

If I have already half Phineas of experience so in interaction with industry what we realize is as they’re moving towards more machine learning Nazi themes in projects right they need leaders they need people who they need leaders to believe these projects right so they’re looking for the uber seasonal needing big teams who are seasoned in managing clients and all of that right to need some of these themes and projects and there’s a lot of scarcity in the market in terms of experience and knowledge about machine learning and AI.

So we see when we talk to the companies or he realizes they are really struggling to find top talent in these areas with a good demonstrated experience of you know the business impact and business outcome so when you marry that your rate shear so experienced with this these techniques right then it becomes really powerful and actually, companies want to hire that kind of patent, so that’s a reason if you have a lot of user experience right but if you have a say if you have been managing deans managing clients managing missus verticals outcomes all of that plus machine learning makes it much more powerful.


Here you can learn all the techniques of Machine Learning & Artificial Intelligence.

What we help them with is defining the scope of the problem defining the approach and all of that right but with that, you can build a portfolio of projects that you of the problem that you want to solve great that is very evolving and contemporary.

These three things what we make sure is that the learning outcomes what people get a tender program are wonderful right and that also translates into really good career outcomes for our students another man okay and we covered all the techniques which are using the machine learning and AI all of them are covered in the course right so deep learning neural networks right we use Python Python is the language of choice for especially for AI algorithms like TensorFlow etc developed are developed in vitam we cover all the basics plus you know.

We don’t need you to be proficient in Python during the course we ensure that you build a base in Pike ton and then start using it so so there is no expectation for you to come coming into the course for you to have a prior understanding of Python right and the other thing is known focuses in addition to having theoretical concepts covered but so that you have a good appreciation and understanding of all of these techniques he focuses also and actually more on making sure that these are married to the applications right to your business understanding so that you can use these techniques to solve real business problems, so this is more of practitioners perspective that you collide and then you have a portfolio well plus in the sea projects this is the structure of the program right we start with a little start a little slow by building a base in Python and stats then we go and covered in machine learning techniques.

In the AI we come to cover all the relevant use cases and deep learning using your networks which are computer vision NLP intelligent agents it’s a girl thing and the AI part of the course has been created by IIT Bombay professors for us they again there’s some examples of the kind of projects which our students have done looking at let’s say New York City’s taxi supply using looking at data from uber like some of these from which the company still uber is trying to solve and kind of evolving problems is what we usually do as capstone looking at cash customer sentiment forum is on reviews like all of this we just give examples on how our projects predict our structure than they’re always on the cutting edge on the things that and temporary problems with industries also trying to solve eight, so these are the prerequisites for the program and I get a lot of questions on this that do.

We know do we need to know Python do we need to know programming language programming before a friend you know signing up for this course you know the simple answer there is as long as you’re not scared of programming right then you are good because whatever you need to know and Python is a very easy language to learn right it’s unlike like C C++ where you have to write lines of codes Python is a very convincing language with like two-three lines of code you can not an evil network it’s very very easy user friendly and easy to use and very easy to learn right so so you don’t need to know but you should definitely not be scared from writing up some lines of codes.

So that’s the prerequisite of the program right and these are some of the other things that we do to ensure there is career success for each participant so we do a lot of career development workshops right where we talk about what emerging trends what kind of roles can you look to apply for what can a force exists right what kind of transitions people have made what kind of skills do you need to have all of this right industry connect through mentors who are in a lot of we also do career fairs at regular intervals every month lasts a career favorite it was in Chennai where about 20 companies came in on the same day and about 200 alumni attended that very affair and 140 of them got you to know further letters of intent from that process.

We keep doing it and all our alumni and students invited us to all of these career fairs like peer to peer networking we keep publishing opportunities lateral opportunities on our accelerate platform right so all of this is this ecosystem this support system also is in place to help you once you have achieved learning outcome to also help you get the career out comparing self okay and if you look at this list where our alumni work what you realize is that now and this is only an indicative list right I’m guessing any major organization we have a relevant I’m working there and that too at a fairly you know mid to senior role in ml Nazi domains right and what you’ll see here is that the companies are secure are not particularly are not limited to only some domains.

It’s like 10 years ago it used to be only technology big technology companies like Amazon Facebook Google right but now you see things companies like oil and field right Todd’s Apollo Hospitals so across sectors now there are opportunities and machine learning and AI which are across sectors and as I was talking about this is more a broad-based opportunity which is evolving like and this is the usual the kind of analogy which I take here is you go back to 1990s right where the whole idea revolution happened it started with only a few companies but within five-six years every company was utilizing it.

Now you cannot imagine a company that is not utilizing you know complex IT systems similarly for this within ten years it will be unimaginable for any company not using machine learning or AI, so that was all for me let me just see if there any questions okay let me just quickly go through some of the questions, yeah, so one question is if you go to 99% accuracy with deep learning does it not over for it right, so there is the way this deep learning techniques are defined the going to define are very centered on the outcome that they have so that ensures there is no overfitting there right and so some of these details yet you will learn in the program late can I understand the DL is the enhanced model of ml.

How can you relate Deep Learning with Artificial Intelligence?

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.