Artificial Intelligence Or Machine Learning: What's Right For Your Business?

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.

Defining Artificial Intelligence And Machine Learning

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.

Business Applications Of AI And ML

The world of communications, marketing and customer service is experiencing major disruption as a result of advancements in AI. Commercially available and custom-developed AI tools are helping companies provide high levels of customer service by employing advanced chatbots with more knowledge and flexibility than traditional chatbots. They can dissect and resolve complex inquiries without the need for human intervention. The natural language processing (NLP) aspect of modern AI allows these tools to provide customized marketing and communications that are reactive and continually evolving.

Common applications of ML technology include hyper-segmented customer profiling, predictive maintenance and fraud detection. Each of these is based on labeled (structured data), unlabeled (unstructured data) and reinforcement learning, where prior outputs are evaluated and used as inputs to adjust and refine ML’s results.

Read the full article published by Forbes.