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Technologies

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Machine Learning

Evocative image of machine learning

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Introduction

Machine Learning … … a new approach to solving problems

… learns from supplied big data

… can perform better than humans

… has its limitations

… useful applications for customs agencies

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• Machine learning (ML) is a type of artificial intelligence (AI) that provides computers with the ability to learn without being explicitly programmed

• ML uses computer programs that change when given new data

• Deep Neural Networks (DNNs) are a set of algorithms, modeled loosely after the human brain, that are designed to recognize patterns

Definitions

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• Virtual personal assistants (e.g. Siri, Cortana) – Including natural language recognition

• Search query results, recommender systems & auto-complete

• Language translation • Medicine – finding cancers & other diseases • Computer vision

– tagging/labelling images – handwriting recognition

• Self driving cars • Winning at games (e.g. Go) • Financial fraud analysis

Successful Uses of ML

etc

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• Needs a large amount of data for training

• Supervised learning – guidance given with the data, e.g. labels attached

• Unsupervised learning – clustering of information into groups with common features

• DNNs have layers of “neurons”

– each layer feeds next layer

– automatic adjustments from feedback produce the learning

How ML Works

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• Don’t really know how they work

– e.g. detection of onset of schizophrenia

• Can make mistakes

• Can be fooled

Limitations

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• In spite of the limitations, ML is very powerful

• Many different industries are using it: – finance, insurance, medical, advertising etc

• A growth area that will be “as big a change for tech as the internet was” (Greg Corrado, Google)

• Has multiple potential applications for customs such as: – automatic threat detection

– intelligence gathering

– trend analysis

– other potentials …

Uses for Customs Agencies

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• One of the uses of ML for customs is Automatic Threat Detection (ATD)

• Taught with labelled images to distinguish between benign and threat

• Once taught, computers can recognise threats very quickly with a high accuracy

• Can learn new threats without the need for reprogramming of algorithms

Automatic Threat Detection

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• Trained with a dataset of firearms and large firearm parts in baggage, 1090 items total – firearms: revolver, shotgun, self-loading pistol, assault

rifle, convertible, sub-machine gun – large firearm parts: barrel, magazine

• Tested with a dataset of 48 items made up from each category

• Average accuracy of 97% • Average PPV of 98% Accuracy = (TP + TN) / (TP + TN + FP + FN) ; the percentage of correctly classified cases Positive predictive value (PPV) = TP / (TP + FN); the percentage of threat cases correctly identified

ATD Stats

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• Heatmap showing identified threat

ATD Example

Source: L3 / Cosmonio ; project funded by UK Home Office

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• L3 believes that Machine Learning is a very effective tool for Automatic Threat Detection

• Being able to find items quickly can change the CONOPS for scanning – scan higher percentage of items

– use for triaging / targeting of items

• Human operators still need to be involved for validating the results and secondary checks

• Need collaboration with customs agencies – to use realistic data

– to direct future applications of Machine Learning

Conclusions