Common AI Definitions

Why do we bother with definitions? Because we need to make policies.
To make policy, we have to be able to describe the systems that we’re using to make policy

  • Affective Data

  • Artificial Intelligence - no straightforward, consensus definition of artificial
    intelligence. AI is best understood as a set of techniques aimed at
    approximating some aspect of human cognition using
    machines.

  • AI technologies

    • Natural language processing (NLP)- interpret texts, including semantic analysis (as used in legal services and translation), and generate texts (as in auto-journalism).

    • Speech recognition - use of AI for spoken words, including smartphones, AI personal assistants, and conversational bots in banking services

    • .„Image recognition - use of AI for facial recognition

    • Autonomous agents- use of AI in computer game avatars, malicious software bots, virtual companions, smart robots, and autonomous warfare.„

    • Affect detection- use of AI to analyses sentiment in text, behaviour and faces.„

    • Data mining for prediction - use of AI for medical diagnoses, weather forecasting, business projections, smart cities, financial predictions, and fraud detection.„

    • Automated Decisionmaking

  • Biometrics - Types of Biometrics

  • Emotions are mainly expressed by verbal or non-verbal communications. Verbal communications include spoken and written.

  • Emotional Intelligence - ability to identify, assess, and manage the personal emotions of oneself and others

  • Ethics and Governance of Artificial Intelligence

  • Ethic Vectors - to evaluate the ethics of an AI appl, you need the following measurements : fairness, privacy, reproducability, correctness and performance

  • HarmT- development of artificial intelligence can cause harm to society and the public; existing dangers should not be aggravated, nor new dangers caused, through the abuse of artificial intelligence.

  • ICANN - Internet Corporation
    for Assigned Names and Numbers

  • IETF 0 Internet Engineering Task Force

  • Fairness (and Justice)

    avoid bias or discrimination against specific groups or individuals, and avoid placing disadvantaged people in an even more unfavourable position

  • Interoperability is the ability of an organization to interact towards mutually beneficial goals, involving the sharing of information and knowledge

  • MLOps (Machine Learning Operations)

    It is an engineering discipline that aims to unify ML systems development(dev) and ML systems deployment(ops) to standardize and streamline the continuous delivery of high-performing models in production

  • Model registry

    is a repository for storing and tracking the versions of models, analogous to a version control system like Git, but for MK,

  • Stakeholder
    individual or organization having a right, share, claim, or interest in a system
    Eg. Data Subjust, makers (developers, designer) Auditors(Policy makers)

  • List of acronyms and abbreviations

  • AI : Artificial Intelligence

  • GDPR General Data Protection Regulation

  • GOFAI Good-Old-Fashioned

  • ML Machine Learning

  • NLP Natural Language Processing


    Reference

  • SEVOCAB: Software and Systems Engineering Vocabulary