So You Have Heard These AI Terms and Nodded Along Let Us Fix That

Artificial intelligence is changing the world and simultaneously inventing a whole new language to describe how it is doing it Spend five minutes reading about AI and you will run into LLMs RAG RLHF and a dozen other terms that can make even very smart people in the tech world feel insecure This glossary is our attempt to fix that We update it regularly as the field evolves so consider it a living document much like the AI systems it describes AGI Artificial general intelligence or AGI is a nebulous term But it generally refers to AI that is more capable than the average human at many if not most tasks OpenAI CEO Sam Altman once described AGI as the equivalent of a median human that you could hire as a co worker Meanwhile OpenAI’s charter defines AGI as highly autonomous systems that outperform humans at most economically valuable work Google DeepMind’s understanding differs slightly from these two definitions the lab views AGI as AI that is at least as capable as humans at most cognitive tasks Confused Not to worry so are experts at the forefront of AI research AI agent An AI agent refers to a tool that uses AI technologies to perform a series of tasks on your behalf beyond what a more basic AI chatbot could do such as filing expenses booking tickets or a table at a restaurant or even writing and maintaining code However as we have explained before there are lots of moving pieces in this emergent space so AI agent might mean different things to different people Infrastructure is also still being built out to deliver on its envisaged capabilities But the basic concept implies an autonomous system that may draw on multiple AI systems to carry out multistep tasks API endpoints Think of API endpoints as buttons on the back of a piece of software that other programs can press to make it do things Developers use these interfaces to build integrations for example allowing one application to pull data from another or enabling an AI agent to control third party services directly without a human manually operating each interface Most smart home devices and connected platforms have these hidden buttons available even if ordinary users never see or interact with them As AI agents grow more capable they are increasingly able to find and use these endpoints on their own opening up powerful and sometimes unexpected possibilities for automation Chain of thought Given a simple question a human brain can answer without even thinking too much about it things like which animal is taller a giraffe or a cat But in many cases you often need a pen and paper to come up with the right answer because there are intermediary steps For instance if a farmer has chickens and cows and together they have 40 heads and 120 legs you might need to write down a simple equation to come up with the answer 20 chickens and 20 cows In an AI context chain of thought reasoning for large language models means breaking down a problem into smaller intermediate steps to improve the quality of the end result It usually takes longer to get an answer but the answer is more likely to be correct especially in a logic or coding context Reasoning models are developed from traditional large language models and optimized for chain of thought thinking thanks to reinforcement learning Coding agents This is a more specific concept than an AI agent which means a program that can take actions on its own step by step to complete a goal A coding agent is a specialized version applied to software development Rather than simply suggesting code for a human to review and paste in a coding agent can write test and debug code autonomously handling the kind of iterative trial and error work that typically consumes a developer’s day These agents can operate across entire codebases spotting bugs running tests and pushing fixes with minimal human oversight Think of it like hiring a very fast intern who never sleeps and never loses focus though as with any intern a human still needs to review the work Compute Although somewhat of a multivalent term compute generally refers to the vital computational power that allows AI models to operate This type of processing fuels the AI industry giving it the ability to train and deploy its powerful models The term is often a shorthand for the kinds of hardware that provides the computational power things like GPUs CPUs TPUs and other forms of infrastructure that form the bedrock of the modern AI industry Deep learning A subset of self improving machine learning in which AI algorithms are designed with a multi layered artificial neural network structure This allows them to make more complex correlations compared to simpler machine learning based systems such as linear models or decision trees The structure of deep learning algorithms draws inspiration from the interconnected pathways of neurons in the human brain Deep learning AI models are able to identify important characteristics in data themselves rather than requiring human engineers to define these features The structure also supports algorithms that can learn from errors and through a process of repetition and adjustment improve their own outputs However deep learning systems require a lot of data points to yield good results millions or more They also typically take longer to train compared to simpler machine learning algorithms so development costs tend to be higher Diffusion Diffusion is the tech at the heart of many art music and text generating AI models Inspired by physics diffusion systems slowly destroy the structure of data for example photos songs and so on by adding noise until there is nothing left In physics diffusion is spontaneous and irreversible sugar diffused in coffee cannot be restored to cube form But diffusion systems in AI aim to learn a sort of reverse diffusion process to restore the destroyed data gaining the ability to recover the data from noise Distillation Distillation is a technique used to extract knowledge from a large AI model with a teacher student model Developers send requests to a teacher model and record the outputs Answers are sometimes compared with a dataset to see how accurate they are These outputs are then used to train the student model which is trained to approximate the teacher’s behavior Distillation can be used to create a smaller more efficient model based on a larger model with a minimal distillation loss This is likely how OpenAI developed GPT 4 Turbo a faster version of GPT 4 While all AI companies use distillation internally it may have also been used by some AI companies to catch up with frontier models Distillation from a competitor usually violates the terms of service of AI API and chat assistants Fine tuning This refers to the further training of an AI model to optimize performance for a more specific task or area than was previously a focal point of its training typically by feeding in new specialized data Many AI startups are taking large language models as a starting point to build a commercial product but are vying to amp up utility for a target sector or task by supplementing earlier training cycles with fine tuning based on their own domain specific knowledge and expertise GAN A GAN or Generative Adversarial Network is a type of machine learning framework that underpins some important developments in generative AI when it comes to producing realistic data including but not only deepfake tools GANs involve the use of a pair of neural networks one of which draws on its training data to generate an output that is passed to the other model to evaluate The two models are essentially programmed to try to outdo each other The generator is trying to get its output past the discriminator while the discriminator is working to spot artificially generated data This structured contest can optimize AI outputs to be more realistic without the need for additional human intervention Though GANs work best for narrower applications such as producing realistic photos or videos rather than general purpose AI Hallucination Hallucination is the AI industry’s preferred term for AI models making stuff up literally generating information that is incorrect Obviously it is a huge problem for AI quality Hallucinations produce GenAI outputs that can be misleading and could even lead to real life risks with potentially dangerous consequences think of a health query that returns harmful medical advice The problem of AIs fabricating information is thought to arise as a consequence of gaps in training data Hallucinations are contributing to a push toward increasingly specialized and or vertical AI models domain specific AIs that require narrower expertise as a way to reduce the likelihood of knowledge gaps and shrink disinformation risks Inference Inference is the process of running an AI model It is setting a model loose to make predictions or draw conclusions from previously seen data To be clear inference cannot happen without training a model must learn patterns in a set of data before it can effectively extrapolate from this training data Many types of hardware can perform inference ranging from smartphone processors to beefy GPUs to custom designed AI accelerators But not all of them can run models equally well Very large models would take ages to make predictions on say a laptop versus a cloud server with high end AI chips Large language model LLM Large language models or LLMs are the AI models used by popular AI assistants such as ChatGPT Claude Google’s Gemini Meta’s AI Llama Microsoft Copilot or Mistral’s Le Chat When you chat with an AI assistant you interact with a large language model that processes your request directly or with the help of different available tools such as web browsing or code interpreters LLMs are deep neural networks made of billions of numerical parameters or weights that learn the relationships between words and phrases and create a representation of language a sort of multidimensional map of words These models are created from encoding the patterns they find in billions of books articles and transcripts When you prompt an LLM the model generates the most likely pattern that fits the prompt Memory cache Memory cache refers to an important process that boosts inference which is the process by which AI works to generate a response to a user’s query In essence caching is an optimization technique designed to make inference more efficient AI is obviously driven by high octane mathematical calculations and every time those calculations are made they use up more power Caching is designed to cut down on the number of calculations a model might have to run by saving particular calculations for future user queries and operations There are different kinds of memory caching although one of the more well known is KV or key value caching KV caching works in transformer based models and increases efficiency driving faster results by reducing the amount of time and algorithmic labor it takes to generate answers to user questions Neural network A neural network refers to the multi layered algorithmic structure that underpins deep learning and more broadly the whole boom in generative AI tools following the emergence of large language models Although the idea of taking inspiration from the densely interconnected pathways of the human brain as a design structure for data processing algorithms dates all the way back to the 1940s it was the much more recent rise of graphical processing hardware GPUs via the video game industry that really unlocked the power of this theory These chips proved well suited to training algorithms with many more layers than was possible in earlier epochs enabling neural network based AI systems to achieve far better performance across many domains including voice recognition autonomous navigation and drug discovery Open source Open source refers to software or increasingly AI models where the underlying code is made publicly available for anyone to use inspect or modify In the AI world Meta’s Llama family of models is a prominent example Linux is the famous historical parallel in operating systems Open source approaches allow researchers developers and companies around the world to build on top of one another’s work accelerating progress and enabling independent safety audits that closed systems cannot easily provide Closed source means the code is private you can use the product but not see how it works as is the case with OpenAI’s GPT models a distinction that has become one of the defining debates in the AI industry Parallelization Parallelization means doing many things at the same time instead of one after another like having 10 employees working on different parts of a project at the same time instead of one employee doing everything sequentially In AI parallelization is fundamental to both training and inference modern GPUs are specifically designed to perform thousands of calculations in parallel which is a big reason why they became the hardware backbone of the industry As AI systems grow more complex and models grow larger the ability to parallelize work across many chips and many machines has become one of the most important factors in determining how quickly and cost effectively models can be built and deployed Research into better parallelization strategies is now a field of study in its own right RAMageddon RAMageddon is the fun new term for a not so fun trend that is sweeping the tech industry an ever increasing shortage of random access memory or RAM chips which power pretty much all the tech products we use in our daily lives As the AI industry has blossomed the biggest tech companies and AI labs all vying to have the most powerful and efficient AI are buying so much RAM to power their data centers that there is not much left for the rest of us And that supply bottleneck means that what is left is getting more and more expensive That includes industries like gaming where major companies have had to raise prices on consoles because it is harder to find memory chips for their devices consumer electronics where memory shortage could cause the biggest dip in smartphone shipments in more than a decade and general enterprise computing because those companies cannot get enough RAM for their own data centers The surge in prices is only expected to stop after the dreaded shortage ends but unfortunately there is not really much of a sign that is going to happen anytime soon Reinforcement learning Reinforcement learning is a way of training AI where a system learns by trying things and receiving rewards for correct answers like training your beloved pet with treats except the pet in this scenario is a neural network and the treat is a mathematical signal indicating success Unlike supervised learning where a model is trained on a fixed dataset of labeled examples reinforcement learning lets a model explore its environment take actions and continuously update its behavior based on the feedback it receives This approach has proven especially powerful for training AI to play games control robots and more recently sharpen the reasoning ability of large language models Techniques like reinforcement learning from human feedback or RLHF are now central to how leading AI labs fine tune their models to be more helpful accurate and safe Token When it comes to human machine communication there are some obvious challenges people communicate using human language while AI programs execute tasks through complex algorithmic processes informed by data Tokens bridge that gap they are the basic building blocks of human AI communication representing discrete segments of data that have been processed or produced by an LLM They are created through a process called tokenization which breaks down raw text into bite sized units a language model can digest similar to how a compiler translates human language into binary code a computer can understand In enterprise settings tokens also determine cost most AI companies charge for LLM usage on a per token basis meaning the more a business uses the more it pays Token throughput So again tokens are the small chunks of text often parts of words rather than whole ones that AI language models break language into before processing it they are roughly analogous to words for the purposes of understanding AI workloads Throughput refers to how much can be processed in a given period of time so token throughput is essentially a measure of how much AI work a system can handle at once High token throughput is a key goal for AI infrastructure teams since it determines how many users a model can serve simultaneously and how quickly each of them receives a response AI researcher Andrej Karpathy has described feeling anxious when his AI subscriptions sit idle echoing the feeling he had as a grad student when expensive computer hardware was not being fully utilized a sentiment that captures why maximizing token throughput has become something of an obsession in the field Training Developing machine learning AIs involves a process known as training In simple terms this refers to data being fed in in order that the model can learn from patterns and generate useful outputs Essentially it is the process of the system responding to characteristics in the data that enables it to adapt outputs towards a sought for goal whether that is identifying images of cats or producing a haiku on demand Training can be expensive because it requires lots of inputs and the volumes required have been trending upwards which is why hybrid approaches such as fine tuning a rules based AI with targeted data can help manage costs without starting entirely from scratch Transfer learning A technique where a previously trained AI model is used as the starting point for developing a new model for a different but typically related task allowing knowledge gained in previous training cycles to be reapplied Transfer learning can drive efficiency savings by shortcutting model development It can also be useful when data for the task that the model is being developed for is somewhat limited But it is important to note that the approach has limitations Models that rely on transfer learning to gain generalized capabilities will likely require training on additional data in order to perform well in their domain of focus Weights Weights are core to AI training as they determine how much importance or weight is given to different features or input variables in the data used for training the system thereby shaping the AI model’s output Put another way weights are numerical parameters that define what is most salient in a dataset for the given training task They achieve their function by applying multiplication to inputs Model training typically begins with weights that are randomly assigned but as the process unfolds the weights adjust as the model seeks to arrive at an output that more closely matches the target For example an AI model for predicting housing prices that is trained on historical real estate data for a target location could include weights for features such as the number of bedrooms and bathrooms whether a property is detached or semi detached whether it has parking a garage and so on Ultimately the weights the model attaches to each of these inputs reflect how much they influence the value of a property based on the given dataset Validation loss Validation loss is a number that tells you how well an AI model is learning during training and lower is better Researchers track it closely as a kind of real time report card using it to decide when to stop training when to adjust hyperparameters or whether to investigate a potential problem One of the key concerns it helps flag is overfitting a condition in which a model memorizes its training data rather than truly learning patterns it can generalize to new situations Think of it as the difference between a student who genuinely understands the material and one who simply memorized last year’s exam validation loss helps reveal which one your model is becoming This article is updated regularly with new information Have a favorite AI term we missed Let me know in the comments