The AI landscape moves at a breakneck speed, so you’d be forgiven if you’ve been caught off guard by an unfamiliar term (or two). Staying up to date with the latest AI jargon is becoming increasingly important as the technology impacts our daily lives in more and more ways.
This is especially true at work, where AI literacy is the new must-have skill for employers. If you don’t know your AGI from your LLM though, don’t worry. We’ve compiled an A-to-Z list of popular AI terms and explained what each concept means in layman’s terms, to help you learn more about the technology that continues to shape the world around us.
From basic touchpoints like machine learning to more complex concepts like quantum AI, read on to brush up on some interesting terms and learn more about the brave new world of artificial intelligence.
What is AI?
Short for artificial intelligence, AI refers to the intelligence of machines as opposed to the intelligence of sentient beings like humans. AI systems work by receiving large amounts of training data, analyzing the data for patterns, and using these patterns to generate output.
While the concept has been around since the 1950s, artificial intelligence has been brought into the mainstream in recent years due to breakthroughs made by AI developers like OpenAI. The study of AI is vast, and expanding every year, so read on to learn more about artificial intelligence, and related concepts in 2024.
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A for Artificial General Intelligence (AGI)
AGI is a theoretical type of AI that exhibits human-like intelligence and is generally considered to be as smart or smarter than humans. While the term’s origins can be traced back to 1997, the concept of AGI has fallen into the mainstream in recent years as AI developers continue to push the frontier of the technology forward.
For instance, in November 2023 OpenAI revealed it was working on a new AI “superintelligence” model codenamed Project Q*, which could bring the company closer to realizing AGI. It should be emphasized, however, that AGI is still a hypothetical concept, and many experts are confident the type of AI will not be developed anytime soon, if ever.
B is for Big Data
Big data refers to large, high-volume datasets, that traditional data processing methods struggle to manage. Big data and AI go hand in hand. The gigantic pool of raw information is vital for AI decision-making, while sophisticated AI algorithms can analyze patterns in datasets and identify valuable insights. When working together, they help users make more insightful revelations, much faster than through traditional methods.
B is for Bias
AI bias occurs when an algorithm produces results that are systematically prejudiced against certain types of people. Unfortunately, AI systems have consistently been shown to reflect biases within society by upholding harmful beliefs and encouraging negative stereotypes relating to race, gender, and national identity.
These biases were emphasized in a now-deleted article by Buzzfeed, which displayed AI-generated Barbies from all over the world. The images supported a variety of racial stereotypes, by featuring oversexualized Caribbean dolls, white-washed Barbies from the global south, and Asian dols with inaccurate cultural outfits.
C is for ChatGPT
You’ve probably heard of this one, but it’s still important to mention as no AI glossary can be considered complete without a nod to the generative AI chatbot that changed the game when it launched back in November 2022.
In short, ChatGPT is the product that has shifted the AI debate from the server room into the living room. It has done from artificial intelligence what the iPhone did for the mobile phone, bringing the technology into the public eye by virtue of its widely accessible model.
As we recently revealed in our Impact of Technology in the Workplace report, ChatGPT is easily the most widely used AI tool by businesses – and may even be the key to unlocking the 4-day workweek.
Its influence may fade over time, but the world of AI will always be viewed through the prism of before and after ChatGPT’s birth.
C is for Compute
Standing for ‘computing power’, compute refers to the computational resources required to train AI models to perform tasks like data processing and making predictions. Typically, the more competing power used to train an LLM, the better it can perform.
Computing power relies on a lot of energy consumption, however, which is sparking concern among environmental activists. For instance, research has revealed that is takes 1GWh of energy to power responses for ChatGPT daily, which is enough energy to power 30,000 US households.
D is for Diffusion
Diffusion models represent a new tier of machine learning, capable of generating superior AI-generated images. These models work by adding noise to a dataset before learning to reverse this process.
By understanding the concept of abstraction behind an image, and creating content in a new way, diffusion models create images that are more sharpened and refined than those made by traditional AI models, and are currently being deployed in a range of AI image tools like Dall-E and Stable Diffusion.
E is for Emergent Capabilities
Emergent behavior takes place when AI models produce an unanticipated response outside of its creator’s intention. Much of AI is so complex its decision-making processes still can’t be understood by humans, even its creators. With AI models as prominent as GPT4 recently exhibiting emergent capabilities, AI researchers are making an increased effort to understand the how and the why behind AI models.
F is for Facial Recognition
Facial recognition technology relies on AI, machine learning algorithms, and computer vision techniques to process stills and videos of human faces. Since AI can identify intricate facial details more efficiently than manual methods, most facial recognition systems use an artificial neural network called convolutional neural network (CNN) to enhance its accuracy.
G is for Generative AI
Generative AI is a catch-all term that describes any type of AI that produces original content like text, images, and audio clips. Generative AI uses information from LLMs, and other AI models, to create outputs, and powers responses made by chatbots like ChatGPT, Gemini, and Grok,
H is for Hallucination
Chatbots don’t always produce correct or sane responses. Oftentimes, AI models generate incorrect information but present it as facts. This is called AI hallucination. Hallucinations take place when the AI model makes predictions based on the dataset it was trained on, instead of retrieving actual facts.
Most AI hallucinations are minor and may even be overlooked by the average user. However, sometimes hallucinations can have dangerous consequences, as false responses produced by ChatGPT have previously been exploited by scammers to trick developers into downloading malicious code.
I is for Intelligence Explosion
Bearing similarities to AGI, the intelligence explosion is a hypothetical scenario where AI development becomes uncontrollable and poses a threat to humanity as a result. Also referred to as the “singularity”, the term represents an existential threat felt by many about the rapid and largely unchecked advancement of the technology.
J is for Jailbreak
Jailbreaking is a form of hacking with the goal of bypassing the ethical safeguards of AI models. Specifically, when certain prompts are entered into chatbots, users are able to use them free of any restrictions.
Interestingly, a recent study by Brown University found that using languages like Hmong, Zulu, and Scottish Gaelic was an effective way to jailbreak ChatGPT. Learn how to jailbreak ChatGPT here.
J is for Job Insecurity
As AI continues to automate manual processes previously performed by humans, the technology is sparking widespread job insecurity among workers. While most workers shouldn’t have anything to worry about, our Tech.co Impact of Technology on the Workplace report recently found out that supply chain optimization, legal research, and financial analysis roles are the most likely to be replaced by AI in 2024.
L is for Large Language Models (LLMs)
LLMs are a specialist type of AI model that harnesses natural language processing (NLP) to understand and produce natural, humanlike responses. In simple terms, make tools like ChatGPT sound less like a bot, and more like you and me.
Unlike generative AI, LLMs have been designed specifically to handle language-related tasks. Popular examples of LLMs you may have heard of include GPT-4, PaLM 2, and Gemini.
M is for Machine Learning
Machine learning is a field of artificial intelligence that allows systems to learn and improve from experience, in a similar way to humans. Specifically, it focuses on the use of data and algorithms in AI, and aims to improve the way AI models can autonomously learn and make decisions in real-world environments.
While the term is often used interchangeably with AI, machine learning is part of the wider AI umbrella, and requires minimal human intervention.
N is for Neural Network
A neural network (NN) is a machine learning model designed to mimic the structure and function of a human brain. An artificial neural network is comprised of multiple tiers and consists of units called artificial neurons, which loosely imitate neurons found in the brain.
Also referred to as deep neural networks, NN’s have a variety of useful applications and can be used to improve image recognition, predictive modeling, and natural language processing.
O is for Open-Source AI
Open-source AI refers to AI technology that has freely available source code. The ultimate aim of open-source AI is to create a culture of collaboration and transparency within the artificial intelligence community, that gives companies and developers greater freedom to innovate with the technology.
Lots of currently available open-source AI products are variations of existing applications., and common product categories include chatbots, machine translation tools, and large language models.
P is for Prompts
If you’re somehow still unfamiliar with tools like Gemini and ChatGPT, a prompt is an instruction or query you enter into chatbots to gain a targeted response. They can exist as stand-alone commands or can be the starting point for longer conversations with AI models.
AI prompts can take any form the user desires, but we found that longer form, detailed input generates the best responses. Using emotive language is another way to generate high-quality answers, according to a recent study by Microsoft.
Find out how to make your work life easier with these 40 ChatGPT prompts designed to save you time at the workplace.
P is for Parameters
In AI, parameters are a value that measures the behavior of a machine-learning model. In this context, each parameter acts as a variable, determining how the model will convert an input into output. Parameters are one of the most common ways to measure AI performance, and generally speaking, the more an AI model has, the better it will be able to understand complex data patterns and produce more accurate responses.
Q is for Quantum Artificial Intelligence
Quantum AI is the use of quantum computing for the computation of machine learning algorithms. Compared to classical computing, which processes information through 1s and 0s, quantum computing uses a unit called qubits, which represents both 1s and 0s at once. Theoretically, this process could speed up computing speeds dramatically.
In the case of quantum AI, the use of qubits could potentially help produce much more powerful AI models, although many experts believe we’re still a way off in achieving this reality.
R is for Red Teaming
Red teaming is a structured testing system that aims to find flaws and vulnerabilities in AI models. The cybersecurity term essentially refers to an ethical hacking practice where actors try and simulate an actual cyber attack, to identify potential weak spots in a system and to improve its defenses in the long run.
In the case of AI red teaming, no actual hacking attempt may take place, and red teamers may instead try to test the security of the system by prompting it in a certain way that bypasses any guardrails developers have placed on it, in a similar way to jailbreaking.
S is for Supervised Learning
There are two basic approaches when it comes to AI learning: supervised learning and unsupervised learning. Also known as supervised machine learning, supervised learning is a method of training where algorithms are trained on input data that has been labeled for a specific output. The aim of the test is to measure how accurately the algorithm can perform on unlabeled data, and the process strives to improve the overall accuracy of AI systems as a whole.
T is for Training Data
In simple terms, training data is an extremely vast input dataset used to train a machine learning model. Training data is used to teach prediction models using algorithms how to extract features that are relevant to specific user goals, and it’s the initial set of data that can then be complimented by subsequent data called testing sets.
It is fundamental to the way AI and machine learning work, and without training data, AI models wouldn’t be able to learn, extract useful information, and make predictions, or put simply, exist.
U is for Unsupervised Learning
Contrary to supervised learning, unsupervised learning is a type of machine learning where models are given unlabeled, cluttered data and encouraged to discover patterns and insights without any specific framework.
Unsupervised learning models are used for three main tasks, cluttering, which is a data mining technique for grouping unlabeled data, association, another earning method that uses different rules to find relationships between variables, and dimensionality reduction, a learning technique deployed when the number of dimensions in a dataset it too high.
X is for X-risk
X-risk stands for existential risk. More specifically, the term relates to the existential risk posed by the rapid development of AI. People warning about a potential X-risk event believe that the progress being made in the field of AI may result in human extinction or global catastrophe if left unchecked.
X-risk isn’t a fringe belief, though. In fact, in 2023 several tech leaders like Demis Hassabis CEO of DeepMind, Ilya Sutskever Co-Founder and Chief Scientist at OpenAI, and Bill Gates signed a letter warning AI developers about the existential threat posed by AI.
Z is for Zero-Shot Learning
Zero-shot learning is a deep learning problem setup where an AI model is tasked with completing a task without receiving any training examples. In machine learning, zero-shot learning is used to build models for classes that have not yet been labeled for training.
The two stages of zero-shot learning include the training stage, where knowledge is captured, and the interference stage, where information is used to classify examples into a new set of classes.
