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Hosted by IRMA (Information Risk Management and Assurance) Specialist Group of the BCS (British Computer Society).

12/03/19 @ The Chartered Institute for IT, London

Speaker: Mike Small, Senior Analyst at KuppingerCole

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Topics of talk:

• What AI is

• Limitations of AI today

• AI in practice today

• Areas where AI could be successfully applied

• Ethical issues of AI and potential approaches

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• ​Is the attempt to simulate human abilities. 

• • For this we need knowledge. Historically understood via epistemology, mathematics, and science.

• Involves robotics and decision making. 

  • Mike will focus moreso on decision making.

• Is not new. History stretching back to the 1940s with various attempts to imitate human abilities e.g. chess, speech, etc.

  • What has changed is the invention of the Cloud. It brings the tech from the 1980s into the modern era by storing more data and making it easily accessible.

• Works using rules. These same rules can be misapplied or misinterpreted by people: AI systems can help. AI often seen as less fallible and more trusted.

  • Anecdote: Salford University has an autonomous car which students regularly, intentionally step in front of in order to make brake. Would not do so if a fellow student were driving.

• E.g. social security advice at DHSS – standardising advice by offering consistent suggestions.

• E.g. container dock layout planner in Hong Kong – economising on space and efficiently organising the mooring of ships.

• Is the business of data analytics. It builds up from statistical systems using algorithms into neural networks and results in features such as computer vision and natural language processing.

Machine Learning: “Iterative convergence on best match to training data”

• One trains the system to analyse data with a training data set. Subsequently, just add more data to generate output.

• Perceptron as example of an algorithm: a form of maths used to turn data sets/input into output, using weightings and thresholds.

• AI is more general than Machine Learning. However, these terms are often interchangeable. Though, it used to be the case that rules-based sytems were called AI, and then systems based on LISP. Now it is Machine Learning.

• Mike’s joke/point: systems are called solutions when finished and work; they’re called AI or Machine Learning when unsure – needs a bit of fairy dust to make it sound more interesting/sellable.

Deep Learning aka convolution – layers of processing

Lack of common sense

• Neural networks cannot explain themselves

• Insufficient training data + bias

• Machine Learning can be duped

  • E.g. exploit facial recognition using glasses (Adversarial Misclassification).

​Security Analytics

• • Identify anomalies; event analysis and support less skilled analysts.

  • AI looks not just at data set but also books, websites, social media, etc. to output suggestion to analyst that takes into consideration recent developments and news.

    • Aside: AI can be used to augment more junior and less skilled roles – requires less of them, gives suggestions and makes them spot aspects/issues that they previously failed to acknowledge.

• Manufacturing

  • Trained to spot defects in HD TVs by scanning the surface for pixel damage.

• Regulatory Compliance (Finance)

  • Helps deal with sifting through regulation, particularly new regulation, and relates obligations to company.

  • Identifies any adjustments needed, especially in light of regulatory change.

  • Also used to identify insider trading since communication between traders are monitored.

    • E.g. identify buzzwords and code that can be linked to trader behaviour e.g. “it going to rain in Chicago” and then trader sells shares in Chicago.

• Autonomous database

  • Suggestions to organise data differently and thereby improve performance.

• Narrow AI (emerging)

  • Single tasks, single domain: very accurate, very fast.

• Broad AI (disruptive)

  • Muti task, multi domains: explainable; transfers skills and tasks across domains.

• General AI (revolutionary)

  • Cross domain learning and reasoning; autonomy.

• The challenge is public acceptance.

  • GM crops/Golden Rice as case study of innovative technology curtailed by protests.

  • Computing may flit from benign to malign in public perception, thus evading risks but also losing out on potential gains and benefits.

• Surveillance Capitalism: the focus has changed from money to data. This could potentially face a backlash.

• Involves looking at the Context->Consequences->Justification

• Popular case study: Trolley problem

  • Different cultures have different answers.

  • Can’t sue a car, who do you sue? The designer, developer, builder, owner?

  • In court, would need to defend the decision made by the car. Would this be clear?

• Need to consider:

  • Benefit v harmfulness

  • Inclusion v exclusion 

    • Diversity

    • Disability

    • Bias

  • Bias v unfairness

    • Bias towards the culture of the data set

    • Not all bias is bad, unfair or unintended bias is.

  • Good v bad behaviour

  • Responsibility

  • Economic impact

• How do you convince an AI that it has done something wrong?

  • Film example: ‘Dark Star’ bomb.