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The Bard's Lexicon: A Shakespearean Guide to the Mysteries of AI and Data Science

Accountability - The burden of answerability doth rest upon the keepers of AI's machinations.

Artificial Intelligence (AI) - The arcane craft that bestows upon lifeless metal the mimicry of man's own wit.

AI Governance - The codex, fair decrees, and virtuous counsel that steer the course of AI's making and employ.

Algorithm - A scripted dance, a sequence of steps, that leads yon computer to resolve its quests.

Algorithmic Bias - A shadow cast by AI, where outcomes bear the taint of partiality, sprung from flawed lore or calculus.

Algorithmic Fairness - The noble quest to render AI as just, shunning partiality based on lineage, sex, or years.

Automation - The alchemy that grants machines the skill to act, sans human hand to guide them.

Autonomy - The sovereign will granted unto AI, empowering choices made without man's yoke.

Bias - A skew, a tilt, where outcomes favour some, through fault in data or in guiding rule.

Bias Mitigation - The artful schemes to quash the slant, and steer AI towards the righteous path.

Big Data - A vast ocean of numbers, facts, and tales, that begs the skill of wizards for its reading.

Chatbot - A conjured sprite within the screen, with whom one trades in words as if it breathes.

Cloud Computing - The summoning of distant powers, bestowed via ether, to perform one's computational needs.

Collaboration - The fellowship of minds and hearts, in pursuit of common quests and newfound lore.

Computer Vision - The sight granted unto machines, to read the world in pixels and in frames.

Consent - The willing nod of those from whom we glean the data, ere their secrets feed our spells.

Dashboard - A tapestry where numbers and portents do display, to guide our gaze and chart our course.

Data - The humble bricks and mortar whence we craft our towering inferences.

Data Cleansing - The purging rites that scrub our data clean of lies and inconsistencies.

Data Protection - The sacred duty to shield each soul's numeric shadow from misuse or harm.

Data Science - The alchemy that turns raw data into nuggets of wisdom and insight.

Data Visualization - The art that paints our data in such forms as charts and graphs, for easier reading of its tales.

Dataiku - A guild of craftsmen offering tools to make sense of numbers and to forge AI's wonders.

Deep Learning - The study of intricate webs, akin to minds, to teach machines to grapple with vast complexity.

Deployment - The act of releasing one's crafted model into the world, to labour or to serve.

Ethical AI - The pious crafting of AI that honours virtue and the dignity of man.

Ethics - The moral compass that guides our hand when we wield AI or other learned crafts.

Explainability - The grace of clarity that lets us see within the blackened box of AI's thought.

Fairness - The just and equal treatment that AI must bestow on every soul it touches.

Feature - A single facet, a strand of data, that feeds the hunger of our learning models.

Governance Framework - The scaffold and the blueprint that guide our making and our use of AI.

Human Rights - The inalienable dignities that even AI must honour, as do all of man's creations.

Human-Centered Design - The crafting of AI with human wants and ethics as its lodestar.

Insights - The gems of knowledge plucked from the raw rubble of our data mines.

Internet of Things (IoT) - A web of chattels, each a tiny brain, that speak in whispers across the ether.

Machine Learning (ML) - A branch of AI where machines are taught to glean their wisdom from the data.

Model - A simulacrum in equations, trained to predict or categorize or serve.

Natural Language Processing (NLP) - The magic that lets machines comprehend and parley in the tongue of men.

Neural Network - A mimic of the brain, a web of nodes, that seeks patterns in the data's sprawl.

Prediction - The oracle within the machine that reads the present signs to speak of future days.

Predictive Analytics - The art of divination through numbers, using pasts to whisper futures.

Privacy - The sacrosanct defence of every man's and woman's digital solitude.

Public Engagement - The invitation to all folk to join the discourse on AI's role and rule.

Regulation - The laws and statutes penned to bound and guide the reach of AI's long arm.

Reinforcement Learning - The teaching of a model through trial, error, and the sweet or bitter taste of outcomes.

Responsible AI - The creed that binds us to create AI that is just, and fair, and good.

Robotics - The craft that breathes life into metal forms, for tasks both great and small.

Robustness - The sturdiness of AI, its grace in facing change or challenge without a falter.

SQL (Structured Query Language) - The tongue in which we speak to databases, to ask them of their secrets.

Supervised Learning - The training of a model with a shepherd's hand to guide its early steps.

Training Data - The primer and the textbook for our fledgeling models, full of questions and of answers.

Transparency - The shining light that makes opaque AI as clear as crystal glass.

Unsupervised Learning - The schooling of a model left to find its own way through the data's wilds.

Workflow - The journey of tasks, each following the last, within the Halls of Data.


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