The technical and scientific nature of artificial intelligence technologies make AI an intimidating topic for most people. And the hype around what AI can do blurs the lines between reality and science fiction. While you might think that AI is too “bleeding edge” for your organization, it is very likely that you are already using AI in one way or another, at work and at home. Worry no more: let this descriptions guide you.
At Novacene, our mission is to enable businesspeople without a data science background to leverage the power of AI. Some definitions of AI are helpful but lengthy. So, with this post, I will attempt to make it easy for non-technical people to understand basic concepts AI, by providing simple and to-the-point explanations.
Artificial intelligence (AI)
AI refers to the ability of computers to perform tasks that resemble humans’ abilities to perceive the world around them and to make decisions based on that perception. For example, AI enables computers to:
- read a document and perform text analysis to understand topics;
- see the objects in an image and organize them in categories;
- hear and understand the instructions you tell it to perform;
- and more.
Sensory capabilities like seeing and hearing are possible thanks to sensors like cameras and microphones; while machine learning algorithms facilitate the cognitive capabilities like understanding and organizing.
Machine learning (ML)
Machine learning refers to a set of statistical methods that make it possible to predict an outcome. The larger the amount of data used to predict the outcome, the higher the likelihood that the prediction will be correct. Like humans, machines learn to make better decisions as their exposure to data increases.
Supervised machine learning
Supervised learning refers to the way in which a machine learning model is trained using labeled data. For example, let’s say you are trying to automate the verification of user accounts to determine whether they are real or fake. In a supervised learning scenario, you train the machine learning model using labeled data. In our example, labeled data could be an Excel spreadsheet with columns for the different attributes of an account and a column containing the corresponding label (real or fake). The machine learning algorithm would create a mathematical representation that reflects the relationships in the data, and use that representation to predict the labels for cases that it hasn’t encounter before, effectively predicting whether a new account is real or fake.
Unsupervised machine learning
Unsupervised learning is another method used by computers to predict outcomes. This method does not require labeled data, however. Instead, the algorithm uses statistical methods to make predictions. For example, an unsupervised learning model could be used to categorize responses to the question “Which issues drive your voting preferences” into categories such as the economy, the environment, healthcare, etc. In this case, the computer performs a few tasks: first, it converts the responses to a numerical representation. Then, it plots these numbers on a graph and groups nearby points together into clusters. These clusters represent the different categories.
Predictive analytics
In the context of AI, there is a continuum of ways in which analytics can be visualized. You’ll hear terms like descriptive analytics, diagnostic analytics, predictive analytics and prescriptive analytics. The first two refer to the ability to describe the past and the present, and the ability to diagnose patterns and relationships in the data. The last two are about predicting future outcomes and prescribing which actions to take.
The path forward
We hope that these explanations help increase your understanding of AI. But these AI definitions are just the beginning. We are here to support your organization as it begins to plan and incorporate AI into day-to-day operations. Don’t hesitate to get in touch to discuss your needs.
