A robot closes a loop between perception, action, and consequence. Learn to audit grounding, feedback, transfer, sim-to-real, and recovery.
«Neural networks inspired by the human brain» appears in nearly every brochure in the industry. There is a real, documented borrowing — a 1980 paper states it in writing — and there is an analogy that breaks the moment you look at two things: the watt and the gradient. Telling «inspired by» from «works like» is a skill that survives every product rebrand.
A camera that announces whether a candidate is nervous or a student is paying attention does not read emotions: it measures facial movements and calls that an emotion. Behind the gap between those two sentences sit a 68-page scientific review, Microsoft's 2022 product withdrawal, and a European ban in force since 2 February 2025. It is enough to read any claim of this kind.
AI already supports fraud detection, analysis and financial operations, but that is not evidence of AGI. A guide to separating capability, autonomy and accountability.
Medical AGI remains hypothetical. This guide shows how to demand intended use, external validation, clinical utility, human factors, and monitoring.
Generality is not a sum of modules. How to measure transfer, composition, retention, and recovery in AI systems.
The ethics of artificial general intelligence becomes useful when it stops being a list of values and answers four testable questions: what the system can do, how much autonomy it gets, who is accountable, and how harm can be remedied.
Clustering, projections, and autoencoders pursue different goals. Learn how to choose them and test whether their representations work beyond training.
Common sense is not a single function. This guide shows how to test what knowledge, exceptions, and grounding an AI system actually demonstrates.
Talking about many tasks does not prove AGI. A method for separating performance, generality, and autonomy and auditing the evidence.
At the 2012 ImageNet competition, one system cut the classification error from 26.2% to 15.3% and the world decided machines could finally see. A year later, the same kind of network was switching categories because of image changes no human eye can detect. Between those two dates sits the only capability that matters here: knowing exactly what a computer-vision number measures before you believe it.
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