Understanding the AI Landscape

Artificial intelligence is transforming the world, and it’s creating a new language to describe its impact. If you’ve ever attended a product meeting, pitch, or panel, you’ve likely heard terms like LLMs, RAG, RLHF, and many others that can make even the most tech-savvy individuals feel uncertain. This glossary aims to bridge that gap by providing clear, pain-free definitions of the most common AI terms you’ll encounter, whether you’re building with AI, investing in it, or simply trying to stay up-to-date with the latest developments in the field.

Artificial General Intelligence (AGI)

AGI is a term that’s often used but rarely defined. However, it generally refers to AI systems that are more capable than humans at a wide range of tasks. OpenAI CEO Sam Altman has described AGI as the equivalent of a median human that you could hire as a coworker. Meanwhile, OpenAI’s charter defines AGI as highly autonomous systems that outperform humans at most economically valuable work. Google DeepMind’s understanding differs slightly, viewing AGI as AI that’s at least as capable as humans at most cognitive tasks. Despite these definitions, even experts in the field are often confused by the term.

AI Agents

An AI agent is a tool that uses AI technologies to perform a series of tasks on your behalf, going beyond what a basic AI chatbot could do. This might include tasks like filing expenses, booking tickets, or even writing and maintaining code. However, the concept of an AI agent is still evolving, and the term might mean different things to different people. As the field continues to develop, we can expect to see more sophisticated AI agents that are capable of carrying out complex tasks.

API Endpoints

API endpoints can be thought of as the hidden buttons on the back of a software application that other programs can press to make it do things. Developers use these interfaces to build integrations, such as allowing one application to pull data from another or enabling an AI agent to control third-party services directly without human intervention. As AI agents become more capable, they’re increasingly able to find and use these endpoints on their own, opening up new possibilities for automation.

Chain of Thought

Chain-of-thought reasoning is a concept that’s essential to understanding how AI models work. In simple terms, it’s the process of breaking down a problem into smaller, intermediate steps to improve the quality of the end result. This approach is particularly useful in logic and coding contexts, where it can lead to more accurate and reliable outcomes. By developing models that can perform chain-of-thought reasoning, we can create AI systems that are more capable and effective.

Coding Agents

Coding agents are a specific type of AI agent that’s designed to perform software development tasks autonomously. Rather than simply suggesting code for a human to review and paste in, a coding agent can write, test, and debug code on its own, 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.

Compute

Compute 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 powerful models. The term is often used as a shorthand for the kinds of hardware that provide the computational power, such as GPUs, CPUs, TPUs, and other forms of infrastructure that form the bedrock of the modern AI industry.

Deep Learning

Deep learning is a subset of self-improving machine learning in which AI algorithms are designed with a multi-layered, artificial neural network (ANN) 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. By identifying important characteristics in data itself, deep learning AI models can improve their own outputs through a process of repetition and adjustment.

Diffusion

Diffusion is the technology at the heart of many art-, music-, and text-generating AI models. Inspired by physics, diffusion systems slowly destroy the structure of data by adding noise until there’s nothing left. In physics, diffusion is spontaneous and irreversible – sugar diffused in coffee can’t be restored to its original form. However, 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.

Fine-Tuning

Fine-tuning 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.

GANs

GANs, or Generative Adversarial Networks, are 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 networks that compete with each other to generate new data that’s indistinguishable from real data. This process allows GANs to learn complex patterns and relationships in data, making them a powerful tool for a wide range of applications.