Keys to a More Advanced Future: Machine Learning and Deep Learning

Ask anyone who knows a bit about technology, and you’ll more than likely hear them say a few things about machine learning and/or deep learning. These two concepts are catapulting artificial intelligence into strata never seen before, and sometimes it seems as though they are the same thing – but look closely and it becomes clear that they are two different beasts, and both must be tamed before artificial intelligence achieves its next breakthrough.

Machine Learning at a Glance

At its simplest, it is possible to describe machine learning as a process by which machines use algorithms to take in data, learn from that data, and use what they’ve learned to make more nuanced, informed decisions. This involves a great deal of data and usually brings machine and social networking into the picture. For example, music streaming services are capable of looking at what you listen to, finding other users who listen to the same music, and suggesting more music for you based upon the preferences of users similar to yourself.

This process is, at its core, still a process that is programmed. The machines are simply performing a programmed task, learning while doing that task, and improving their performance based on what they learn. These processes may become more nuanced when they advance to deep learning.

The Details of Deep Learning

To boil down the difference between machine learning and deep learning, it may be easiest to say that machine learning still relies on guidance from engineers and programmers. Machines follow routines and functions set forth by engineers and learn from these preset parameters. Deep learning goes, well, deeper than that.

A deep learning model does the same essential thing that a machine learning model does, but the difference is that a deep learning model is capable of making decisions for itself eventually. Given the proper data and ample processing power to analyze that data, a deep learning model could make better and more accurate predictions, thus teaching itself to parse data and make decisions without human guidance. It sounds a little crazy, but that’s because our own brains are crazy powerful, and deep learning models are based upon our brains.

The Importance of ANN

The network that is set up to power deep learning models is called an artificial neural network, or ANN. These artificial neural networks are made to mimic our own neural networks as best they can, and this means that artificial intelligence has the capability of making refined conclusions and to think more like we do. Even humans err, however, so ensuring that a deep learning algorithm doesn’t make incorrect conclusions is the next step in the evolution of artificial intelligence.

Once we figure out how to minimize the chances that a machine draws incorrect conclusions from its data, we can reach the next level of artificial intelligence. Machine learning is the foundation, and deep learning builds upon it. As people and machines take in more data, we only become smarter and more capable.

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Joshua Crawford | Managing Director | PROPRIUS