Are you ready to embrace Machine Learning?

Yesterday, I shared Gartner 2016 Hype cycle and got lot of people interested in it.

Looking at the hype cycle I see Machine Learning in peak of inflated expectation. I wonder how long it will take to be at either slope of Enlightenment or Plateau of Productivity?

In my early days whenever I heard word ‘artificial intelligence,’ I can’t help but imagine a future where robots regularly interact and get mistaken for humans.

My quest with AI started in 90’s with me trying with the development of Back-propagation Neural Network algorithm for Speech Recognition. Since then, to my current Machine Learning algorithm exploration and with Masters specializing in Machine learning/AI, I feel Machine Learning is ready to take over the world. Look at the estimated global installed self-driving cars, this projection from BI Intelligence below suggests we will have 10 million self-driving cars on the road by 2020.

Since its inception in 1956, artificial intelligence (AI) research has had two important but distinct research goals: first, to learn more about how the human brain works by developing architecture that closely mimics human brain processes (or at least results in the same outputs as the human brain). Second, to develop dynamic computer algorithms that are able to change their reactions and outputs in response to different input situations — algorithms that are able to learn.

We may be still need time to mimic mysterious human brains, but when it comes to dynamic computer algorithm, I feel that Machine Learning might be ready.

Now that I am into Machine Learning Algorithms and using it to solve various business problems. I feel today, the explosion of data is creating an adjacent world, a lack of systems that help you use that data to drive business value, whether it’s customer experience, revenue, or cost savings. This is the gap that machine learning is filling today. Machine learning brings together computer science and statistics to harness that predictive power. It’s a must-have skill for all aspiring data analysts and data scientists, or anyone else who wants to wrestle all that raw data into refined trends and predictions.

Some of the visualization tool is aiding to show how several algorithms function and how their parameters affect and modify the results in problems of classification, regression, clustering, dimensionality reduction, dynamical systems and reward maximization etc.

I also feel Machine Learning and AI can also very well be the future of User Experience (UX) by clearly predicting what user wants and adopting to it.

I still feel robots will not take over the world yet (just a hunch) but I feel Machine learning is poised for mass adoption and ready to Take Over. Machine learning will continue to play an increasing role by offering ways to alleviate the burden of data analysis and by providing the foundation for more personalized and predictive interactions and solutions at every stage of the user's lifecycle.

Wherever it is that we are, however, it is likely Machine Learning will be a driving force. Company's move towards machine learning with prescriptive/predictive analytics is inevitable. If you are not already moving in this direction right now, you can be sure your major competitors are. In today’s highly competitive economy, even the slightest advantage can be the difference between winning and losing. Machine Learning doesn’t just give a slight advantage – it helps you understand your own data and make better decisions to improve your business. After all, isn’t that what analytics are about?

Now are you ready to embrace Machine Learning?