Before digging into the wonders of machine learning let us see what is machine learning- Machine learning is a core sub-area of artificial intelligence; it enables computers to get into a mode of self-learning without being explicitly programmed. When exposed to new data, these computer programs are enabled to learn, grow, change, and develop by themselves. It allows computers to find insightful information without being programmed where to look for a particular piece of information; instead, it does this by using algorithms that iteratively learn from data. This means that it is capable of analysing far more than any human would be able to, and make connections that may be beyond our grasp. Interesting, isn't it? Well then let’s delve into it a little more.


The famous BUTTERFLY EFFECT says that there are certain things which even the most advanced science can’t predict. But that list of things got shorter as scientists are working on using machine learning to predict chaos.






The traditional approach towards making predictions about systems like stock market or weather systems have been that scientists measure data as much as they can with maximum accuracy and then create a computer model to see what the model does next. But after extensive research and a series of paper published, chaos theorist Edward Ott along with his team discovered a different approach. They used a machine learning algorithm called reservoir computing, to repeatedly measure, predict, test and tune those prediction about a chaotic system until they reached the maximum level of accuracy.


The algorithm was tasked with predicting how a wall of flame would behave if it moved through a combustible medium like a sheet of paper. The technical term for it is the KURAMOTO-SIVASHINSKY EQUATION, which is used to study things like plasma waves or air turbulence. The solution to this evolves just like a flame front where scientists fed data from the evolution into their algorithm. With every input of data, artificial neurons in the machine-learning network send out signals. The scientists then measure the signal strengths of several neurons or reservoir which were chosen at random. These were then weighed and combined in different ways to produce a set of outputs. The algorithm then compared these sets of outputs (predicted data) with the next inputs (actual data). Tiny adjustments were made to improve the accuracy of the next measurement.  Every measurement and subsequent adjustments gets the algorithm closer to nailing it on the next try. Finally you use all of that to make a real prediction about how the system will behave.
This research is no small, especially because there are ample number of things hard to model. There is practically no equation to describe a whole lot of chaotic systems and it is even difficult to make grand, complex models of those systems. And thus comes the role of machine-learning to just measure behaviour in chunks and then fine tune the predictions.
It will be fair to say that this opens up a brand new world of highly efficient possibilities. Be it forecasts, earthquake warnings, or monitoring heart rhythm for impending heart attacks and even neuron firing patterns for impending seizures- machine learning has predictive insights to all of this. 
The system process through a large amount of data to identify pattern and trends which are not visible using traditional tools. Besides providing insights, it aids in predictive monitoring – using algorithms one can forecast the equipment breakdown time and schedule a maintenance before that happens. With these predictions, the problem of unplanned downtime and machinery failure can be countered. We might be able to combine this new approach with the existing modelling techniques to get even better results.




Ott had said, “If we have ignorance we should use the machine learning to fill in the gaps where the ignorance resides.”

Predicting chaos is a surprisingly accurate science and we can surely be prepared for many more wonders to happen through machine learning. It is at the core of our journey towards artificial intelligence. 
It is hard to not end this article without quoting a line from a very famous song, “It's something unpredictable, but in the end it's right...".