A (very) Brief Introduction to AI & Data Science in the Industry 4.0 – DataDrivenInvestor

What is AI? Does data science impact society? How do jobs change? In the age of AI, data science and Industry 4.0, what should we expect for our future lives, jobs and relationships with technology? This article aims to answer these questions and more. Read on to find out more.

In the rapidly evolving digital world, it is crucial that young people are prepared for a future where artificial intelligence (AI) & Data Science will likely play an ever-increasing role in their work lives. In the next few years, AI & Data Science will likely make a significant impact on many aspects of our society, including our work lives. AI & Data Science will revolutionize the workforce and change how we think about jobs and careers. It may also enable innovations that help to improve life for everyone on the planet.

Even though some people refer to machines as conscious, intelligent beings that are capable of learning and honing their skills through experience and interaction with humans, these notions are difficult for people to accept in today’s context because it is hard for us to imagine machines truly being “alive”. However, the term “artificial intelligence” has a more specific meaning. It refers to the ability of software to replicate or contain aspects of human intelligence. AI is often contrasted with “narrow AI”, which refers to robots that have precise physical actions and movements.

In this article, I will attempt to draw conclusions regarding the future of AI & Data Science and its potential impact on society. By describing how jobs may change due to AI, I hope to shed light on this important future issue and provide insights into the evolution of technology and society as a whole.

Now, if you want to learn about the future and the present, then you always need to know the past because that’s very important and if you look at the technological revolutions, right? And I’m sure all of you must have listened to this and all of you may know about it, but I just want to spend a minute about this to say there has been sort of, you know, four technological revolutions that have changed the way we think or the way technology has improved and each one of these technological improvements or revolutions have sort of jumped or leapfrogged the technology for the entire human generation.

Industry 1.0

The first one is Industry 1.0, which is basically the steam engine. So the steam engine basically helped until that point, the people who produced and the people who consume had to be close to each other. The steam engine was the first time when you know, the people who produced and the people who actually consumed at a very fast pace because the steam engine is basically the engine or the train that pulled the train and it could take goods too far off places.

Industry 2.0

The second one was the invention of electricity that revolutionized because now you could work 24*7 technically, right? And also this mass production, so people could actually do a lot of mass production like telephones, bulbs, etc.

Industry 3.0

The third one was the electronic revolution. This is when the personal computers, your internet, your cell phones, all of that was basically invented or you can say created and that basically led to a humungous amount of data that was created. So people started thinking, you know, “How do I start collecting this data so that I can get some a lot of value into it? How do I start now generating more data in a very automated way?” And all of that has led to something called Industry 4.0.

Industry 4.0

This is where the concept of Artificial Intelligence, this Data Science, the Internet of Things and all come into the picture. This is where you know this data that’s been generated, “how do I make sense out of it?” For example, if I have data of somebody’s buying patterns, how do I make sure next time he or she logs in that I can do a recommendation to say, “Hey, buy this product, or if they have a particular service, let’s say in your broadband, so the next time you call, how do I make sure based on the usage pattern, where I can say, Hey, this person needs a better service or a lower service.” So all of that is basically the concept of using AI or data science and then generating automated data using the Internet of Things is what is at a high level is called the Industrial Revolution.

So I have a quick example.

This picture basically shows Vatican City in 2005 and this is basically people are waiting for the Pope. So basically, they’re waiting for a new pope to be nominated and the new pope will come out on the balcony, but that’s not the important part. The important part is you can see was in 2005, people have some digital cameras and they’re waiting for him.

The same thing got repeated in 2013. But now everybody has a smart device, whether it’s a phone or iPod or whatever. The concept is that the moment you get a picture, what do you do? You start sharing whether it is YouTube, or Whatsapp or Facebook, etc. Every time you do that, you generate data. Every time you do a digital transaction, you buy something, you sell something, you take a ride, you search something, you generate data and that is how data is generated and these are all human-generated data.

Similarly, there’s a huge amount of machine-generated data. A lot of you must have smartwatches, every time you walk, it says how many steps you walk and then it loads in some server or some cloud, that is machine-generated data. Every time it tells you your heartbeat is that, your pulse is that, and that is completely machine-generated data. You are not putting the data into the watch or whatever device you have like Fitbit, watch, whatever you want to call it, that is machine-generated data. That’s all part of the IoT, and that’s how their data is generated, the moment data is generated using techniques to actually understand and get patterns or any useful information from the data, and this is what data science is all about. Data is the generation, but science is applying that and hence the word Data science.

Now, I just want to talk about the Industrial Revolution 4.0 a little bit so that you understand what it is and why it is.

Now, I wanted to share a video and this is how much of the data is generated in a minute and it will give you the format of data generated in one minute on the internet, one minute on the internet. After watching this video, you will feel like, “it is tremendous.” So it is a tremendous amount of data in one minute on the internet. Now think of 24*7 internet because when we are sleeping, somebody else is generating the data and when they are sleeping, we are generating the data.

People say that Industrial Revolution started in 2007 and that led to the entire AI and everything, and we will see now.

Let’s see what happened (Globally) in 2007.

• At the beginning of 2007, on January 30th in the Moscone Center in San Francisco, a guy with the turtleneck and the jeans, and I’m sure everybody recognized that guy called Steve Jobs introduced to the world, something called iPhone. This was the first time the world saw a phone completely different. Until that point, they were so-called smartphones, then came the iPhone and the computing power of the iPhone was more than the computing power of Apollo 13 that went to the Moon, but that was not the main thing about iPhone. Yes, the computing power was higher but it created a revolution. How? It created this concept called apps. It created this concept where every information can be accessed, you have that information at your fingertips and you don’t have to go to 200 sites to get it. One small app can basically get some information to you, so that is what happened.
• Then in 2007, Facebook was launched, but in 2006 actually, it was started in a dorm in Harvard, and then in 2007 it came up to the world.
• In 2007, actually, a company called Twitter was started.
• In 2007, one of the most important software, what today we call it for AI and Big Data called Hadoop, which is now used by 80% of the companies in order for their big data strategy.
• The second-biggest software, I’m sure most of you must have heard it or some of you may not have heard it, what makes cloud possible started in 2007 called VMware. VMware with what makes virtualization today available in the cloud, and that virtualization is what makes cloud the most attractive in terms of the technologies that have come out.
• In 2007, one of the other companies, which you must have heard of called GitHub and it started in 2007 and it is the largest repository of software in the world.
• In 2007, basically, Google bought a small video company, which today we call it as YouTube.
• In 2007, three design students went to a conference in San Francisco and they had carried three air pillows because they saw that all the hotels were filled, so they actually leased out the three air pillows and they got an idea, Wow, if I can do that, why can’t I use some people homes to do it? And so that’s how Airbnb was started.
• In 2007, Google started an alternative operating system to Apple, and they called it Android and today 85% of the world’s phones run in this operating system called Android.
• In 2007, Amazon actually released its first e-reader and as we know it today, it’s called Kindle.
• In 2007, IBM actually went live in terms of, you know, their processing power.
• In 2007, if you look at the curve of solar energy, just look at 2007 alone, the capacity just went up because of the technology.
• In 2007, the cost of sequencing the human genome because of all these improvements, as you can see, it just dropped. It started with a billion-dollar or a million dollars and $100 million dollars in some cases and today, less than, you know, $100-$150, you can get your entire genome of your body sequenced.
• 2007 was the first time Intel used non-silicon-based material for their chips, for their ICs because that’s how only they could keep up Moore’s law.

What is Moore’s law?

Moore’s law says that in every 18 months or 24 months, the capacity doubles.

• In 2007, and if you’re familiar with the natural gas production in the US, right? Because fracking was something that was invented in 2007.
• In 2007, Michael Dell, who had resigned from his job as the chief executive of Dell two years back, took the company private and came back as the chief executive.
• 2008 was the first time we have public data for cloud computing, which means to say obviously, you’re going to have 2007 data in 2008. So that’s when the public cloud started.
• In 2007, Google actually released something called Street View. In India, it is not there, but if you are familiar with that, just go to Street View and basically, it’s not just your Google Maps, but it’ll tell you how the street looks like.
• 2007 is when the first time Netflix switched from its DVDs into streaming. So they said DVDs don’t work anymore. They said they went into streaming because they saw that the cloud, everything had come up so much and said this is when I have to switch into streaming.
• 2007 was the first time a cyberattack happened on a sovereign country, and Russia was responsible for it for Estonia. Obviously, today’s situation is, you know, everybody knows what’s happening, but 2007 was the first time Russia actually had recorded an official cyber attack. The country’s first cyberattack from country to country in Estonia.

Let’s see what happened in India in 2007.

• Jio (India’s largest 4G network) started in 2007.
• In 2007, two kids said, you know what, if amazon can do it globally, we can do it in India and today we know it as Flipkart.
• In 2007, probably one of the biggest fashion sites in India called Myntra started.
• InMobi, which is, you know, basically the mobile ads company started in 2007.

So 2007 is basically like the rocket sort of, you know, for the fourth generation. So many technologies came together, and that’s what makes Industry 4.0. It’s not one technology, it’s not just artificial intelligence, but this entire story that makes it. So 2007 is the birth of your industry 4.0.

AI

Now, what is always all about this artificial intelligence? What does that mean to be artificial intelligence and what are the jobs available? But before that, I wanted to say one of the big applications of artificial intelligence is called NLP natural language processing. So basically this video talks about how in real-time you can do a conversion from language A to language B, listen to it. So basically, as you can see, in real-time when people are speaking from English to Chinese or Japanese I think. So this is the power of AI.

Now let’s get into what is data science, and there are two fields that you need to be aware of, they are Data science and Computational Data science and let’s talk a little bit about it.

Data Science

If you are interested in basically, you know, to get into pure data science, what does that mean? If you’re learning in and interested in, you know, basically getting information from the data, understanding that then data science basically has three components.

• One is called programming.
• The second is called visualization and it is basically, “how do I display the data using the software to do that?”
• Third, one is the actual algorithms and the model, which is called machine learning statistics and deep learning.

So these are the key components that are needed for you to learn data science.

Computational Data Science

However, if you’re interested so much about, you know, for example, cloud technologies, “how do I actually get the data? How do I actually modify data? How do I actually set up the data?” Then it is called computational data science, which is basically a bit of computer science and engineering and then you also have to learn machine learning, science and programming.

But the main difference between Data Science and Computational Data science is basically, in one case, a lot more model development and then a sort of presenting the data and the other one is more of understanding the data and gathering the data. So these are sort of the two you will hear going forward in data science and in computational data science.

What do you require for anybody to be a good data scientist?

For that, basically, there are two things.

One, the domain knowledge is very important and this is something you know, you may get that knowledge, you may not get the knowledge, but the domain is something important because where do you apply data science or where do you apply your artificial intelligence. You’re applying in the health care field, you’re applying in finance fields, it could be in any field, right? So that way, which domain is also important.

The second thing is, you need to understand how do I apply it for business? There is a lot more importance there because the problems that you are going to choose are going to be very business-specific and so most problems in business, they speak the language of business. So you need to convert a business problem into a data science problem and that is something also important, and that is something very, very important for your kids.

What do you need to succeed in Data Science?

I come up with this called TALENT and it is divided into,

1. TA — Technical ability.

What are the things that you need to learn for data science?

Basically, your basic math is very important. Then your basic statistics, basic analytical tools, which are your statistics, so you should be good statistics.

Second, there is a certain amount of machine learning that you need to learn like algorithms and things like that.

Programming is very, very important, if you want to be a good data scientist, especially on the technical side, you need to learn programming. There are two programming languages. One is called Python and the other is called R, you need to sort of be good in both.

Then you need to learn something called data visualization and next is called model development. One of the things let me tell you, and this is very important, not everybody in data science is somebody who needs to be an expert in programming. There are a lot of jobs in data science that don’t require any programming thing or things like that. They require what is called a very important thing is basically understanding the topic so that you can take a business problem. So somebody says, “Hey, my sales are down,” take the problem and convert it into a data science problem. So there are a lot of non-programming jobs also, but you still have to go through the basics of programming to understand that and so don’t think that I have to be an expert programmer to be a good data scientist.

2. LE — Language expertise

What do you mean by that?

I mean the soft skills. Soft skills are very, very important for you to succeed, which means to say you need to learn “how to present your data? How do I do presentation skills? How do I work in a team? How do I have leadership skills too?” So all those soft skills are also very important for you to come up in your career.

3. NT — Networking.

What do you mean by networking?

Networking, which doesn’t mean it’s a computer network, but networking means in order for you to get your job, how do you create your profile? How do you make sure that your profile stands out in everybody? And there also you need to have a digital persona for yourself, you need to make sure that you’re active on Linkedin. You start developing slowly, especially if you’re a young student or you just started your career, start following people whom you think are good in their field and that way slowly you build up the networking and that helps you in your career.

So basically, for you to succeed in data science you need TALENT.

What types of jobs are available out there in Data Science?

One of the things that if you look at data science is, the good news for data science is can be used anywhere, any domain. It can be in finance, it can be in healthcare. In every industry, there’s a need for data scientists, there’s a need for people both, you know, business and data science knowledge, with pure data science knowledge, with pure engineering knowledge.

This is from LinkedIn and they say these are the highest paid jobs in 2021. AR/VR engineer, ML engineer, Big data engineer and these are all connected with data science. This tells you that at every level, there is a huge demand and it’s a huge opportunity for data science.

Data Science is being used everywhere. How?

I would like to share a video and please listen to it.

So many told that cinema is very mythical, AI can never come into that but see, it’s coming everywhere. So what I’m trying to get here AI is everywhere, jobs are everywhere. There are lots of jobs, but the question is, are you prepared in the right way? Do you have the right training? Do you have the right techniques to do that, and that is where choosing the right organization (training academies) makes a huge difference.