AI Myths, Realities, and Challenges:
Summary Notes of Event
Event Description
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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:
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What AI is
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Limitations of AI today
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AI in practice today
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Areas where AI could be successfully applied
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Ethical issues of AI and potential approaches
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What Is AI:
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​Is the attempt to simulate human abilities.
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For this we need knowledge. Historically understood via epistemology, mathematics, and science.
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Involves robotics and decision making.
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Mike will focus moreso on decision making.
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Is not new. History stretching back to the 1940s with various attempts to imitate human abilities e.g. chess, speech, etc.
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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.
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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.
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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.
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E.g. social security advice at DHSS – standardising advice by offering consistent suggestions.
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E.g. container dock layout planner in Hong Kong – economising on space and efficiently organising the mooring of ships.
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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”
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One trains the system to analyse data with a training data set. Subsequently, just add more data to generate output.
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Perceptron as example of an algorithm: a form of maths used to turn data sets/input into output, using weightings and thresholds.
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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.
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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
Limitations of AI:
Lack of common sense
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Neural networks cannot explain themselves
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Insufficient training data + bias
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Machine Learning can be duped
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E.g. exploit facial recognition using glasses (Adversarial Misclassification).
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AI in Practice:
​Security Analytics
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Identify anomalies; event analysis and support less skilled analysts.
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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.
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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.
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Manufacturing
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Trained to spot defects in HD TVs by scanning the surface for pixel damage.
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Regulatory Compliance (Finance)
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Helps deal with sifting through regulation, particularly new regulation, and relates obligations to company.
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Identifies any adjustments needed, especially in light of regulatory change.
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Also used to identify insider trading since communication between traders are monitored.
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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.
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Autonomous database
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Suggestions to organise data differently and thereby improve performance.
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Future of AI:
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Narrow AI (emerging)
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Single tasks, single domain: very accurate, very fast.
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Broad AI (disruptive)
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Muti task, multi domains: explainable; transfers skills and tasks across domains.
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General AI (revolutionary)
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Cross domain learning and reasoning; autonomy.
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The challenge is public acceptance.
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GM crops/Golden Rice as case study of innovative technology curtailed by protests.
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Computing may flit from benign to malign in public perception, thus evading risks but also losing out on potential gains and benefits.
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Surveillance Capitalism: the focus has changed from money to data. This could potentially face a backlash.
Ethics:
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Involves looking at the Context->Consequences->Justification
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Popular case study: Trolley problem
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Different cultures have different answers.
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Can’t sue a car, who do you sue? The designer, developer, builder, owner?
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In court, would need to defend the decision made by the car. Would this be clear?
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Need to consider:
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Benefit v harmfulness
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Inclusion v exclusion
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Diversity
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Disability
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Bias
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Bias v unfairness
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Bias towards the culture of the data set
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Not all bias is bad, unfair or unintended bias is.
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Good v bad behaviour
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Responsibility
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Economic impact
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How do you convince an AI that it has done something wrong?
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Film example: ‘Dark Star’ bomb.
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