Definition
Machine learning (ML)
The branch of AI where computers learn patterns from data and improve at a task without being explicitly programmed with rules.
Learn the language of AI-native founders
Every week: the new terms, tools, and workflows founders are using, straight from the podcast.
Related terms in AI Fundamentals
Local AIAI processing that occurs on a user's device rather than in the cloud.Local modelA model running on your own hardware or private infrastructure, so the data never leaves the building.Long-Term Conversational MemoryA technical capability that allows an AI agent to retain context across multiple sessions over days, weeks, or months - referencing prior conversations, tracking commitments made, and building a longitudinal understanding of the user. Distinct from in-session context (which resets after a conversation ends), long-term memory enables genuinely cumulative relationships. In accountability products like Commitify, this capability is the core retention mechanism: the value increases with each call because the AI remembers what you said last time and can follow up on specific commitments.Loss functionThe formula that scores how wrong a model's predictions are. Training is the process of driving that score down.Markov chainA model of a sequence of events where the odds of what happens next depend only on the current state, not on how you got there.ML modelA custom machine learning model trained on your own data, as distinct from a general-purpose LLM. More expensive and data-hungry to build, but harder to replicate because the training data is proprietary.ModelIn AI, the trained system that takes an input and produces an output, like a prediction, classification, or generated text.Model agnosticNot tied to one underlying AI model — able to route the same task across many models depending on cost, speed, or capability.