A cheat sheet for AI buzzwords and their meanings
The arrival late last year of the ChatGPT chatbot, with its remarkably sophisticated answers to a vast array of queries, was a milestone in artificial intelligence.
Scientists were experimenting with “computer vision” and giving machines the ability to “read” as far back as the 1960s. Chatbots began life when the Beatles were still making music.
Today it’s possible to imagine a computer being able to perform most human tasks better than people can.
Whether you’re worried about being replaced by a machine or just intrigued by the possibilities, here are some frequently used AI buzzwords and what they actually mean.
Artificial intelligence
This qrefers to the use of technology to model human intelligence. AI promises a world of personalized products and news feeds and services that are cheaper, faster and free from human error. For example, factory managers or transport network operators could use it to make better use of their engineers’ time and spot component failures before they happen. Computer scientist John McCarthy coined the term in the 1950s, but the field didn’t take off in earnest until this century, when technology giants combined vast computing power with deep pools of user data. While AI can show humanlike abilities in data processing or conversation, the machines don’t yet “understand” what they’re doing or saying. They’re still relying essentially on algorithms.
Algorithm
An algorithm is a step-bystep process used to solve a problem. Take an input, apply some logic and you get an output. Humans have been using algorithms to solve problems for centuries. Some financial analysts spend their careers building algorithms that are able to predict future events and help them make money. Our world runs on these “traditional” algorithms, but recently there has been a shift toward “machine learning,” which builds on these ideas.
Machine learning
This is the process of feeding data into algorithms so they get more refined and sophisticated over time. It allows a computer to “learn” without necessarily having to be trained on the specifics of the job at hand. Take the iPhone photo app. Initially, it doesn’t know what you look like. But once you start tagging yourself as the face in photos taken across many years and in a variety of environments, the machine “learns” to recognize your face. The more data it’s fed, the more effective it is. To be sure, this depends on a capable model underneath that can differentiate a human face from, say, two fried eggs, a mushroom and a sausage on a plate.
Natural language processing
This is a branch of AI that helps computers understand, process and generate speech and text the way a human would. NLP relies on machinelearning algorithms to extract data from written text, translate languages, recognize handwritten words and discern meaning and context. It’s the underlying technology that powers virtual assistants such as Siri or Alexa and allows them to not only understand requests but respond in natural language. NLP can also gauge emotion in text, which is why if you tell Siri “I’m sad” it will suggest you call a friend or loved one. Other everyday applications include email spam filtering, web search, spell checking and text prediction.
Chatbots
Known as chatterbots in the 1990s, these are the products such as ChatGPT that can hold advanced, humanlike conversations with people about anything from historical trivia to lists of creative recipes using a watermelon. An early example are the tools used by companies on their “Contact Us” pages as a first line of defense when a customer needs help. These are relatively unsophisticated and limited in their conversational abilities, much like voice-activated virtual assistants. It’s expected that chatbots will rapidly improve as a result of recent advances in AI.
Computer vision
A field of AI that allows computers to scan visual information such as images and video, identifying and classifying objects and people. The systems can react to what they see and take or recommend a particular action. The technology is being used to track wildlife for conservation and guide autonomous vehicles. There’s been concern about its use in military operations and policing, where it’s been shown to exhibit racial bias and to lack the precision needed to reliably identify a particular person.