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Artificial Intelligence vs. Machine Learning

We have more computing power and data sets than ever before that fuel artificial intelligence (AI) and its potential to transform the digital landscape. This technology is becoming ever more human, yet few understand how it actually works. Artificial intelligence is an umbrella term for machines mimicking cognitive human functions such as learning and problem solving. These tasks are performed using a number of methods, but the most breakthroughs are occurring through machine learning and deep learning.

Machine Learning

Machine learning is a subset of artificial intelligence that involves computers learning from large datasets to help them learn to perform a task and improve over time. For example, if you want to teach a computer to identify whether or not a person is smiling, you feed the computer pictures of people smiling and it learns from the those pictures how to identify a person that is smiling.

Deep Learning

So how does a computer learn like a human does? There are many different ways to apply human attributes to a computer, but one of the most successful is deep learning. Deep learning mimics the brain using algorithms. Much like a brain, the computer uses “neurons” called artificial neural networks to learn.

Neural networks use hyperparameters that distinguish objects and actions. A computer analyzes everything using numbers, so when it is fed an image, it sees that image as a set of numbers. The hyperparameters in a “neuron” are a range of numbers. If a neuron sees that an object’s numbers fall within its range, then that neuron “fires.” That means that if a neuron is trying to distinguish if a person is smiling, and the set of numbers it reads on an image falls within its hyperparameters, then the neuron would fire ‘yes,’ and predict that the person is smiling.

A computer’s neurons are arranged in layers, and each layer is set to identify an aspect of an image, phrase, etc. The deeper the layer, the more complex the learning. One layer of a neural network that is set to identify smiling might identify shapes, while another identifies faces, and the final layer identifies whether or not a person is smiling. One neuron on the shapes layer might identify rectangles, while another identifies triangles and so forth. The two neurons on the final layer predict yes or no.    

When you first train a computer to learn, you program the computer to tell it whether it predicted right or wrong. When the computer predicts incorrectly, it redefines the hyperparameters on its neurons to predict more accurately in the future. This is a computer learning how to perform a task.

Artificial Intelligence in Business

In the business sphere, AI is poised to transform the world. Nowadays, with all the tools and software we have available, you can use something like IBM Watson instead of paying millions of dollars to create your own AI technology.

AI is used in a diverse range of businesses. A popular one in retail is recommendations. Businesses can use IBM Watson to create clothing recommendations based on previous purchases, inventory forecasts (seeing how much stock you need for a certain item) and customer service.

The most nimble and adaptable companies and executives will thrive using artificial intelligence. Organizations that can quickly respond to opportunities will seize the advantage in the AI-enabled landscape.

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