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 to use (NPL) Natural Processing Language?

My focus was more risk in operations and I was leading a team of data scientists after their right so within my previous role I was working on a use case where we were using you know for we were trying to optimize her customer care spend call center to spend.

What we’re trying to do was using natural language processing NLP which is a deepening technique we would figure out and also utilize understanding by understanding exactly you know what kind of activity has a customer done before calling us it’s so basic idea is that people have issues and then that’s the reason they call customer care for an easy resolution right so we try to understand and try to predict that what would be the reason before they called us if they called us.

What could be the reason that disguised calling us and using some of the initial sentences which deciphered end we changed the idea menu so that the first two options would closely match what this guy was the reason this guy’s calling for like and then what we realized was in 60% of the cases where this guy would have otherwise moved to her to speak to an agent the query was resolved in a much shorter time and with no cost because we had the solution ready for that customer so some of these use cases so many companies many businesses have these customer care centers and call centers right.

Now imagine some of these use cases becoming more popular and they become utilizing it they have to write because if the competitor can cut their customer care center by cost by half and if they’re not able to then they will lose the competitive edge so so this is a particular specific use case which I have done using deep learning, but this is it’s a good example on why when every company has to go through this journey and start adopting some of these advanced techniques right and what it means is that the jobs’ creation in machine learning and AI and deep learning will happen first of all it has been happening at a very healthy rate right now.

So in the last five years, we have seen 5x growth in jobs for AI right and ASSAM predicted that in 2018 last year that we have created a hundred thousand jobs in machine learning and AI in India itself right so we have seen that they seem very healthy demand for machine learning in AI but as he’s going on this rate this demand is going to become higher and higher and higher right because of some of these rapid developments and use cases and companies adoption right off some of these techniques okay, so this is a slide which summarizes exactly what kind of trends are emerging in AI right.

I want to you know I talked about the bottom right corner which is for point-like 5x growth in jobs requiring high skills since 2013 in the five years since 2013 we have seen 5x growth right the other thing which I want to point out here is the error rate which is the middle left and the adjacent box has gone down from twenty-eight point five percent in 2010 and in seven years it has gone down to two point five percent right and for Facebook B phase that is now in 2018 it was only 83 basis points right so now with thinking drastic improvement in the algorithms a lot of use cases which as I was describing some of these we cannot go to market with a 30 percent error rate within some of the use cases but you can definitely go to market with any 80 basis points every bit.

So when these use cases are now becoming more and more mainstream right and can now be applied to a variety of business problems so so then that is what is fueling this growth in demand for AI and creating a lot of jobs in AI right the other thing which I want to point out here is that 84 percent of companies have already invested in AI o me that’s a staggering number right 84 percent and I was surprised when I looked at this number because from our popular perception from talking between us from our between appear to grow among appear group right the sense that we would have the others would be of a lower number but 84% is staggering I mean that’s.

We do see that momentum building towards more jobs and more demand for AI and we saw that happening, ut this momentum is building to get more and more of you know these jobs created Nicola right and these are some of the charges which this point which means the same point which I was making that if you look at the trend of AI course enrollment that is just picked up beginning let’s say 2013 2013 2014 you see a dramatic change in the slope right and that’s where these some of these use cases are becoming more and more production-ready.

If you look at ml course enrollment the chart picks up a little earlier it picks up from 2012 right because machine asset business machine learning has been fairly pervasive and I think some of these guys some of you who are in the service sector would also have realized within your work in the kind of projects that you’re you’re getting from your clients these days.

I think that most of these projects are in machine learning there is a marked shift in the kind of projects that are coming from some of these companies right in you would also even otherwise in your workplace you would see that this is a kind of shift which is happening and if you break it down further these are the skills which are come which companies are looking for which is a machine learning deep learning and again we see that there’s growth since only 16 right and it’s just growing at a very amazing place and all of the skills which are in demand machine learning deep learning natural language processing computer vision speech recognition right all of these skills are first of all they are within that realm of machine rolling in AI skills.

These are all the skills which we also teach in our program and you will learn in the program late the other indicator is GitHub stars which basically is you know awarded to people who are contributing meaningfully who get up and get up as a repository where some of the special professionals using machine learning and AI techniques use as a platform to showcase the kind of work that they’re doing right and you see what we see is that TensorFlow which is a library use for more deep learning algorithms in sci-kit-learn is a Python library which is used for machine learning algorithms right.

We see a much more rapid trend or increase intensive flow since 2016 almost starting from the same level as we see that TensorFlow is now growing and obviously the machine learning algorithms also currently and by the way GitHub we in the program be also us get up because the idea is that whatever you do in the program so during the program what each participant gets to do with us is to work with a lot of projects in lab exercises right and all of these are based on simulating some of the business decision or business situations and you know all of the algorithms that you apply you can get a sense of how do you get to apply that in your business.

All of these projects are submitted on GitHub and then each participant builds a repository of projects that he has done on GitHub which again can be used to showcase the kind of experience the kind of learning each participant has and what you observed is you know for this technical community for the community which is working on ml in AI that’s the kind of frame we also see for let’s say for job applications or interviews where people expect to look at your published work and this is a marked change from the previous kind of skill set how the skills were assessed right.

I mean early they used to be a lot of in-person interaction and talking about your experience and all that today the whole shift test awards show me what have you learned to show me what have we worked on what kind of projects have you worked on the right unless discussing on the specifics of that project so having GitHub repository helps in so casing very easily even on the LinkedIn right the kind of projects at your work now and the kind of knowledge that you have in these areas right and the other point is that you know this AI story is not just standing out in the US or in the developed markets it is equally panning out in India like in the last decade the kind of model service model that we have employed in India right.

How to collect information through algorithms?

When you go to the web every one of you gets personalized ads based on what kind of size had you visited before right all of that information is collected algorithms are run on that machine learning algorithms are all of that and then a very specific for a segment very specific types of ads are wrong so I mean.

Now as we go along it’s not a choice for any business to not use machine learning they have to retain the competitive edge everyone is running these machine learning algorithms right but the thing with machine learning is that and in business scenarios in most business scenarios an accuracy or 60 or 70% is acceptable right so if you want to target some population for your marketing and decide which marketing campaign to run even.

If you’re going off by 20-30 percent that is fine right as long as what you’re most of your population is the target audience, so that’s the reason machine learning techniques are now widely being used in all business decisions where 60 to 70 percent accuracy is acceptable right that actually if it goes beyond 70 beyond let’s say 80 85 percent people often worry about that the algorithm is overfitting right then all of these concepts are you now covered in the course and as you learn about machine learning you learn about these concepts right, but that’s how it is that 60 70 percent accuracy is when machine learning you got them to see used now what is deep learning.

Now when you want this accuracy to go higher from 60 70 percent – let’s say 99 percent plus right and that’s a big jump the kind of techniques that you start using is very very different and all of those techniques are grouped under the term deep learning so I described to you the example of Amazon Go stores right there you cannot live with a 70% accuracy you cannot be billing seven customers right and three customers strongly right because that’s the perception that will build around wrong billing will build up and then your model will never succeed or if you’re doing image recognition you cannot be okay with seven people being identified correctly and three people not being.

If I correctly write so in some of these Vista scenarios and some of these use cases you need very very high accuracy upwards of ninety-nine percent and that’s where you start using the deep structured learning algorithms and this whole broad family of techniques under deep running.

So that’s the difference so to summarize I mean that was the difference between her xi artificial intelligence machine learning and deep learning artificial intelligence being the broad umbrella for any machine to do a specific task under a specific intake machine learning so slightly more advanced techniques which gave about 60 70 80 percent accuracy and from there if you want to go to 99 percent accuracy in above then you have to start using deep learning techniques which are again you have heard about it neural networks convoluted neural networks all of that right.

So some of these from the techniques using deep learning and that’s the V sticking on Alfred deep learning okay so let’s so this it was the session was the previous section was more about understanding what is AI right and just getting a little more sense on what is being talked about and what is a distinction between some of these terms right in this section we’ll talk about how does it apply to us how does it apply to the companies how does it change how the overall environment around us is changing right and how does it translate into jobs and opportunities right so in this slide what we see is a path weight from business intelligence and this is the path which every company will have to follow right in the whole journey towards AI.

So back in the 90s most companies were using data and visualizing the data and most of these companies were doing it not because they wanted to do it but because there was a regulation there was if they were required to do it by regulation right so, for example, let’s say putting the data together for financial statements banks would you know reporting there are risk metrics to the regulator’s right former companies were reporting some of the test metrics right.

So they were just gathering data and the max they where Boeing was just doing a visualization of DE tightness and then like obviously all of us have seen the trend charts and pie charts just very very basic visualization to understand what is happening right just figuring out sales forecast is increasing so one of that is clubbed under business intelligence right and they got more and more sophisticated they started using some of these security tools to do it in a better way but the fullest hence was to just look at the data and understand there any patterns in the data.
That used to be in the 90s for most organizations but as they moved along the second stage was using the data to do predictions, so people started building very very simple kind of models with this data or you know just you know looking at a trend in predicting it tears on the line.

All of these small prediction modelings started happening which also at that point of time involved let’s say regression allows the integration of all of that so so all of that started happening in the early 2000s which are found under Dixon breakage modeling right and then after that as the cost of storing data declined rapidly and also the cost of computation became much much you know cheaper and as we saw that most organizations were starting to the IT systems became more complex all of that this started gathering a lot of data right.

Now to process the data the whole concept first structure the data and store the data the whole concept of Big Data came into play right and then as recently as five-six years ago people just figuring out on using Hadoop and data Lakes meters to store the data right but with the advent of such large volume of data, although then there was a need to use more sophisticated techniques which were more efficient in handling this kind of data and those techniques then were called data science right and then from their companies a move to using machine learning techniques and for some use cases.

Now a lot of companies have started using AI and deep learning so so this is the way you know this is the data maturity in adoption in companies this is the way it happens in each company and any company that you think of is lying somewhere or the other in this park I can right now most of the companies are at least between data science and machine learning right and many of those are had crossing machine learning point and then they are moving towards utilizing more declawing techniques Hey but even then even.

If the company is not developed to such an extent right it is the next logical step for that company so every company will have to go through this cycle will have to sorry for this path will have to develop and use more and more sophisticated techniques as they go along right because as I was saying it is not an option anymore in this competitive world right and the kind of use cases that we are seeing right now which are enabled through some of these advanced techniques, for example, I personally I used to work at a bank called Capital One it’s the fifth-largest bank in the US and I used to be a director there and I had you know a team.

What is the logic behind all the Voice Assistance?

Nowadays it doesn’t ask us to lock it automatically recognizes people in the photo and whatever tagging that he had done provided for the learning for this algorithm so I got them learned to use that data and now it has become proficient enough to start matching human-level accuracy so this algorithm for accuracy is now 99.1 4% while human accuracy is just tired ahead at ninety-nine point three zero percent right good.

So now obvious single Gotham’s which are mirroring human-level accuracy but when we say narrow eye it is more about doing a specific task and doing it well the second stage of AI is generally I like in the example here and more let’s say your voice assistance like Alexa Siri or Google Assistant right so Erin you can ask you can talk to your voice assistant and ask that voice assistant a plethora of things you can ask it about the weather about a school for a game you can ask you to set an alarm right and later you can also ask follow-up questions with algorithms.

If you say something in some context ask a question some context and follow up another question the algorithm will retain the context it will know exactly what is it what context are you talking about right so so this is more, so these algorithms are more purpose to handle a variety of tasks and use interlink knowledge from one domain to another so then so so that was the example that is the second stage is Istanbul AI and the third stage of AI is super AI right in which kind of now goes into the realm of science fiction because right now we don’t have algorithms which are which can be categorized as super AI.

What super a mean says that sentient machines which have their own intelligence which have the context right which can act independently and are inherently smarter than humans right because once you impart intelligence to machines like the kind of data reading rate that they can access the kind of memory that they have the kind of things that they can link even now far exceeds that of a human so if you add intelligence on top of that then these machines become smarter than humans, so this is again this is on the horizon nobody knows what’s the fine line to this right and this is now going to the realms of but right now it doesn’t anything like this exists and it is now going to realms of science fiction and you know you know movies like dominated, etc so I just want to quote hang here.

In doing as you all know is a leading you know researcher and faculty in AI and machine learning across the world, so his coat is I worry about super AI in the same way that I worry about the overpopulation of mass right so from this code we can understand that this is a very far-fetched idea at the moment although from the recent trends what observed is that been a belief whenever we think of an idea is far-fetched.

We have been surprised and ideas have come to fruition much much sooner than we ever thought they would for example the voices sustained you know these days there’s a voice assistant in all our all of her pockets you know in our phones in terms of either Google an assistant or City it is there for all of us to access but the whole idea about a wise assistant or up coherent product which was demoed was only in 2016 right in Google i/o conference.

So within three years from becoming Justin from being just an idea this kind of technology is now ubiquitous it just resides with everyone we can access it like it is deployed at production scale and all of that like then it is very very accurate as well right similarly that say when I heard personally of Amazon go stores so I’m not sure how many of you are familiar with Amazon goes towards promises on both stores are essentially stores which are cashier-less so again the idea was coined in.

I think two years ago and the idea was that you can just walk into a store to pick anything from the shelf and then just walk out and you’d be automatically billed right and imagine the kind of accuracy need to have the kind of algorithms at play to be able to decipher what have you bought right an accuracy that you need to correctly will the customer right because of you go wrong building you know the product like this can never succeed you cannot be billing customers with the wrong amount right.

I thought personally when it was the cool idea was coined two years ago that it will take another five-six years for it to be implemented right but do have a surprise and many people are aware that we didn’t do yours last year late last year Amazon actually opened some of these stores in San Francisco in the Silicon Valley area of the United States in San Diego and La right so so we are now constantly being challenged on how much can we think how far ahead of anything right and whatever we think is going to think to take X amount of time is now happening much much sooner.

Yeah, so it does super AI do seem like a thing of the future but again we may be surprised and it may just happen much sooner cool so from that context on what is and what are the stages of AI let’s also try to understand some of these terms which are now used by everyone and there is a lot of overlap in these terms right beef everyone is talking about artificial intelligence everyone is talking about machine learning about deep learning right but I’m not sure that everyone knows about what is the distinction between using AI or machine learning or deep learning, okay, so AI basically is the umbrella term for anything which fit which machine does which can be termed as intelligent right so anything with where it is independent and taking its own decisions and be called as artificial intelligence.

It can be very rudimentary can be rule-based as well right even if you let say you know program your microwave for you know fully to run differently for different kind of food that also has intelligence like automatically if it runs so it can be very rudimentary but any kind of behavior which a machine is displaying which you know does tasks in an automated fashion where some calls have to be taken of the machine is taking it independently that has coined as article intelligence within that realm right there is a subset of that which is machine learning.

Machines which are powered by ML Algar so data science algorithms which are a slightly more complex kind of algorithms right which involve an element of learning rate which and by learning what I mean is that if you run these algorithms multiple numbers of times every run these algorithms will become better and better either this and learning you there’s a feedback loop by so what the algorithm does is it looks at the output it is producing the output which is desired understand from what is the difference then and then self-corrects to improve its output as it goes along okay.

So that is all machine learning so all of these concepts of random for s and supervised learning ensemble techniques right all of these techniques are used under this umbrella of machine learning and a lot of organizations use machine learning techniques to make their presence decisions and business decisions can be let’s say forecasting sales or figuring out who to go and do them with who to market or should they enter a particular market or not in all of these business use cases nowadays what organizations use are fairly advanced machine learning techniques they’ve gone on the days when you know there used to be only one ad targeted at everyone in a newspaper around or on a TV channel right now.

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.