I started to delve into the field of Machine Learning few months back and after making a few projects, I thought to myself, "this isn't really tough". That was until I encountered Deep Learning. A whole new field of study, Deep Learning requires a vast amount of mathematical as well as analytical knowledge. As I was preparing to get hands on with Neural Nets, I realized that it is so overwhelming. There are so many complex concepts that cannot be just "learnt and implemented".

Digital data and cloud storage follow Moore's law: the world's data doubles every two years, while the cost of storing that data declines at roughly the same rate. This abundance of data enables more features, and better machine learning models to be created. "In the world of intelligent applications, data will be king, and the services that can generate the highest-quality data will have an unfair advantage from their data flywheel -- more data leading to better models, leading to a better user experience, leading to more users, leading to more data," Somasegar says. For instance, Tesla has collected 780 million miles of driving data, and they're adding another million every 10 hours. This data is feed into Autopilot, their assisted driving program that uses ultrasonic sensors, radar, and cameras to steer, change lanes, and avoid collisions with little human interaction.

Innovation continues, life goes on. Old jobs are made obsolete, new jobs are created. When the greatest minds of the world decide to create something new, their engineers set out to find a way to do it. Concepts are created, wireframes are built, prototypes are designed, algorithms are written. In essence: these ideas start to take shape.

Machine Learning is being used to solve many problems, which problems can you use it for? In the last 5 years there has been growing success using machine learning. Rapidly increasing processor speed and access to large scale data sets are allowing for many new problems to utilize machine learning successfully. Today machine learning is being applied by innovative companies in almost every field. Using machine learning to solve problems is becoming central to many companies core points of differentiation.

You Might Have Heard: The Microsoft AI experiment with Tay, their machine learning Twitter bot, ended after a mere 24-hours. The company pulled the plug when she almost immediately turned into a sexist, racist Nazi. Tay was suppose to learn how to communicate like a human by engaging in conversations with Twitter users. "This gets to the underlying problem," Vice argues. "Microsoft's AI developers sent Tay to the internet to learn how to be human, but the internet is a terrible place to figure that out."

Artificial Intelligence represents the next chapter of the Information Age, and Google, Microsoft, Amazon, IBM, and others are engaging in an arms race to control the platform that dictate tech's future writes the New York Times. "The relationship between big companies and deep machine intelligence is just starting." So, what counts as artificially intelligent anyway? The Verge explains the difference between machine learning, deep learning, and neural networks, how they work, and why the future of AI is likely to be more subtle than you think. The next wave in technology isn't about the technology, but rather the market that emerges from the technology.