Mediaboss Marketing

Weve compiled a list of phrases and concepts useful to understanding artificial intelligence, in particular the new breed of A.I.-enabled chatbots like ChatGPT, Bing and Bard.

If you dont understand these explanations, or would like to learn more, you might want to consider asking the chatbots themselves. Answering such questions is one of their most useful skills, and one of the best ways to understand A.I. is to use it. But keep in mind that they sometimes get things wrong.

Bing and Bard chatbots are being rolled out slowly, and you may need to get on their waiting lists for access. ChatGPT currently has no waiting list, but it requires setting up a free account.

For more on learning about A.I., check out The New York Timess five-part series on becoming an expert on chatbots.

Anthropomorphism: The tendency for people to attribute humanlike qualities or characteristics to an A.I. chatbot. For example, you may assume it is kind or cruel based on its answers, even though it is not capable of having emotions, or you may believe the A.I. is sentient because it is very good at mimicking human language.

Bias: A type of error that can occur in a large language model if its output is skewed by the models training data. For example, a model may associate specific traits or professions with a certain race or gender, leading to inaccurate predictions and offensive responses.

A brave new world. A new crop of chatbotspowered by artificial intelligence has ignited a scramble to determine whether the technology could upend the economics of the internet, turning todays powerhouses into has-beens and creating the industrys next giants. Here are the bots to know:

ChatGPT. ChatGPT, the artificial intelligence language model from a research lab, OpenAI, has been making headlines since November for its ability to respond to complex questions, write poetry, generate code, plan vacationsand translate languages. GPT-4, the latest version introduced in mid-March, can even respond to images(and ace the Uniform Bar Exam).

Bing. Two months after ChatGPTs debut, Microsoft, OpenAIs primary investor and partner, added a similar chatbot, capable of having open-ended text conversations on virtually any topic, to its Bing internet search engine. But it was the bots occasionally inaccurate, misleading and weird responsesthat drew much of the attention after its release.

Ernie. The search giant Baidu unveiled Chinas first major rival to ChatGPT in March. The debut of Ernie, short for Enhanced Representation through Knowledge Integration, turned out to be a flopafter a promised live demonstration of the bot was revealed to have been recorded.

Emergent behavior: Unexpected or unintended abilities in a large language model, enabled by the models learning patterns and rules from its training data. For example, models that are trained on programming and coding sites can write new code. Other examples include creative abilities like composing poetry, music and fictional stories.

Generative A.I.: Technology that creates content including text, images, video and computer code by identifying patterns in large quantities of training data, and then creating original material that has similar characteristics. Examples include ChatGPT for text and DALL-E and Midjourney for images.

Hallucination: A well-known phenomenon in large language models, in which the system provides an answer that is factually incorrect, irrelevant or nonsensical, because of limitations in its training data and architecture.

Large language model: A type of neural network that learns skills including generating prose, conducting conversations and writing computer code by analyzing vast amounts of text from across the internet. The basic function is to predict the next word in a sequence, but these models have surprised experts by learning new abilities.

Natural language processing: Techniques used by large language models to understand and generate human language, including text classification and sentiment analysis. These methods often use a combination of machine learning algorithms, statistical models and linguistic rules.

Neural network: A mathematical system, modeled on the human brain, that learns skills by finding statistical patterns in data. It consists of layers of artificial neurons: The first layer receives the input data, and the last layer outputs the results. Even the experts who create neural networks dont always understand what happens in between.

Parameters: Numerical values that define a large language models structure and behavior, like clues that help it guess what words come next. Systems like GPT-4 are thought to have hundreds of billions of parameters.

Reinforcement learning: A technique that teaches an A.I. model to find the best result by trial and error, receiving rewards or punishments from an algorithm based on its results. This system can be enhanced by humans giving feedback on its performance, in the form of ratings, corrections and suggestions.

Transformer model: A neural network architecture useful for understanding language that does not have to analyze words one at a time but can look at an entire sentence at once. This was an A.I. breakthrough, because it enabled models to understand context and long-term dependencies in language. Transformers use a technique called self-attention, which allows the model to focus on the particular words that are important in understanding the meaning of a sentence.

See original here:
Artificial Intelligence Glossary: AI Terms Everyone Should Learn - The New York Times