Enterprises today are finding it exceedingly meaningful and resourceful in the massive amounts of data they generate and save every day. The required algorithms, applications and frameworks to bring greater predictive accuracy and value to enterprises’ data sets are available; therefore, businesses need to make sure they have data sets of sufficient size and quality. It is due to the excessive need to do a better job in capturing and utilizing data.
The rise of deep learning and neural networks has spread in everyday lives. It took about six years for neural nets to show impressive results, first in speech recognition, then computer vision, images, image detection and diagnostics, and more recently, in natural language processing. After all, Siri and Google have taken voice recognition mainstream.
Smart devices use Internet technologies like Wi-Fi to communicate with each other, your laptop and sometimes, directly with the cloud. Some smart devices also talk to a central hub that serves as a control point for many different devices, like the Revolv.
In financial markets, a huge amount of trades is done by algorithms and learning machines. Above all-in-all probabilities, your pension and retirement savings are changing hands and are currently in the hands of micro-trading machines conducting millions of trades per day.
All of this is having a profound impact on us as we struggle to keep up with the changes they bring.
Data is immensely valuable, high performing, challenging and huge. A wide variety and huge volumes of data are readily available to be explored, such as computational processing, which is cheaper and more powerful, and with affordable data storage.
Data emerges from the infrastructure of cities, as well – from sensor-equipped buildings, trains, buses, planes, bridges and factories. All these imply that it is automatically possible to produce valuable data models that can analyze bigger, more complex data and deliver faster, more accurate results – even on a very large scale. The end result is high-value predictions that can empower valid decisions and smart actions in real time without human intervention.
One of the most important factors in machine learning is producing smart actions in real time, and that’s done through automated model building. Analytics thought leader Thomas H. Davenport wrote in The Wall Street Journal that with rapidly changing, growing volumes of data, "... you need fast-moving modelling streams to keep up." And you can do that with machine learning. He says, "Humans can typically create one or two good models a week; machine learning can create thousands of models a week."
Machine learning is not just an IT industry phenomenon. PayPal, Netflix, Skype and Uber were also once dismissed as small players, and in recent years, the number of technologies in the small players phase has risen sharply: 3D printers, digital cash, self-driving cars, smartwatches, Internet TV, 3D goggles, robots, smart clothing, massively open online courses (MOOCs), drones, expert systems, do-it-yourself medical tests, the quantified self, artificial intelligence and more.
There are many machine learning applications and opportunities – to mention a few:
These above examples all give you recommendations which are based solely upon the information provided by you. Machine learning technology even tracks how long you watch a movie and the various kinds of products you purchase – many of these sorts of actions are being monitored and recorded. Machine learning is quite ubiquitous and useful with some information that you may never have thought about it in the first place.
Machine learning algorithms are very good at arriving at predicting outcomes and optimizations because calculations are based in milliseconds. Whenever a miscalculation happens, the error is rectified, and the machine learning algorithms correct the error and begins another iteration of the data analysis.
All the necessary algorithms, applications and frameworks bring greater predictive accuracy and value to enterprises’ data, leading to machine learning’s ability to scale across IoT, big data and cloud computing.
The adoption of machine learning is made possible through simulation, as well. Right from inception, growing and optimizing a supply chain network with the help of generating terabytes of data – mainly through advanced simulation techniques – it is possible to forecast accurately by deploying several optimization techniques.
It is important to remember that machine learning algorithms are iterative in nature and always looking forward to positive, optimized outcome. The future potential is virtually endless.