ByCory McNeley – Artificial intelligence (AI) has transformed the business landscape and changed how we work. Its capability to automate tasks, analyze extensive datasets efficiently and provide concise business insights facilitates both the speed and quality of business operations.

“Artificial intelligence” is often used to describe other technologies, such as machine learning (ML) and deep learning (DL). However, each of these technologies is distinct, and those differences impact which solution is right for your specific challenges. Understanding the high-level differences between each and the challenges that remain with implementation and adoption can help you have more meaningful and direct conversations about the role of these technologies in your organization.

AI is centered on programs that replicate common human-like skills. AI can solve problems, perform advanced calculations, and make decisions through the use of statistical models, neural networks and programmed rules. AI is an umbrella term that also includes various subsets of technology like ML and DL.

ML allows programs to identify patterns from data, which is used to enhance the program’s performance over time without the need for explicit programming. Common learning models include supervised, unsupervised and reinforcement learning techniques. This subset of AI is especially useful for data-driven decisions with extremely large data sets, such as sales forecasting. DL uses neural networks, a technique to replicate the human brain that is commonly found in image recognition and detection systems, as well as advanced AI applications such as autonomous vehicles.  Read On: