If you’ve ever issued the command: “Siri, call Mom,” you’re using artificial intelligence.
Artificial Intelligence (AI) is an aspect of computer science in which machines can be programmed to interact with people in a way that seems human – with the ability to speak responses, solve problems and learn. AI began in 1956, but it is only recently with the advent of big data computing power that it has become commercially viable. Now we use AI to set our morning alarm, play music and change TV channels. Companies are investing in AI to power manufacturing, research and development, sales, human resources, and customer service.
With the rapid evolution of AI, buzzwords are everywhere. Knowing some of the terms associated with AI helps us participate in the conversation and see the possibility of this fascinating technology in our own lives and businesses. Here is a glossary of terms we assembled to improve our understanding of AI.
Artificial Intelligence – see above.
Chatbot – is a computer program that conducts a conversation using either spoken words or written text. The dialog is programmed to simulate human interaction. If you’ve purchased anything online, chances are you’ve talked with a chatbot.
Deep Learning or DL – is a subset of Machine Learning and depends on Neural Networks (see definitions below). Deep Learning is a system of powerful computers networked together with the intention of processing huge volumes of data to identify patterns. Google Translate uses DL not only to translate text from one language into another, but also to interpret images as well. The next time you travel abroad, take a photo of your menu in a foreign language and Google Translate will instantly interpret it into the language of your choice. Photograph a street sign and receive an immediate translation. This form of DL is known as optical character recognition.
Machine Learning or ML – is a system that enables machines to use data to improve functions over time. Technologies that use ML get “smarter” with use. Personal digital assistants like Siri, Cortana, Alexa or Google Assistant use ML to process commands and fulfill the action. These assistants require task-specific data, which is a pre-set algorithm. The more you use them, the more they adapt and advance in their “thinking.”
Natural Language Processing or NLP – is a technology that allows machines or systems to understand human language. For example, while typing into a search engine you may notice that text appears to finish your words or even suggest the rest of the sentence. This is a form of NLP called predictive text. NLP is also used in speech recognition tools such as speaking into your phone to translate your words into text, as well as online chatbots that interact with and understand users’ questions to help with various activities.
Neural Network or NN – is a set of connected computer systems that were inspired by and act similar to the network of neurons of a human brain. For example, researchers working on Google Brain, a network of 1,000 computers programmed to process images and find patterns, taught the NN to recognize human faces and bodies. Face recognition is an example of NN that makes connections by identifying similar features from a subject’s face. You may notice while tagging friends and family on Facebook photos that there is a box that often appears around faces indicating who they may be. Although you never told Facebook the name of that friend, it uses face recognition to match with a similar profile.
Robot – is a machine that can be computer programmed to carry out actions automatically. Robots are used to build cars, assist surgeons and explore outer space and the depths of the ocean. Robots can perform tasks that are dangerous or repetitive for humans. If your Roomba vacuums your floors, you have assigned a robot to do a household chore that you probably don’t miss doing yourself.
Artificial Intelligence improves human lives. It also will change the way some humans earn a living. A Forrester Research report predicted that nine percent of U.S. jobs will be lost to automation in 2018. Yet, at the same time, another two percent of jobs will be created to support the “automation economy.”
As companies seek efficiency, cost containment and scale, initiatives such as digital transformation, which depend on the use of AI, will have greater and more noticeable impact. Look for increased automation of white-collar jobs in functions such as information technology, data management, human resources, talent acquisition, and compliance. According to Forrester, “Companies that master automation will dominate their industries.”
Forrester also predicts that AI and automation will spur a backlash among the populace, impeding its progress. Ultimately automation will win because “its economic value outweighs any political resistance.” Forrester further recommends that companies that invest in automation should also invest in change management programs and public relations campaigns.
We hope this glossary of terms is useful to you. When you need help communicating your company’s AI and digital transformation initiatives to key stakeholders, have your chatbot give us a call. Or email us at firstname.lastname@example.org.