Glossary#

Artificial General Intelligence#: Used to describe models that have broader cognitive capabilities, able to match or slightly surpass humans across a variety of tasks, from recommending restaurants and booking your flights to driving your car—all in one model.
Artificial Intelligence#: The simulation of human intelligence processes by machines, especially computer systems. It includes learning, reasoning, and self-correction.
Artificial Superintelligence#: A theoretical step further, where AI surpasses human intelligence and the top human minds in every field, able to even perform tasks and solve problems that would be impossible for humans.
Limited Memory#: As the name suggests, this next type of machine has the ability to evaluate past inputs and actions to learn and improve over time.
Machine Learning#: The subset of Artificial Intelligence focused on algorithms that can “learn” the patterns of training data and, subsequently, make accurate inferences about new data. This pattern recognition ability enables machine learning models to make decisions without explicit, hard-coded instructions.
Narrow AI#: This makes up most current AI models, where the system is designed to perform a single, specific task (such as coding, image generation, or writing) but can’t expand or apply their skills outside of what they’re programmed to do.
Reactive#: These systems respond to user inputs but have no memory to enable previous mistakes to inform future decisions. Because of this, a specific input will always have the same input.
Reinforced Learning#: A type of machine learning that involves training models to make decisions in environments where there are no clear consequences of their actions. In other words, the model is reinforced with feedback based on its performance so it learns which actions lead to the best outcomes.
Self-Aware#: The endgame of AI evolution in many sci-fi stories is a self-aware system that has devleoped consciousness and goes a step beyond understanding the emotions and feelings of others and would hypothetically understand its own existence and internal process.
Supervised Learning#: The machine is given a set of training data, including input data and the corresponding expected output. The machine then learns to map the input to the output using a function so it can produce the expected output for any new input.
Theory of Mind#: These would be able to understand that other entities ahve thoughts, emotions, desires, and intentions, and also how its own actions influence those. This would represent a huge step towards more personalized, human-like interactions with AI.
Unsupervised Learning#: The data isn’t labeled. The algorithm must find patterns in the data. This type of machine learning is used to find groups in data and to make predictions about new data points. It’s also used for tasks such as clustering and dimensionality reductions.