Machine learning’s rise, applications, and challenges Posted on : Jun 21 - 2021

The terms “artificial intelligence” and “machine learning” are often used interchangeably, but there’s an important difference between the two. AI is an umbrella term for a range of techniques that allow computers to learn and act like humans. Put another way, AI is the computer being smart. Machine learning, however, accounts for how the computer becomes smart.

But there’s a reason the two are often conflated: The vast majority of AI today is based on machine learning. Enterprises across sectors are prioritizing it for various use cases across their organizations, and the subfield tops AI funding globally by a significant margin. In the first quarter of 2019 alone, a whopping $28.5 billion was allocated to machine learning research. Overall, the machine learning market is expected to grow from around $1 billion in 2016 to $8.81 billion by 2022. When VentureBeat collected thoughts from the top minds across the field, they had a variety of predictions to share. But one takeaway was that machine learning is continuing to shape business and society at large.

Rise of machine learning

While AI is ubiquitous today, there were times when the whole field was thought to be a dud. After initial advancements and a lot of hype in the mid-late 1950s and 1960s, breakthroughs stalled and expectations went unmet. There wasn’t enough computing power to bring the potential to life, and running such systems cost exorbitant amounts of money. This caused both interest and funding to dry up in what was dubbed the “AI winter.”

The pursuit later picked up again in the 1980s, thanks to a boost in research funds and expansion of the algorithmic toolkit. But it didn’t last, and there was yet another decade-long AI winter.

Then two major changes occurred that directly enabled AI as we know it today. Artificial intelligence efforts shifted from rule-based systems to machine learning techniques that could use data to learn without being externally programmed. And at the same time, the World Wide Web became ubiquitous in the homes (and then hands) of millions (and eventually billions) of people around the world. This created the explosion of data and data sharing on which machine learning relies. View More