FOMO: The Fear of Missing Out
It was Bill Gates who said, “We always overestimate the change that will occur in the next two years and underestimate the change that will occur in the next ten.” Nowhere is the outlook more on display that when it comes to the topic of artificial intelligence (AI).
Make no mistake, technology in general and AI specifically are having a major impact on the work growth-focused organizations are taking. AI is absolutely something you should be aware of, and, to some degree, keeping track of. It is not, however, something that should be at the top of any small or mid-market growth company executives attention or worry list.
What Is Artificial Intelligence
Part of the difficulty with addressing AI is that it often means a bunch of different things to different people. What’s more, the term AI is often used to infer things that are not necessarily in place. AI is very confusing to many, so I turned to my friends at HubSpot, who published a nice piece on important definitions surrounding AI. Here are some of the key terms:
Artificial Intelligence: In the most general of terms, artificial intelligence refers to an area of computer science that makes machines do things that would require intelligence if done by a human.
Machine Learning: In short, machine learning is the ability of a program to absorb huge amounts of data and create predictive algorithms.
If you’ve ever heard that AI allows computers to learn over time, you were likely learning about machine learning. Programs with machine learning discover patterns in data sets that help them achieve a goal. As they analyze more data, they adjust their behavior to reach their goal more efficiently.
Deep Learning: On the far end of the AI spectrum, deep learning is a highly advanced subset of machine learning. Deep learning can find super-complex patterns in data sets by using multiple layers of correlations. In the simplest of terms, it does this by mimicking the way neurons are layered in your own brain. That’s why computer scientists refer to this type of machine learning as a “neural network.”
Natural Language Processing: Natural language processing (NLP) can make bots a bit more sophisticated by enabling them to understand text or voice commands. On a basic level, spell check in a Word document or translation services on Google are both examples of NLS. More advanced applications of NLS can learn to pick up on humor or emotion.