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There’s no denying that machine learning and artificial intelligence (AI) are invading the enterprise at warp speed. Almost every industry is affected by the technology, and the innovation it inspires is fascinating. According to Gartner, machine learning is one of the top 10 strategic technology trends for 2016. Most of us are already — often unwittingly — interfacing with the technology when we use Google, Siri, Netflix and almost everything smartphone related (how does my iPhone know where I’m headed every time I start my car?).
On Tuesday night at our B2B IT Forum on machine learning, our moderator, BBJ executive editor Doug Banks, began by asking our panelists from Nara Logics, Luminoso, Zaius, and RapidMiner, a simple question: what’s the difference between machine learning and AI? Is it accurate to describe machine learning as a subset of AI?
Their near unanimous response: they are one and the same. AI is defined as the intelligence exhibited by machines or software. It is also the name of the academic field of study which studies how to create computers and computer software that are capable of intelligent behavior. So from a B2B standpoint, why should we care about machine learning?
According to Ingo Mierswa, founder and CTO at RapidMiner, the key is that machine learning allows people to make decisions extremely fast, no matter the complexity, about things they don’t necessarily know much about.
But in what areas do these decisions need to be made? Where can machine learning be found? Or in other words, how is artificial intelligence invading the enterprise?
Jana Eggers, CEO at Nara Logics, spoke on behalf of all the panelists when she said, “there are very few areas that are NOT being impacted by machine learning.” The more obvious areas include marketing and sales, analytics and cyber security, although even lesser known areas (farming was referenced) are seeing huge advances in machine learning as well. Medicine is being transformed by the technology. Due to machine learning, we are now seeing better image analysis, especially in radiology.
“There will come a time when human resources will receive recommendations from machine learning,” explained Mark Gally, CEO at Zaius. Although still in the early years, HR is beginning to use machine learning to make predictions – such as determining whether or not someone is going to be a successful employee. It’s also being used for alerts when employees might be getting ready to make a move by tracking their LinkedIn profiles.
But if machine learning is a method of data analysis, it’s important to first qualify the data.
“Good data is data that is relevant and on-point for your particular problem, whatever that happens to be,” said Dennis Clark, co-founder and CSO at Luminoso.
“Good data is data you know. It is whatever data you are going to use for a specific instance – data that is applicable to your situation,” added Jana Eggers.
A recent Forrester report confirms what our panelists discussed: “Artificial intelligence systems, which include robots, automation, smart machines and machine learning systems, will replace 7% of U.S. jobs by 2025. That’s a net reduction because the analyst firm predicts that technology will replace 16% of U.S. jobs but will create the equivalent of another 9%, leaving a 7% total reduction.”
One audience member asked: are you concerned about machines taking over for humans? As machines become as smart and then smarter than humans, are you worried about the future?
“Let’s balance the hope for today with the reality of what might be coming,” said Eggers. “If we go in with our eyes wide shut, then we may face some of those problems.” In other words, if we’re already thinking about what’s in our future, we will not be blindsided by it.