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.