Artificial Intelligence (AI) is all the rage in tech circles these days. In fact, in its “Top 10 Strategic Technology Trends for 2018” report, the global IT research and analysis firm Gartner revealed that calls from clients regarding AI-related topics increased more than 500% during the last year.
When tech topics become this hot, distinctions between terms and jargon can become blurred. So, to clarify for our readers, we offered these simple definitions basic AI elements in one of our previous posts:
- Machine Learning -- Software applications that become more accurate in predicting outcomes by using algorithms for statistical analysis.
- Chatbots – Computer programs that can respond to texts or chat messages from people, emulating the conversational language of humans.
- Mobile Messaging – Related to Mobile Instant Messaging (MIM), an app that applies AI such as machine learning and/or chatbots to communicate between computers and people through mobile devices.
Each of the technologies has multiple nuances, but machine learning especially seems to inspire confusion and fallacies. Perhaps because it’s become a sort of vanguard of AI, introducing businesses to the realm of AI. As CIO contributor Mary Branscombe writes in a recent column: “Machine learning is proving so useful that it's tempting to assume it can solve every problem and applies to every situation.”
So, we reviewed Branscombe’s article and cherry-picked items from her list of “machine learning myths.” Here are three points we believe are particularly pertinent to business leaders sizing up AI for their organizations:
- All AI is not machine learning
Beware any discussion that uses the terms AI and machine learning interchangeably. AI is involved in areas such as robotics and natural language processing, for example. But neither of those fields must necessarily involve machine learning. Branscombe’s advice for understanding the role machine learning plays is “think of it as anything that makes machines seem smart.” Machine learning essentially is about learning patterns and predicting outcomes. “The results might look ‘intelligent’,” she explains. “But at heart it’s about applying statistics at unprecedented speed and scale.”
- Machine learning is objective
The old data-processing adage “garbage in, garbage out” applies to machine learning. If data sets include some sort of bias, machine learning will replicate the bias or even amplify it. Branscombe offers the example of a machine searching for photos of CEOs on the internet. The machine likely will return with pictures of white males because today most CEOs are indeed white males. But all CEOs are not white males. So, these results reflect a pattern but are not a complete, accurate or objective depiction of reality. As Branscombe explains, data sets must be “trained” to be useful in machine learning applications.
- Machine learning will eliminate jobs
Machine learning systems enhance efficiency in repetitive tasks, improve compliance with complex regulations and reduce costs in the process. So, yes, some current jobs may become obsolete. But like previous generations of automation, machine learning also will create new business opportunities in areas such as customer service and product innovation, which means new positions will be created, too. In fact, some analysts believe machine learning – and AI in general – will generate a net gain in employment over the long run.
Whatever form machine learning may come to your company, the support of an IT Managed Services Provider (MSP) can help integrate this emerging tech into your everyday operations.