WHERE IT GETS INTERESTING

Look closer at how your models reason, and at what certification actually requires.

Seedling tray with model predictions boxed and labelled weed or crop
Seedling tray with ground-truth boxes labelled weed or crop

Featured Case Study

Building a Squinting Pipeline
for Weed/Crop Detection with Squint Vision Studio

How embedding-space monitoring can make embedded computer vision systems more reliable, efficient, and production-ready

Ground Truth

Predictions

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Annotated cutaway line drawing of a jet

Featured Case Study

Neuro-symbolic reasoning
and why this makes AeroCert fundamentally different from generative AI tools

Repeatability:

run the process again on the same inputs and the same artifact.

Origin:

every artifact traces back to the requirement, function, and code that produced it.

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A butterfly-shaped cloud of coloured particles

Research paper

Cognitive Information Theory

Structural Limits of Objective-Function AI through Irreducible Entropy, Causal Ambiguity, and Sequential Distribution Drift

How do you audit a decision that deletes its own evidence?

The record here builds month after month with the same terrain missing from it.

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Topics

CASE STUDIES
INSIGHTS
RESEARCH PAPERS
Why an automated jet engine inspection model misses the cracks it most needs to catchWhen a design search pushes a surrogate model past what it learnedBuilding a hybrid navigation stack with Squint Vision StudioHow an unfamiliar market condition turns a demand forecast into a supply chain problemHow an onboard cloud filter decides which satellite images to keepBuilding a squinting pipeline for weed and crop detection with Squint Vision StudioFraud detection: finding the transactions a model cannot judgeWhy a camera-based landing system passes every test and still cannot be certified to flyWhen a weld-inspection model is most reliable on the defects that matter least
Why certifying safety-critical software costs so much more than writing itNeuro-symbolic reasoning, and why this makes AeroCert fundamentally different from generative AI toolsWhy intelligence requires awareness of uncertaintyWhy end-to-end learning struggles in complex environmentsWhy out-of-distribution detection keeps failing in deploymentUnderstanding ambiguity through representation overlapWhy confidence thresholds don't workThe geometry of shortcut learningLatent drift: how models change without anyone noticingWhy the future of AI is not more data, but better reasoningThe illusion of generalization in machine learningReasoning under entropyWhat makes an AI system cognitive?Watchdogs, not monitors: a new control paradigm for AI systemsWhy a neuron has no meaning: distributed representations and the limits of interpretabilityWhy autonomy without cognition is unsafeThe confidence fallacyWhy AI fails in the ordinaryWhy feature attribution fails to explainSelf-correcting AIMapping the mind of AIWhy debugging AI is harderThe deployment gapWhy scaling models doesn't fix fragilityThe hidden costThe AI maturity curveWhy AI cannot self-correctInternal representations are opaqueMaking AI thinkSecond-order modelsCognitive AI
Cognitive Information Theory - Structural Limits of Objective-Function AI through Irreducible Entropy, Causal Ambiguity, and Sequential Distribution Drift
Machine Learning with Applications - A novel application of XAI in squinting models: a position paper
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Documentation

CIFAR-10 DATASET

You will be guided through the process of setting up a project, importing the CIFAR-10 dataset and model...

BREAST CANCER DATASET

This guide walks you through a complete workflow in Squint Vision Studio using the Breast Histopathology Images Dataset...

COCO DATASET

This guide walks you through a complete workflow in Squint Vision Studio using a custom 10-class subset of the COCO dataset...

MNIST DATASET

You will be guided through the process of setting up a project, importing the MNIST dataset and model...