Machine Learning is the Right Way Forward for Data Science, not Artificial Intelligence
We get Wooed by the term Artificial Intelligence, but its Machine Learning that reinforces development. Here’s How.
Everyone in the digital era go crazy about the term “artificial intelligence,” evoking ideas of creative machines anticipating our every whim, though the reality is more banal: “For the foreseeable future, computers will not be able to match humans in their ability to reason abstractly about real-world situations.” This is from Michael I. Jordan, one of the foremost authorities on AI and machine learning, who wants us to get real about AI.
“People are getting confused about the meaning of AI in discussions of technology trends—that there is some kind of intelligent thought in computers that is responsible for the progress and which is competing with humans. We don’t have that, but people are talking as if we do,” he noted in the IEEE Spectrum article.
He wrote in an article for Harvard Data Science Review, we should be talking about ML and its possibilities to augment, not replace, human cognition. Jordan calls this “Intelligence Augmentation,” and uses examples like search engines to showcase the possibilities for assisting humans with creative thought.
Let us now actually make out the difference between ML and AI technically:
Artificial Intelligence is the science and engineering of making intelligent machines, especially intelligent computer programs. It is related to the similar task of using computers to understand human intelligence, but AI does not have to confine itself to methods that are biologically observable.
Machine learning is a branch of artificial intelligence (AI) and computer science which focuses on the use of data and algorithms to imitate the way that humans learn, gradually improving its accuracy.
And, to be clear, machines are much better at some things. For instance, people could do low-level pattern-matching but at a significant cost, whereas machines are able to perform such mundane tasks at relatively little cost. Another example is that ML is broadly used for fraud detection in financial services. We could have people poring over millions upon billions of transactions, but it makes more sense to point computers at the problem.
We know that most AI projects fail.The more we get “real” with AI, in other words, the more likely we’ll find success. Fortunately, Jordan wrote, most of the time when we’re talking about AI, we really mean ML. “ML is an algorithmic field that blends ideas from statistics, computer science and many other disciplines to design algorithms that process data, make predictions and help make decisions,” he wrote in the Harvard Data Science Review. ML is essential to “any company in which decisions could be tied to large-scale data,” he added.So..the first rule for success in AI is to stop doing AI, and instead consider data science problems as fundamentally about ML, about finding patterns in large quantities of data. It’s not Jetsons, but it’s real.