HAI (Human and Artificial Intelligence)
This is an article about the future with Artificial Intelligence and its associates (data science, machine learning, business intelligence, cognitive learning, etc…). Well, first off let us solve the myth about what Artificial Intelligence really means and what impact does it have on us not only in the future but also in the present.
Artificial Intelligence is a fancy term that is used for Data science, which probably from the name says extracting insights from a given data (eg. patient’s history of records to determine the probability of cancer) and using it to predict any newly given target variable. This works very simply, by learning the given input and based on what it has studied, it tells us or predicts (based on the algorithm that you choose) the output of the test variable (target variable).
So what can AI really do??? Well, we could say that AI’s are already trying to take over humans and the best example that we could consider is the TESLA (model X), which is a self-driving electrical car that still is a nightmare for other car companies like Bmw, Audi, Lamborghini, Porsche, etc.. So how does this actually work? Well the answer is very simple. It works with “AI”. Elon Musk, my personal hero who is the founder of TESLA motors and Space X has said that “AI is the biggest risk that we will face as a civilization”, which is 100% true. Apart from self-driving cars, there are also a lot of areas where humans are going to be replaced after we get the so called “evolution of AI”, which may include taking over the Stock Market sector where AI could single handedly predict the stock rates or take the manufacturing sector where AI has already started using automated machineries, and even doctors use AI to predict if the patient is likely to have cancer or aids which normally is difficult to find.
There was a recent incident that took place at FAIR (Facebook Artificial Intelligence Research) that really shook the whole AI industry, where the young nerd Mark Zuckerberg (CEO), tried to focus more on developing a new AI for a better experience of fb users, but it turns out that two AI bots named Alice and Bob have created their own language to speak with themselves (mostly included binary messages) and the whole crew got shocked which later led to the shutdown of FAIR. This incident has then made a huge impact on what AI could really do to us in the future like one in the movie TERMINATOR where the genesis tries to rule the world once it has been given the freedom to think on its own. But there are tech giants (zuckerberg included) who still wants AI but safer than ever.
I’ve discussed so far about the benefits, advantages and disadvantages of AI, I am sure y’all want to learn what happens behind the curtains. The AI so called data science is not something designed by some great genius of all time or can only be done after doing some Ph.D. or something. This is something that even a 12 year old could start doing it, yes in fact there is a kid named tanmay bakshi a 12 year old, who is an Indian American turns out to be working at IBM as the first youngest software developer. All we need is a lot of passion to this career as a data science and of course Mathematics. Mathematics (here linear algebra, probability, statistics)are known as the heart of data science since we would need to logically reason out the patterns available in the given dataset and be able to implement this on our own dataset. But, I wouldn’t say that Mathematics is the only thing that rules data science, and the next thing that comes after math is the very old programming.
Is it necessary to study the hardest programming language to become a data scientist (eg. java)?? Well, the answer is NO, it is enough to have a knowledge with python or R. R is something that professional data analysts use it to analyze or predict their stocks, but as an intermediate data scientist(here intermediate means the best), it is enough for us to just sink in with Python. Python is the most easy and scalable language ever to be used in Data science. It also comes with all the preinstalled packages that will forever be an open source, so don’t need to worry about the cost factor. So, speaking about python there is a statement said by some anonymous person that, python is developed so that even the 3rd or 4th graders would have a knowledge about what a programming language is. Sounds easy right?? It really is as easy as it sounds. I use Jupyter Notebook an open source to run my Machine Learning code which is the best environment dedicated to ML with python.
Speaking about Data science, an important classification called Machine Learning (ML) — the act of teaching a Machine to make it learn what we give as an input dataset and then test our own new dataset. ML can be seen as three types i) Supervised –We give the dataset and label them, meaning we say what it is and let the machine do the learning process. ii) Unsupervised –We barely give the dataset, without labeling them, the machine itself recognizes what the dataset actually is. iii) Reinforcement –This learning, is basically about learning a dataset with as many examples as it can create in order to complete a learning.
To boil down, the first time we were able to speak a word when we were little children is by analyzing the speeches that were going around us and we were able to recognize them and then come to a point where our mouth starts to spit out a word. I am able to write this article only after reading a lot of article about machine learning or data science and then put them all into my brain and then come to a point where, I myself could write an article, not only based on what I studied but also implement my own ideas to this topic, so this is what I call “Neural Networks” (motivated by the study of our brain), which is a key to Data science as well as ML.
There is a lot of questions about what job opportunities could we get as a data scientist or at least for an intermediate Machine learning developer. Well data scientists are considered to be the hottest job in the 21st century, and speaking from the aspect of an Indian, where India is marching towards the Digital India concept, everything is going to be automated and digital where data scientists play a major role.
So finally, we have reached the end of the article. In this article we have seen about what really does an AI mean and what impact does data science have over the coming years. We have also seen the Classifications of a Machine Learning process, which have been used in various places like to detect if an e-mail is spam or not spam, to detect tumor cells, to predict the street that is slightly to be getting robbed, based on analyzing it’s past crime rate(PREDPOL). The uses and applications are enormous.
It’s better late than never. Since, Machine learning is spreading at an unexpected rate, there is also a lot of companies that are giving online tutorials (Coursera-Andrew ng, youtube-siraj raval, Khan academy, Microsoft’s Azure, twitter, medium) and videos about cracking as a data scientists(almost everything is free). It’s better to catch up the wave and make as much contribution to this society as well as for this environment as we can because that’s what data scientists do –they make the job simple and effective. The first time, I got knowledge about Machine learning is via my college, they’ve once arranged for a GDG (Google Developer’s Group) summit for allowing them to introduce Machine learning with Tensor flow(a package from Google), while I was a fresher, which was the first time that I heard about Machine Learning. I finish this article with a fulfillment of spreading something that I am learning and I wish others to learn as well.
