Context

List out the areas that people need to read up on, and find some good basic references to link to - maybe curate some of the better simplified descriptions for non-technical people

• https://ig.ft.com/generative-ai/

What should we talk about

Background

• We don’t need yet another “introduction to AI” - people can read that elsewhere

• Perhaps a simple reminder that “AI” used to be called “machine learning” and before that it was called “data science” and before that it was called “statistics”

• At its core, it is about pattern recognition and extrapolation / prediction

• Why is this a hot topic now? Availability of compute, data and research has caused a massive acceleration (but not a whole timeline)

• Probably need to introduce 2 major innovations that have a direct bearing on the subject:

  • Transformer models - neural networks that learn context, trained on very large data sets (“foundation models”) - leading to many new applications in NLP (e.g. ChatGPT)

    • Consider adding background on what a neural network is too

  • Generative Adversarial Networks - pairing of two neural networks (a generator and a discriminator) - creates very realistic new content (including deepfakes)

• The goal is to enable consumers of this information to have sufficient context to understand how the different modalities work and in turn how they apply to IDV

• Consider the output as diagrams / infographics rather than defaulting to a whitepaper format for this material

Relevant modalities

• Explanation of what they are, how they work, how they’re used in IDPV today

• Do we need to explain the current state of the art, or will that be handled in the other topic areas about defences and attacks?

• How are these different to how a human verifies identity?

• Computer vision

  • Optical character recognition

    • Extracting text from documents (to read information from them)

  • Object detection

    • Recognising features on documents (to determine authenticity)

  • Biometrics (face, fingerprint, palm, voice??)

    • Matching unique features of a subject against enrolled individuals

  • Liveness detection

• Pattern / anomaly detection

  • Behavioural analysis

  • Risk scoring

• Natural language processing

  • Language translation

  • Disambiguating and “fuzzy matching” (against data sources)

  • Sentiment analysis??

• GANs

  • Creation of realistic image/video/audio

    • Deepfakes

Where might this go

• Speculation about how things might evolve and whether that could lead to new impacts on the IDPV sector

  • Consider teeing this up here, but the detail should be in the "Attacks" section

  • Liaise with Heather on future scenario development

• Leave the impression that the only certainty is change going forward

Who should we get to contribute?