Artificial intelligence (AI) has been the stuff of science fiction for decades, yet technology is finally bringing intelligent computer systems to life. While Elon Musk has warned that AI could create immortal dictators, companies around the world have been deploying AI to improve customer experience and streamline business practices for years.
When it comes to how to use these new technologies, it’s important to think beyond just machine learning vs artificial intelligence and understand how they can work together.
While often used synonymously with AI, machine learning is actually a subset of AI technology, describing the process by which systems grow beyond core programming to expand toward intelligence.
Though people often use artificial intelligence and machine learning interchangeably, there are important differences between the two – especially when building customer experiences.
Feed the machine
Where AI aims to create truly intelligent systems based on human intelligence, machine learning leverages large data sets to optimize computer systems for specific tasks. If you’ve ever wondered how Facebook recognizes your friends’ faces in your photos or Netflix can predict the perfect show for the next hour of your weekend binge, that’s machine learning at work. The more data a system receives, the more sophisticated results it can produce.
Increasingly, machine learning seamlessly powers consumer experiences. Since 2015, Google has deployed machine learning in RankBrain, an intelligent component of its core algorithm. As its algorithm has matured, Google has focused on delivering contextual results based on a user’s individual search history, user behavior, and their experience on the websites they visit. This has resulted in search results that grow more tailored to individual users.
Solving large customer problems through data
Software has always aimed to improve human intelligence and creativity. Yet, until recently, all software we use was explicitly programmed for a specific purpose and outcome. Creating true AI systems requires a fundamentally different approach to computer systems, one that uses large data sets to grow beyond its base coding structure without human input.
Rich Brookfield, VP of Technology at 352 Inc., believes both machine learning and AI have the ability to change the way we think about software experiences.
“When you think about traditional programming, you are really talking about immutable outcomes,” Brookfield said in an interview for this article. “You expect the program to always exhibit the same behavior and provide the same results. Rather than being a true artificial intelligence, machine learning is a completely different approach in the sense that, just like a human, your software changes as it processes more data, allowing it to outperform the capabilities of the person who wrote it.”
Machine learning offers a huge opportunity for businesses to create more rewarding customer experiences and improve business efficiencies. In recent years, chatbots have risen to perform business functions like automated customer support and delivery tracking. According to a recent study by Juniper Research, chatbots alone will result in $8 billion annual savings for business by 2020.
Chatbots, just like voice and image recognition systems used by providers like Google and Amazon, all work within human-supervised frameworks to gather data, analyze patterns, and predict future behavior to optimize service to free employees for more important tasks.
As technology progresses, machine learning will continue to drive greater internal efficiency, build smoother experiences, and refine business models. While we’ll hopefully avoid Elon Musk’s robot AI apocalypse, machine learning and restricted AI systems will continue to shape business and human experience for years to come.