With the advent of software like ChatGPT, we’re learning a whole new language for artificial language, with new expressions and TLAs (two and three letter acronyms). Here are some of them explained to help you get up to speed.
ML: Machine learning
Machine Learning: The system learns from experience and without getting instructions from humans. Learning is a result of patterns which are then analyzed.
AI: Artificial intelligence
XAI: Explainable AI, the ability to understand or explain how an AI model makes predictions. It also refers to its anticipated impact and its potential biases.
LLM: Large language models (e.g. ChatGPT)
GPT: Generative pre-trained transformer
Responsible AI: AI that engenders trust and that companies can scale with confidence.
ChatGPT: An AI chatbot that was initially built on a family of LLMs collectively known as GPT-3
Chat: Refers to the chatbot front-end that OpenAI built for its GPT language model.
Machine (and algorithm) bias: Reoccurring errors in the system that create unfair (biased) outcomes. This is not a technical issue, but has to do with social and political expectations and what information the AI system has been fed, trained on and how it’s been corrected by humans, with their own biases. Which means it really is a business issue.
Data Ethics: “Data ethics is a branch of ethics that considers the impact of data practices on people, society and the environment. The purpose of data ethics is to guide the value and conduct of data practitioners in data collection, sharing and use” (from Open Data Institute). One example is if GPTs are gathering personal information on Canadians, are they doing it without informed consent.
FleeceWare: Beware of “fleeceware,” apps that start ChatGPT-curious users off with free trials, then stick them with recurring charges. One such app generated $700k+ in revenue last month.
Narrow AI: designed to complete a single, specific task without human assistance.
Artificial Intelligence: Refers to a machine that is mimicking human intelligence, where it learns from experience and problem solving. And it makes its own decisions without directions from a human. It’s a combination of technologies, including machine learning. In a 2018 statement by the American Medical Association (AMA), they said, “Artificial intelligence encompasses a host of computational methods that produce systems that perform tasks normally requiring human intelligence.”
Transparency: People who are impacted by AI technologies (products and services) have access to information, both internally and from external suppliers, regarding the technology.
If you come across other words related to AI, LLM or GPTs, and you’d like us to research and include them, just let us know at firstname.lastname@example.org, and we’ll update the list.
While ChatGPT may be the most recognizable option chatbot today, there are many competitors to ChatGPT in the field of natural language processing and conversational AI. Several of them are addressed in this article.
But first, a better understanding of what these concepts represent. I think Jess Peck from SearchEngineLand (An SEO’s guide to understanding large language models (LLMs) (searchengineland.com) does a good job of it with this analogy. To quote:
“There are two ways to get a person to do something – tell them to do it or hope they do it themselves. When it comes to computer science, programming is telling the robot to do it, while machine learning is hoping the robot does it itself. The former is supervised machine learning, and the latter is unsupervised machine learning. An unsupervised natural language machine learning model can be trained on many different kinds of datasets.”
This is the first article in a series on Artificial Intellicence.
For more articles in the series on Artificial Intelligence, go to:
- An AI (Artificial Intelligence) Primer
- AI’s Capabilities
- AI Achievements
- ChatGPT (or any AI bot) and Your Medical office
- Teaching AI in Medical School
- Patient Trust in AI Chatbots & ChatGPT
- Competitors to ChatGPT
- AI Policies and Regulatory Challenges
- Ai Bias
- AI’s Limitations, Concerns and Threats
- What AI Can’t and Shouldn’t Do
- AI and Accountability
- The Dangers of AI
- The Future of Generative AI
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