Explore the fundamentals and sharpen your tech savvy.
Can your bank explain the ins and outs of algorithms and deep learning? Do you know your neural networks from your natural language processing? Can you spot an RPA working diligently in your office?
If so, congratulations! You’re an artificial intelligence (AI) ace.
For the rest of us, entering the world of AI seem more daunting. It’s uncharted territory, filled with new vocabulary, new technology and new resources to manage. Find your footing and move forward with confidence by exploring these AI fundamentals.
What’s the difference between artificial intelligence and machine learning?
The two concepts are related, but AI is broader and describes the entire discipline.
Artificial Intelligence: The field of AI encompasses a number of techniques that enable computers to emulate human behaviors, such as interpretation, understanding, reasoning, planning and communication. Machine learning, deep learning, natural language processing and intelligent automation are all subsets of AI.
Machine Learning: Machine learning is another general term. It describes the process of getting a computer to act without specifically being programmed. Instead, the computer learns by experience and without human involvement.
What happens behind the scenes to make AI work?
The typical AI toolkit includes these elements:
Algorithm: Algorithms are the recipes behind your organization’s AI, its most basic building blocks. They’re simple rules and step-by-step instructions—programming commands and math formulas, for example—that instruct the computer on how to solve problems on its own, using a specific set of inputs or “ingredients.”
Neural Network: A neural network helps a computer develop human-like functions, such as perception, reasoning, visual recognition or language processing. Its setup simulates the sophisticated hierarchies and connections between neurons in the human brain.
A neural network organizes dozens to millions of artificial neurons (called units) into layers, with a different type of processing occurring in each layer. As data and inputs move through the layers, the neural network adds findings and develops greater understanding—just as humans use multiple senses and types of thinking to interpret the world around them.
Deep Learning: Deep learning is the activity that occurs when data and inputs pass through the neural network. “Deep” refers to the numerous layers of processing and the vast amounts of data involved. Unlike traditional computing, which requires specific instructions at all stages, deep learning is autonomous and self-teaching; each time the system performs a task, it finds patterns and improves it performance.
Supervised Learning: Even autonomous machines need some help from time to time. Supervised learning describes the most common approach to “training” an AI application. Using a training set of data, the organization provides the computer with both the question and the answer.
For example, teaching a self-driving car to recognize traffic signals begins with the question: Is this a signal to stop? The answers might include a set of “Yes” images clearly labeled stop sign and red light, and a second set identified as “No” images.
Unsupervised Learning: With unsupervised learning, organizations provide the question without the answer. This approach is less common, and requires more upfront work to show the computer how to carry out advanced calculations. Computers capable of unsupervised learning represent the full potential of AI.
What are some specific applications of AI?
The possibilities with AI are almost endless; below are some common applications.
Natural Language Processing: NLP uses AI to train a computer to interpret and respond to human communication, in text or speech forms. NLP powers chatbots, and virtual assistants like Siri and Alexa.
Image Recognition: Also called computer vision, image recognition enables computers to identify objects, places, people—even handwriting—that exist as images. It powers iPhones that authenticate users by their face, and self-driving cars that recognize pedestrians in the roadway.
Chatbot: Also known as a conversational interface, a chatbot leverages NLP to conduct an interactive “chat” with a human user, through a website, mobile app or telephone system. Chatbots are most prevalent in customer service.
Virtual Assistant: An AI-powered application that offers help with common tasks. Siri and Alexa are two examples of virtual assistants for consumers.
Robotic Process Automation: Specialized software that is easily programmed to handle routine business processes, such as collecting data, updating spreadsheets or moving information between applications. RPAs quickly and accurately handle repetitive tasks and basic workflows. While not technically a component of AI, RPAs are a common first step before an AI initiative.
Intelligent Automation: Also known as intelligent process automation, IA is a more advanced form of RPAs, used to streamline business processes. With IA, not only do machines take over routine and repetitive tasks typically handled by humans, they also leverage AI to learn and, over time, do them better.
Up next in our AI series: Which AI use case is right for your bank? From customer experience and operational efficiency, to risk management and revenue growth, there are numerous ways for FIs to benefit from AI.