What's The Difference Between AI And Machine Learning?
When you're asked to evaluate the potential of AI or ML to solve your organisation's problems, you'd better understand the distinctions between the two. Artificial intelligence and machine learning get lumped together so often these days that it’d be easy for people to mistake them as synonymous.
That’s not quite accurate, though: They’re most certainly connected but not actually interchangeable.
“Artificial intelligence and machine learning are closely related, so it’s no surprise that the terms are used loosely and interchangeably,” says Bill Brock, VP of engineering at Very. If you’re not using AI or ML yet, you soon will be evaluating its potential for your organisation.
“AI as a workload is going to become the primary driver for IT strategy,” Daniel Riek, senior director, AI, Office of the CTO, Red Hat, recently told us. “Artificial intelligence represents a transformational development for the IT industry: Customers across all verticals are increasingly focusing on intelligent applications to enable their business with AI.
“This applies to any workflow implemented in software, not only across the traditional business side of enterprises, but also in research, production processes, and increasingly the products themselves. The improved scale of automation achievable with AI will quickly become critical for a company’s competitiveness building and will make AI a strategy-defining technology.”
Advances in natural language processing and other AI-enabled capabilities help organisations rethink customer service chat and analyse large pools of unstructured data. That will enable more predictive analytics, drive increased efficiency, and enhance decision-making.
So what’s the difference between AI and ML? Let’s start by defining the terms.
What AI means
“AI, simply stated, is the concept of machines being able to perform tasks that seemingly require human intelligence,” Brock says. “This involves giving computers access to a trove of data and letting them learn for themselves.”
Machine learning is a specific application or discipline of AI, but not the only one. In machine learning, Brock explains, “algorithms are fed data and asked to process it without specific programming. Machine learning algorithms, like humans, learn from their errors to improve performance.”
As a starting point for distinguishing AI and machine learning, it’s helpful to think of AI as the higher-level or umbrella category that encompasses multiple specific technologies or disciplines, and machine learning is one of them.
“AI includes various fields of study including ML, NLP (natural language processing), voice/audio recognition, computer vision/image recognition, search, routing, autonomous robots, autonomous transport and other disciplines,” says Mahi de Silva, CEO and co-founder of Amplify.ai.
Speaking of umbrellas, Michael McCourt, research engineer at SigOpt, offers a distinction-by-comparison for a rainy day:
“Machine learning is like a spoke running out of the artificial intelligence umbrella, with a much more specific definition.”
Let’s back up for a second: McCourt notes that AI by definition is very broad, it’s the umbrella, so much so that if you ask a group of ten people to give their definition, you’ll likely get ten different answers.
“Artificial intelligence is an umbrella term without a concrete definition, as it encompasses all mechanical, robotic, and automotive tasks that emulate human capabilities,” McCourt says.
Moreover, AI’s definition has changed, and it will continue to change over time: “Twenty years ago, tools like spellcheck were considered artificial intelligence,” McCourt notes. “Ten years ago, artificial intelligence meant being able to classify images.”
What Machine Learning means
While machine learning technologies and uses might evolve, the core definition is much more concrete and specific.
“Machine learning models generate findings based on stored data sets and queries for the purpose of learning a specific pattern,” McCourt says. “If the answer is not previously stored, machine learning analyses the environment to present its best guess as to what the correct response might be.”
Tom Wilde, CEO at Indico Data Solutions, points out that there’s a very current reason that AI and machine learning get used and confused in tandem.
“The reason for confusion is understandable: ML can be considered as the current ‘state of the art’ of AI,” Wilde says. Spell-check aside, he adds, machine learning is one of the oldest and best-established AI disciplines. It’s also the one bearing the most current fruit in terms of enterprise use cases.
Understanding the difference between AI and ML isn’t just a matter of clarifying terms or relieving annoyance with non-technical folks who just don’t get it. Rather, it’s table-stakes for success with AI projects.
“It’s important to distinguish between AI and machine learning, as this is critical to successfully designing, building, developing, and maintaining an application or platform,” Brock says. That’s true for your in-house knowledge and AI skills development; it’s also true for evaluating and selecting the right vendors.
Remember when every product suddenly had the word “cloud” added to its name? You may see some of that with AI and ML, too.
Beware AI-washing of products
“The confusion between artificial intelligence and machine learning creates some major problems,” says McCourt from SigOpt.
“First, it creates a moving goalpost for what artificial intelligence and machine learning success actually looks like.
“Second, the ambiguity opens up room for companies to inaccurately claim they are employing machine learning technology, without much fear that they will be challenged.”
That’s a steep downside, especially given that so many organisations are just beginning (if at all) to identify their potential AI opportunities. Mixed with high doses of hype, a lack of understanding of key terms makes it tough to properly evaluate options.
“While many companies describe themselves as using AI, many actually use very little machine learning and are mostly using rule-based systems,” Brock says.
This isn’t to say that we should reject the overlaps and connections between different terms and technologies; rather, we simply shouldn’t treat those overlaps and connections as meaning these are the same thing. Amplify.ai CEO de Silva notes that the various disciplines that AI encompasses, such as machine learning and NLP and computer vision, can have an amplifying effect when properly used together.
“It’s important to recognise that there is a lot of opportunity for cross-pollination across these areas of research and implementation, where these technologies ‘stack up’ to provide even more utility to humans,” he says.
Just make sure you see the lines of distinctions clearly to ensure your best odds of success in current and future AI projects. For now, knowing the difference between AI and machine learning is a great fundamental.
“For CIOs and IT decision makers, it’ll be important to be familiar with both concepts and work with teams, both in-house employees as well as third-party vendors and consultants, who have a comprehensive understanding of the concepts and their applications,” Brock says. “In the next two years, we’ll see an explosion of machine learning projects as many move into the production phase, so it will be vital to have the right level of expertise to ensure such projects are successful.”
What is supervised vs. unsupervised machine learning?
While we’re here, let’s also distinguish between the two general types of machine learning. Supervised machine learning is the more common of the two at this point in terms of use cases. This type of ML “teaches the machine by giving information on the parameters of the desired categories and letting the algorithms decide how to classify them,” Brock explains.
Unsupervised machine learning, on the other hand, uses no training data. As a result, Brock notes, it’s more complex, and as result has been used in fewer applications to this point.
But if you hear someone excitably using AI and ML in an interchangeable fashion, they might be thinking of unsupervised ML, because it doesn’t require as much (if any) human input and training as supervised ML.
“Unsupervised machine learning is where a lot of the excitement over the future of AI stems from,” Brock says. “Unsupervised machine learning is already being used, or is in the process of being developed, for applications such as image recognition, cancer detection, music composition, robot navigation, autonomous driving, and many other innovations.”
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