What is machine learning?
The simplest machine learning definition? The practice of teaching a computer how to spot patterns and make connections by showing it a massive volume of data. So rather than programming software to accomplish a specific task, the machine uses Big Data and sophisticated algorithms to learn how to perform the task itself. Machine learning allows applications to “think” and independently make a determination or prediction – going beyond what predictive analytics and Big Data analytics can do, and often beyond what humans can do. A popular consumer example of machine learning is a recommendation engine in an online retail environment.
What is deep learning?
Deep learning, sometimes known as cognitive computing, is a form of advanced machine learning. It uses multi-layered (aka deep) neural networks to simulate human thought processes. These networks are made up of small computing nodes that mimic the synapses of the human brain. Using input data sets and sophisticated algorithms, machines can help solve complex, non-linear problems. Deep learning is responsible for breakthroughs such as speech and image recognition and natural language processing. Some popular deep learning examples include:
- Facial recognition software
- Self-driving cars
- Smart home automation devices
What is supervised learning?
There are three main ways that machines can “learn”:
- Supervised learning – in this approach humans label the inputs and outputs and then the model figures out the rules for connecting the two.
- Semi-supervised (or reinforcement) learning – the machine is
rewarded or penalized for actions it takes through trial and error, and the algorithm adjusts accordingly. - Unsupervised learning – algorithms are left to discover patterns in the data (which is sometimes clustered) on their own.
Regardless of the type of training used, the machine is able to learn from the data on its own, absorbing new behaviors and functions over time. The result is a model which can be used to predict outcomes based on data, and which is regularly retrained for accuracy.
Welcome to the future of business
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Benefits of machine learning
Faster decision making
Machine learning can automate and prioritize routine decision making processes – so you can achieve best outcomes sooner. For example, when coupled with the Internet of Things, it can help you decide what to fix first in your manufacturing plant.
Machine learning use cases
Smart business processes
Machine learning shifts traditional rules-based processes to intelligent ones that can discover new patterns in large, unstructured data sets and make strategic predictions all on their own. It can also take on highly repetitive tasks such as checking invoices and travel expenses for accuracy.
Digital assistants and bots
Advances in AI technology suggest that self-learning algorithms may soon come to their own conclusions within certain parameters and develop context-sensitive behavior. Devices will be able to schedule meetings, translate documents, and take on other routine business tasks.
This story originally appeared on SAP.com.