AI has always depended on rules and systems that enabled predictive outcomes. These rules were predefined and were usually manipulated by a thought leader on the topic. The key was that the predefined rules were based on humans, rather than raw data. Data has increased in volume and needs have evolved, thus replacing that approach and introducing “machine learning” which has an infinite scale of possibilities.
Deep learning is based on the thought structure of a person’s brain. This is why much of the jargon pertaining to ML and AI have neurologic roots. Machine learning and perception are parallel. As neurons send signals through a human’s nervous system, we perceive it. This perception model is how ML works. Machine learning attempts to perceive data, and draws patterns from that data. Machine learning requires heavy repetition to ensure accuracy. A network of these perceptions is what makes it all come together, and is called a neural network.
Deep learning and machine learning can be leveraged in nearly every industry and category. Here are some examples:
Transportation: This is slowly becoming a reality for us. Self-driving cars are risky and have many unintended consequences, but the art of this is being perfected. By continuous testing in controlled environments, this application becomes a more viable use case every day and is one of the most exciting areas.
Commerce: Personalizing user experience is a key element of every company’s digital strategy. Providing recommendations and suggestions based on data and interest is already being applied but are in need of continuous improvements. This benefits sellers and consumers alike and is one of the more obvious applications.
Health & Wellness: There is no shortage of data that the healthcare industry has as its disposal. This use case has been a great fit for leveraging DL. Examples include imaging protocol, detection of diseases and disease prevention, and drug development. This will be beneficial to humankind as innovation can be accelerated to offer better options to patients.
These are only a few examples of use cases for machine learning and we believe the technology will touch just about every industry and make an enormous impact on our daily lives in the next few years.
