New phrases are appearing everywhere as people become more acclimated to a world linked to AI. So, whether you’re trying to appear intelligent at the dinner table or dazzle in a job interview, here are some key AI terms to know.
Basic AI terms you should know
Artificial general intelligence, or AGI:
An idea that indicates a more advanced type of artificial intelligence than we now have, one that can execute tasks considerably better than humans while also educating and increasing its own capabilities.
AI ethics
Principles aimed at preventing AI from causing harm to humans, such as specifying how AI systems should collect data or deal with bias.
AI safety
A multidisciplinary field concerned with the long-term effects of AI and how it could abruptly evolve into a superintelligence hostile to humanity.
Algorithm
A set of instructions that enables a computer program to learn and analyze data in a specific way, such as recognizing patterns, and then learn from it and do tasks on its own.
Alignment
Changing an AI’s behavior to achieve a better result. This might range from content moderation to preserving positive interactions with humans.
Anthropomorphism
When humans endow inhuman objects with human-like traits. In AI, this can involve believing a chatbot is more humanlike and knowledgeable than it is, or that it is joyful, sad, or even sentient.
Artificial intelligence
The application of technology to replicate human intellect, such as in computer programs or robotics. A branch of computer science that seeks to create systems capable of performing human jobs.
Bias
In the case of big language models, errors are caused by training data. As a result, stereotypes may be used to attribute certain qualities to certain races or groups.
Chatbot
A program that communicates with humans by simulating human language through text.
ChatGPT
OpenAI’s AI chatbot that employs massive language model technologies.
Cognitive computing
An alternative phrase for artificial intelligence
Data augmentation
To train an AI, current data is re-mixed or a more diversified set of data is added.
Deep learning
A method of artificial intelligence and a subfield of machine learning that employs numerous factors to recognize complicated patterns in images, sounds, and text. The method is based on the human brain and uses artificial neural networks to generate patterns.
Diffusion
A machine learning method that adds random noise to an existing piece of data, such as a snapshot. Diffusion models teach their networks how to re-engineer or retrieve that image.
Emergent behavior
Emergent behavior occurs when an AI model demonstrates unanticipated abilities.
End-to-end learning, or E2E
A deep learning procedure in which a model is taught to complete a task from beginning to end. It is not trained to do a problem in a sequential manner, but rather learns from the inputs and solves it all at once.
Ethical considerations
An understanding of the ethical implications of AI, as well as issues with privacy, data usage, fairness, misuse, and other safety concerns.
Foom
Also called a fast takeoff or hard takeoff. The idea is that if someone creates an AGI, it may be too late to save humanity.
Generative AI
A content-creation technique that uses artificial intelligence to generate text, video, computer code, or graphics. The AI is fed massive quantities of training data and searches for patterns to develop its own unique replies, which can occasionally be identical to the source material.
Google Bard
Google’s AI chatbot that works similarly to ChatGPT but draws information from the current web, whereas ChatGPT is limited to data until 2021 and is not linked to the internet.
Guardrails
AI models are subject to policies and limits to guarantee that data is handled appropriately and that the model does not generate upsetting content.
Hallucination
A falsified reply from AI. Can include generative AI producing answers that are incorrect but stated with confidence as if correct. The causes of this are not well understood. For instance, an AI chatbot can give the wrong answer when asked, “When did Leonardo da Vinci paint the Mona Lisa?” by answering, “Leonardo da Vinci painted the Mona Lisa in 1815,” which is 300 years after it was actually created.
LLM, or large language model
A machine learning algorithm was developed to comprehend language and produce original material with human-like language.
Machine learning, or ML
Machine learning is an aspect of AI that enables computers to acquire knowledge and provide more accurate predictions without explicit programming. Can be coupled with training sets to generate new content.
Microsoft Bing
A search engine from Microsoft that can now provide AI-powered search results thanks to the technology underlying ChatGPT.
Multimodal AI
AI that can process a variety of inputs, such as text, photos, videos, and audio, is known as multimodal AI.
NLP
Natural language processing is a subfield of artificial intelligence that makes use of deep learning, machine learning, and learning algorithms to enable computers to comprehend human language.
Neural network
A neural network is a computational model that closely matches the architecture of the human brain and is used to find patterns in data. consists of interconnected nodes, or neurons, that have the capacity to learn through time and identify patterns.
Overfitting
Overfitting is a machine learning error when the model behaves too closely to the training data and may only be able to recognize particular examples in the training data but not in new data.
Prompt chaining
Prompt chaining is the ability of AI to influence future reactions by using knowledge from earlier interactions.
Stochastic parrot
An LLM analogy shows that however plausible the output may sound, the program does not have a deeper comprehension of the meaning underlying language or the environment around it. The expression alludes to a parrot’s ability to mimic human speech without comprehending its meaning.
Style transfer
Style transfer is the capability of an AI to understand the visual characteristics of one image and apply them to another by modifying the style of one image to the content of the other. Take Rembrandt’s self-portrait, for instance, and recreate it in the manner of Picasso.
Temperature
Controllable variables that determine how random the results of a linguistic model are. The model takes greater chances at higher temperatures.
Text-to-image generation
The process of producing visuals from written descriptions.
Training data
Datasets, such as text, graphics, code, or data, that are used to train AI models.
Transformer model
A deep learning and neural network design that tracks relationships in data, such as in words or sections of images, to understand context. As a result, it can examine the entire sentence and comprehend the context rather than studying a statement word by word.
The Turing test
The Turing test measures a machine’s aptitude for mimicking human behavior and is named after renowned mathematician and computer scientist Alan Turing. If a person is unable to identify the machine’s response from that of another human, the machine succeeds.
Weak AI
Weak AI, also known as narrow AI, is AI that is only capable of learning within the boundaries of its current skill set. The majority of AI today is Weak AI.
Zero-shot learning
Zero-shot learning is a test where a model is required to finish a job without the necessary training data.