iqtalike

iqtalike is a term used in artificial intelligence discourse to describe a family of benchmarking approaches intended to evaluate cognitive-like abilities of AI systems across tasks that approximate human intelligence. It is not a single test or standard, but a concept for comparing models on domains such as reasoning, learning speed, planning, and adaptation.

The phrase emerged in online discussions and some scholarly work in the early 2020s. The name combines the notion of IQ with 'like,' signaling an attempt to discuss general cognitive performance without endorsing traditional IQ tests. In practice, iqtalike frameworks vary in their scope and scoring, and there is no universally adopted protocol.

Typical iqtalike frameworks assemble a suite of tasks drawn from multiple domains, including abstract reasoning, symbolic manipulation, language understanding, and problem solving. Tasks can be static or procedurally generated to test generalization. Scoring schemes may blend accuracy, efficiency, and robustness, with results aggregated to yield an overall cognitive-score proxy. Some proposals emphasize adaptive task selection to challenge models at the edge of current capabilities.

Supporters argue that iqtalike offers a more holistic view of AI cognition than narrow benchmarks. Critics caution against anthropomorphism, cultural or task bias, and overinterpretation of scores as true intelligence. As a concept, iqtalike remains informal and is used mainly in theoretical discussions or experimental toolkits rather than as a widely adopted standard.