AI Glossary: Understanding GPT, neural networks, and more

With new advancements and applications appearing every week, AI is continually growing and improving, and it sometimes feels like the amount of jargon to keep up with is growing at a similar rate.


We’ve put together a glossary of terms and concepts to help you better grasp the brave new world of artificial intelligence because, all in all, it can be a little intimidating.


simulated intelligence (AI)


The development of intelligent machines that can carry out complicated activities that generally require human-level ability, such as speech recognition, decision-making, and language translation, is referred to as artificial intelligence (AI). AI systems can be trained to learn and get better over time, which enables them to finish more difficult jobs more quickly and accurately.


In-depth learning

Deep learning techniques involve training neural networks with multiple layers, each of which represents a distinct degree of abstraction. Large datasets are typically used to train these deep networks to make predictions or judgments regarding data.


A neural network with a single layer could be able to create approximations of predictions, but adding more layers, each of which builds on the one before it to optimize and refine the predictions, might assist in increasing accuracy.


Deep learning algorithms have significantly advanced a variety of applications, including natural language processing, speech recognition, image recognition systems that include facial recognition, self-driving cars, etc. Deep learning algorithms are highly effective at processing complex and unstructured data, such as images, audio, and text.



In the context of natural language processing (NLP), an embedding is a method for converting a collection of variable-length text into a set of fixed-length numbers. For example, the set of numbers for “dog” and “animal” will be near together in a mathematical sense. Usually, this collection of numbers will keep semantic value in some way. NLP algorithms can process text more effectively as a result.


networks for encoders and decoders

These particular deep neural network architectures operate as both encoders and decoders, taking a given input—in this case, text—and turning it into a numerical representation, such as a fixed length collection of numbers.


They are frequently employed in jobs involving natural language processing, including machine translation.



the procedure of using a fresh dataset to train a previously trained model so that it can perform a particular task. In order for the model to learn to recognize more complex patterns in the data specific to the task, it is first trained on a large, generic dataset before being applied to a smaller, more task-related dataset.


By employing general models rather than creating new ones from scratch, fine-tuning can save time and resources and lower the danger of overfitting, which occurs when a model has learned the features of a relatively small training set exceedingly well but is unable to generalize to other data.


GANs, or generative adversarial networks

a group of artificial intelligence (AI) techniques used in unsupervised machine learning and using two neural networks in competition. A discriminator model, which attempts to categorize instances as either actual data or fake (created data), and a generator model, which is taught to create fresh examples of plausible data, make up GANs. The discriminator subsequently becomes less adept at distinguishing between real and false data and begins to label bogus data as real as the two models fight against one another.


Generative artificial intelligence (AI) is a subset of AI that can produce a wide range of content, such as text, photos, videos, and computer code, by seeing patterns in vast amounts of training data and producing creative outputs that resemble the training data. Generic AI algorithms use deep learning models to provide innovative outputs that are not explicitly programmed or predefined, in contrast to other forms of AI that are based on rules.


Generative AI is a great tool for a variety of applications, including image and video generation, natural language processing, and music composition, because it can create highly realistic and sophisticated content that mimics human ingenuity. Recent innovations like ChatGPT for text and DALL-E and Midjourney for photos are two examples.


Transformer with generative pre-training (GPT)

A series of neural network models called generative pre-trained transformers, or GPTs, are trained with hundreds of billions of parameters on enormous datasets to produce text that resembles human speech. They are based on the Google researchers’ transformer architecture, which enables the models to more effectively comprehend and apply the context in which words and expressions are used. It also enables the models to selectively attend to different parts of the input, focusing on pertinent words or phrases that they perceive as being more important to the outcome. They are able to produce longer responses than merely the subsequent word in a string.


The GPT family of models is regarded as the most substantial and intricate language model to date. They are highly suited for products like chatbots and virtual assistants because they are generally used to respond to queries, summarize text, generate code, have conversations, tell stories, and perform many other natural language processing tasks.


Using GPT-3.5 as its foundation, OpenAI published ChatGPT in November 2022. It quickly gained popularity, with people lining up to test it out. And the buzz is true: more recent developments in GPT have even made the technology transformative rather than just practical for corporate settings like customer support.



A regrettable but common occurrence in big language models, when the AI system gives a convincing-looking response that is factually erroneous, inaccurate, or incomprehensible due to constraints in its training data and architecture.


If a model is asked a true question about something it hasn’t been trained on, it frequently makes up an answer rather than responding with “I don’t know.” We should constantly keep the issue of hallucinations in mind while assessing the performance of any large language model (LLM), as it is an active topic of research.


Large-scale language model

A certain class of neural network called an LLM is able to produce natural language writing that is comparable to prose authored by humans. These models utilize deep learning to comprehend the intricate patterns and interactions between words to produce or forecast new material. Typically, they are trained on enormous datasets of hundreds of billions of words from books, papers, web pages, etc.


LLMs take into account huge portions of text to better comprehend the context, in contrast to classic NLP algorithms that often just look at the immediate context of words. LLMs can take a variety of forms, including designs like OpenAI’s GPT.


LLM agents (such as AutoGPT and LangChain) on their own take text as input and output more text. Systems that are constructed on top of LLMs are called agents because they have the ability to plan tasks independently, make decisions on their own, and act autonomously. LLMs are used by agents to translate high level language commands into the precise actions or programming necessary to carry them out.


Agent-related research and development are currently exploding. Applications like “task list doers,” which accept a task list as input and attempt to complete the chores for you, are made possible by tools like AutoGPT.


Machine learning (ML) is a branch of artificial intelligence that deals with the creation of mathematical formulas and statistical models that allow computers to gradually improve their performance at a given activity without being explicitly taught to do so. In other words, as it processes more data, the machine “learns” from the data and gets better at making predictions or carrying out certain tasks.


Supervised learning, unsupervised learning, and reinforcement learning are the three primary categories of machine learning.


A machine learning technique called supervised learning makes use of labeled datasets in order to teach algorithms the proper way to classify data or anticipate outcomes. For instance, a model can forecast fresh, unlabeled images of cats and dogs if you provide it a batch of annotated images of those animals;

Unsupervised learning, which has little to no human supervision and no pre-existing classifications, searches for undetected patterns in a dataset;

Through reinforcement learning, a model is taught to make choices depending on input from its surroundings. It gains the ability to act in ways that maximize reward signals, like succeeding in a game or finishing a task.

The study of how human language and computers interact is known as natural language processing, or NLP. It blends statistical, machine learning, and deep learning models—typically trained using a lot of data—with rule-based human language modeling to give computers the ability to analyze, comprehend, and produce human language.


Its applications are made to analyze, comprehend, and produce text and speech in human language. Language translation, sentiment analysis, speech recognition, text categorization, named entity recognition, and text summarization are a few typical NLP activities.


neural systems

Warren McCullough and Walter Pitts of Chicago developed the machine learning subfield of neural networks, which is based on the architecture of the human brain. It is made up of layers of interconnected nodes, also known as neurons, that process and analyze data in order to come to conclusions or make predictions. Each layer takes input from nodes in the layer below it and generates outputs that are then supplied to nodes in the layer above. The results are then output in the final layer.


Predictive modeling, natural language processing, image and speech recognition, and other fields have all made use of them.


immediate engineering

A prompt, which can be as simple as a question, is a series of directives you give to an LLM as input in order for it to produce useful outputs. The ability to design efficient prompts that will result in the best output for any given work is known as prompt engineering. Some would even call it art. It necessitates knowledge of the operation of large language models (LLMs), the data used for training them, as well as their advantages and disadvantages.


Learning by reinforcement using feedback from people (RLHF)

Using explicit human input to train a reinforcement learning system’s reward model is known as RLHF. In the context of an LLM, this may be done by having humans score the outputs of the LLM and select the responses they find most appealing. This information is then used to train a reward model, a neural network that can determine if a given response will be appealing to people. The LMM is then adjusted using the reward model to provide output that is more in line with consumer desires.


These methods are believed to be a crucial development step for LLMs like ChatGPT, which have experienced revolutionary improvements in their functionalities.



A transformer is a specific kind of deep neural network design that consists of a number of encoder and decoder units connected in order to handle sequential data, including time series and natural language.

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