Along with the emergence of new devices, we have data which is ever growing. And all this data is being collected for what?
As someone correctly said – to analyze, see patterns and predict!
And for all this data, you might think Artificial Intelligence (AI) to solve all data problems. And that is a good answer too.
But, how do you start off on the path to AI?
It is clearly a path, and not just 1-2 items which can help you get where you want.
Many organizations sit on their data while they chart out the perfect AI plan to utilize the data, but why not start by unearthing the one impactful solution which lies within your data and start with it now?
AI is a vast expanse which when viewed from up above has clear distinctions within, in the form of Machine Learning and Deep Learning. This brings us to what is the differentiation between the three.
What is Machine Learning (ML)?
In the simplest of terms, it is when algorithms are used to parse data, learn and then predict about possibilities in the future. But for ML, you need the data which the system is presented with, to be adhering to a format similar to the historic data. If this condition is not met, the algorithm might throw errors.
What is Deep Learning?
Deep learning started off as Artificial Neural Networks, which stemmed off the biological studies of the human brain. The artificial neural networks used machine learning algorithms to solve data problems. The human brain has millions of neurons which are interconnected with each other in a certain region but the artificial neural networks which were developed by early data researches had distinct layers and a set direction for data propagation. When these artificial neural networks are multiplied to huge numbers and layers, then this is what is defined as Deep Learning.
What is Artificial Intelligence (AI)?
AI is intelligence like a human being but in a computer application. Using Deep Learning, there are many fields such as preventive medicine, user interest based recommendations, driverless cars and much more are seen possible today. The power to think and assess the situation beyond rules is what makes these applications intelligent.
Elon Musk did say that there will be very little that a robot will not be able to achieve in the coming years. But AI is being used predominantly to help achieve specific tasks at a faster pace and more intelligently. But, AI to simulate the intricacies of the human brain is much more complex, and though many scientists are moving in that direction, it does not seem to be a near reality. This is called general AI and that is not something which is easy to emulate, because of the nature of the human brain. Machines might find it really difficult to buy groceries or cross a road, but they will work wonderful when they have to play a world championship of chess or poker.
One of the most important factors which need to be factored in for general AI is common sense.
So now, that we know what AI is all about, do we really need AI or is it something completely different we are looking at?
Predictive Analytics in particular within the machine learning models can be the small step which can bring about instant business impact. Forrester forecasts a 15 percent compound annual growth rate (CAGR) for the Predictive Analytics and Machine Learning (PAML) market through 2021.
Many organizations do not have the inherent capability to build predictive analytical business solutions, but that should not be the reason to not move down the data science path. They have formed partnerships with firms which are equipped with the knowledge and the skills to help them in their business needs. And some firms choose a path which involves solutioning and training, where their staff shadow learn and understand nuances of working with the system to build solutions, as they watch the experts work solutions for their firm.
So, the choice of partnership varies as per business and requirements, but the need to analyze data and using the intelligence to business benefit is the consistent outcome for every data related discussion.
Or at least all the data related discussions which I have had!