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Research draft

artificial intelligence

vr.tr.artificial-intelligence · ACT.ACT

Let an agent describe AI neutrally by technique, application, capabilities, limits, risks and regulation, without hype.

Thing Registry Activities and processes

Research draft, second pass

A second pass drafted this model: the structure a model of this thing needs, and what is known about it in the world. The line under this one says how the second half was obtained - researched against sources, or recalled without web access, in which case nothing here was read anywhere and every claim is a lead to verify. Unreviewed either way.

written by Claude from model knowledge without web access - no source was read, every claim is a lead to verify

Researched by: Claude

Purpose and description

Let an agent describe AI neutrally by technique, application, capabilities, limits, risks and regulation, without hype.

The capability of computer systems to perform tasks associated with human intelligence, such as reasoning, learning, perception and language, and the field that studies and builds such systems, including machine learning and generative AI.

What it is for: Automating and assisting tasks in many domains.

It can be build and evaluate AI systems; apply AI in products and research; assess risks and comply with regulation; disclose AI-generated content.

Distinguishing features

Machine capability for intelligent tasks

Many techniques

Capabilities and limits vary widely

Regulated by new laws such as the EU AI Act

What it looks like

Software systems, models and the services built on them.

How it is recognised

Techniques such as machine learning or search

Applications such as chatbots or vision

Artificial general intelligence is a hypothetical goal

Related models

is a kind of - category

computer science and technology

includes - subfield

machine learning

uses - ingredients

algorithm and data

is regulated by - law

EU AI Act

In practice

Families and kinds

symbolic AI

machine learning and deep learning

generative AI

robotics and perception

artificial general intelligence (hypothetical)

Standards and regulation

EU AI Act

ISO/IEC 42001 AI management systems

NIST AI Risk Management Framework

OECD AI Principles

Failure modes and hazards

Errors and fabricated outputs

Bias and discrimination

Misuse and overreliance

Privacy harms

Also called

AITuberinfrastructure intelligenceHabsoraautomated music productiongenerative artificial intelligenceartificial general intelligenceintelligent systemretrieval-augmented generationText-to-Video AIsuperintelligencefriendly artificial intelligenceexpert systemmetaexpert systemopen-source artificial intelligenceEMYCINThe concept of knowledge graphdiagnostic expert systemrule-based expert systemframe-based expert systemmonitoring expert systemplanning expert systemVision and Languageautonomous navigationneuroroboticsapplications of artificial intelligencemultimodal artificial intelligenceneural fieldhierarchical temporal memoryartificial intelligence governancemachine pattern analysisartificial intelligence in educationmusic and artificial intelligenceimage-to-image translationmachine learning in bioinformaticsmachine unlearningactivity recognitionparticle swarm optimizationheuristic for determination of copyright status of a creatorPathwaysIntelligent Robotics

+142

Where this came from

wikidata · CC0 1.0

Source repository Registry entry point

Drafted structure

Bundle to layer to finding to question, as the second pass will find it: 4 bundles · 8 layers · 8 findings · 16 questions.

Technique How it works.

Techniques differ.

Method

ML, symbolic.

Method

Technique.

  1. Which AI technique does the system use? definition
  2. What data was it trained on? provenance

Capabilities

What it can do.

Capabilities

Capabilities.

  1. What can it do, as shown by which evaluations? provenance
  2. Where does it fail? boundary
Application Use.

Use decides risk.

Use case

Domain.

Use case

Application.

  1. What is the system used for? definition
  2. Is it a high-risk use under the law? boundary

Oversight

Human control.

Oversight

Human oversight.

  1. What human oversight is in place? boundary
  2. Can people contest its outputs? action
Risks Harms.

AI carries risks.

Bias

Fairness.

Bias

Fairness.

  1. Has it been tested for bias? provenance
  2. On which groups? definition

Reliability

Errors.

Reliability

Reliability.

  1. How often does it produce errors? measurement
  2. How are errors caught? action
Governance Law and disclosure.

AI is increasingly regulated.

Law

Obligations.

Law

Legal obligations.

  1. Which obligations apply under the AI Act or other law? provenance
  2. Who is the provider and who the deployer? provenance

Disclosure

Transparency.

Disclosure

Transparency.

  1. Is AI-generated content labelled? boundary
  2. Are users told they interact with AI? boundary

What the second pass must settle

  • Should AI techniques be separate entries?
  • How should systems be linked as instances?
  • How should capability claims be verified?
Draft as JSON