Artificial intelligence (AI) has been deeply intertwined with popular culture for decades. Movies like Blade Runner, Minority Report, The Terminator, and even superhero blockbusters like Avengers: Age of Ultron thrive on a fundamental human fear ”the rise of the machines.
Now, as AI use cases expand in the workplace, we have to go deeper than the pop culture interpretation.
The only problem is that artificial intelligence is no longer a standalone term. In addition to AI, machine learning has quickly become a building block of digital transformation. The difference between the two has become muddled to the point that even technical professionals struggle to separate the two.
If you want to take full advantage of AI and machine learning in your business, you need to start with a firm understanding of where one starts and the other ends.
Understanding the true meaning of AI
In 1956, John McCarthy, who’s regarded as the father of artificial intelligence, coined the term AI to refer to machines that perform tasks that would typically rely on human intelligence. It’s a broad term that can encompass computer systems carrying out anything from planning to problem solving, voice recognition, image processing, translation, and other smart tasks.
Artificial intelligence is meant to replace natural intelligence in a way that eliminates human error, saves time, increases efficiency, and increases workforce productivity by handling mundane tasks.
However, not all artificial intelligence is created equal. Within this broad category, there are two main types of AI to keep in mind:
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Narrow AI: Systems can mimic human intelligence but are limited to a pre-defined task. While narrow AI applications maximize efficiency in their specific use cases, they don’t have consciousness or adaptability to evolve beyond that function. The vast majority of current applications of AI fall into this category ”anything from your email spam filter to personalized Spotify lists, IBM Watson, self-driving cars, and more.
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General AI: This type of artificial intelligence is most often portrayed in pop culture. While we’re a long way off from conscious AI, this category refers to machines being able to handle any number of tasks rather than one pre-defined function. Technology isn’t at a point where we can list examples of general AI and many question if we should even try to get there. Stephen Hawking once said that general AI would accelerate itself to the point that humans, who are limited by slow biological evolution, couldn’t compete, and would be superseded.
But still, artificial intelligence is more of an umbrella term for machines that mimic human intelligence. Terms like machine learning are subsets of this category.
How machine learning is changing AI
Machine learning is a subset of AI that uses algorithms to allow computer systems to continually learn and improve performance of a given task. When you remove the ML from AI, you have a cumbersome, complex set of manually-programmed decision trees. With ML, AI can intelligently update and optimize its tasks.
And developments in machine learning are enabling solutions for all kinds of business use cases. Before machine learning took off, developers could experiment with AI through scripted automation. For example, financial services firms could take advantage of solutions that, thanks to complex decision trees, could determine whether a consumer was eligible for a loan. With machine learning, we can go beyond decision trees and scripted automation to improve future predictions and take advantage of more effective decision-making.
This is especially useful for e-commerce companies. At a time when personalized customer experiences and recommendations can make all the difference between a conversion and an abandoned cart, machine learning-powered AI helps you target product offerings to the right customers. Amazon‘s recommendation engine is the perfect example, pulling shopping data from millions of customers to present additional items prior to checkout. As a result, analysts estimated that 35% of Amazon sales came from its recommendation engine in 2013. And as the algorithm has learned over the last five years, that number has surely grown.
Regardless of the industry use case, the most significant function of machine learning is to add pattern recognition to artificial intelligence. By learning from constant streams of data and identifying emerging patterns, your AI solutions will deliver more effective insights.
Finding ways to leverage machine learning and AI
Machine learning, even in these early stages, is changing the business landscape profoundly. While ML will facilitate the advance of generalized AI use cases, it’s important not to fall back into the machines will replace humans mindset. Rather than eliminating jobs, these solutions are performing mundane tasks and freeing up time for people to focus on the high-level strategic thinking and creative work that drives growth.
However, there’s no one-size-fits-all approach to using artificial intelligence and machine learning. It’s up to business and tech leaders to find creative ways to apply these technologies to unique organizational needs.
