It’s nearly impossible to check the news and not see some mention of the pervasive impact of artificial intelligence. From top technology companies releasing competing software tools to fear-mongering over robots taking over the world, everyone is talking about AI. But what does AI really mean? Actually, it means a lot of different things.
In this post we’ll cover the top AI terms you need to get started on your AI journey. You may be familiar with some of the basic concepts for successful AI implementations such as a strong foundation of data. But we’ll dive a layer deeper and explain how this data can be especially advantageous for models such as neural networks.
What is artificial intelligence? At Digital Nebula we think of artificial intelligence as an umbrella term for a variety of more specific intelligent computer functions. One example of this layered umbrella is shown in the graphic below:
Photo credit: Prowess Consulting
Machine Learning is one of the most well-known subcategories of artificial intelligence. At a high level computers learn just as human do through 1. ingesting 2. learning 3. categorizing and 4. taking action. For example computer image recognition, similar to human vision, is a specific type of Machine Learning that uses Deep Learning. Deep Learning removes human computation from the loop. When a computer is shown a picture of a car, it may not know how to initially makes sense of the data. Over time, with more images of cars, the computer is able to accurately label the images.
Photo Credit: XenonStack
The feature extraction and classification seen in the image above is known as a Neural Network. An artificial neural network is an interconnected group of data points, similar to the network of neurons in a brain.
Using statistical conclusions to find patterns in data is known as Data Science. Statistical Machine Learning uses the same math as Data Science, but integrates it into algorithms that get better on their own. Most AI actions are initiated through Algorithms, procedures or formulas for solving problems, based on conducting a sequence of specified actions. There are numerous algorithms used by Data Scientists. We’ll dive in deeper to each of them in a future article, but here is a chart of their frequency of use:
Photo Credit: KD Nuggets
Decision Tree algorithms composed of hard if-then rules were the initial tools used for Natural Language Processing (NLP). NLP overlaps with Machine Learning under the artificial intelligence umbrella, but focuses on associative connections between written or spoken languages. Probabilistic statistics are now the primary algorithms used for NLP as they allow more room for creativity in associating similar words.
Finally it’s important to understand the difference between supervised and unsupervised learning. Supervised Learning uses data with known labels to create models then makes predictions based on the input data. Unsupervised Learning works off unlabeled data to differentiate the given input data.
Photo Credit: Leonardo Araujo dos Santos
As you likely recognized, the key to success in AI is having a large amount of data as your foundation. From there you can work with experts like our team at Digital Nebula to recommend the best approach to leverage that data to solve current pain points or generate new lines of business. Get in touch today by emailing firstname.lastname@example.org, calling 833-AI-TOOLS or chat us through the chatbot on the right hand corner of the site. –>