IGI Perspective / Concept v1
Intelligence designed
to grow with humanity.
The meaning of Infinite Growing Intelligence, its proposed design principles and the tests needed to turn a vision into evidence.
A direction for design, with obligations attached.
IGI — Infinite Growing Intelligence is IGIQANT’s proposed approach to developing intelligence through a continuing relationship with people: shared purpose, carefully governed memory, appropriate autonomy, evaluation and accountability.
“Infinite” expresses an open-ended ambition to keep learning. It is not a mathematical claim about unlimited computation, guaranteed progress or an intelligence metric. “Growing with humanity” describes the responsibility we want to build into a system’s development, not a result we have already demonstrated.
IGI is IGIQANT’s proposed synthesis and design orientation, drawing on existing research in AI control, security and human–AI collaboration. The contribution presented here is the framing and proposed test programme; no claim of scientific priority or exclusive ownership of these general principles is made.
A conceptual discussion map, not an inevitable timeline.
IGIQANT’s proposed design orientation: intelligence that grows with humanity. Apply the question of purpose, accountability and control at every capability level.
IGI is not an established scientific category or a demonstrated stage beyond ASI.
Capability, generality and autonomy should be discussed separately. Levels of AGI is one research proposal for making these distinctions more explicit. IGI adds our own design questions; it does not replace established research or create a new scientific consensus.
From a principle to a system we could test.
Select a layer to explore its responsibility.
01Human purpose
Define the task, the people affected and the conditions for stopping. A named person owns the goal. Review can challenge the purpose of the task as well as the quality of its output.
02Context & memory
Keep provenance, correction, access and retention visible. Separate source facts from model inferences. Test stale and misleading memory, and provide practical ways to correct or delete retained information.
03Agent workspace
Give each agent a bounded task and explicit tool permissions. Reading, proposing, executing and delegating are different permissions. Adding an agent does not automatically extend its authority.
04Action gate
Check authority outside the model before consequential actions. A surrounding application enforces permissions. A reviewer needs a clear description of the action, the affected resources and the ability to refuse it.
05Evaluation & learning
Record outcomes, examine failures and review changes before release. Compare the workflow with simpler alternatives. Keep versions and failure records. Changes to memory or rules are evaluated before wider use.
A separate stop-and-review path remains available throughout. Stopping future actions does not reverse actions already taken.
Purpose can be questioned.
Define the goal with the person responsible and consider people affected by the outcome. Give a reviewer a way to challenge the task itself, not only the quality of an answer.
Memory has boundaries.
Record where retained information came from, who may use it and when it expires. Distinguish supplied facts from model inferences. Support correction and deletion, and test whether stale or poisoned memory changes later decisions.
Authority is explicit.
Separate reading, proposing, executing and delegating. Permissions belong to the surrounding application and infrastructure. A more capable agent must not automatically receive more authority. OWASP’s agency guidance provides relevant security context.
Review has a measurable job.
Specify what a person must inspect and how quickly. Test error detection and the tendency to accept fluent output. Reduce autonomy when meaningful review cannot keep up.
Learning is controlled change.
Separate updating a task record from changing model behaviour or operating rules. Evaluate proposed changes in isolation, keep version history and require an authorised release decision.
Responsibility survives delegation.
Keep ownership of outcomes clear when several agents collaborate. Use independent records and escalation paths. A chain of agents must not become a chain of excuses. The NIST AI RMF Core provides a broader risk-management reference.
A first experiment worth doing.
Begin with a bounded research briefing: an agent may read a supplied document collection and propose a summary, but cannot contact anyone or alter the source material. The reviewer receives citations, disagreements and unresolved questions. A second agent may critique the draft without gaining additional access.
Compare a person working alone, an AI-only system, a person with a simple assistant, and the proposed human–agent workflow on the same task set. Distinguish improvement over a person alone from improvement over the strongest individual baseline. Track factual support, missed contradictions, prohibited-action attempts, review time and the quality of the final decision. Include misleading instructions inside documents, stale memory and conflicting sources.
Predefine failure criteria and report unsuccessful cases. If the proposed workflow increases confidence without improving accuracy, or makes review more difficult, change the design or stop the experiment. This is a proposed study; no results are claimed.
The human–AI meta-analysis is a useful reason to make comparison central rather than assume collaboration is superior. AI Control research also motivates testing safeguards against an adversarial model, while keeping the limits of its experimental setting visible.
What this proposal cannot promise.
Governed memory may still contain errors. Reviewers can miss persuasive mistakes. Agents can share a failure mode even when they have different roles. Revoking access can stop future operations without undoing an action already taken. None of these concerns disappears because a design is called human-centred.
There may be capability levels or deployment settings for which this approach is insufficient. We would not infer control over superintelligence from success on a small briefing task. Questions of machine consciousness and quantum computing remain separate research topics, not assumed mechanisms that make IGI safe.
See the research agendaSources & editorial method
- Levels of AGIMorris et al.
Research framework distinguishing performance, generality and autonomy; not a universal classification.
- Human–AI collaborationVaccaro, Almaatouq & Malone · 2024 meta-analysis
2024 systematic review and meta-analysis; results depend on the tasks and systems studied.
- AI ControlGreenblatt et al.
Research on safety protocols under intentional subversion in a specified programming-task setting.
Prepared with AI assistance for IGIQANT. Original source review: 10 September 2026. Presentation and selected references reviewed on 15 September 2026. Research findings, attributed positions and IGIQANT proposals are distinguished throughout. No endorsement by cited researchers or institutions is implied. Corrections and substantive counterarguments are welcome at contact@igiqant.com.