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SkillsCast

Human-in-the-loop analytics and machine learning

19th April 2018 in London at CodeNode

There are 3 other SkillsCasts available from AI & Deep Learning Use Cases

In this talk we will discuss the role of human-in-the-loop analytics that incorporate machine learning or reasoning capabilities. Our emphasis is on how machine learning can enhance and engage the experience of analysts working in domains such as cyber security and support complex decision-making / -planning. We shall outline how the increasingly significant challenges of data volume, variety and veracity impede progress within these fields and how practical, well-designed AI techniques can make a positive difference and these problems more tractable. Relativistic anomaly scoring systems based on tensors, bespoke unsupervised clustering techniques and manifolds in high-dimension datasets; Use of container-based machine reasoning in scenario planning, using controlled natural language that is human intuitive and machine readable; and Application of machine learning in generative signature models applied to advanced cyber security and self-healing networks. In parallel with these examples we shall describe a model of computing in which runtimes are called and applied based on assessing their 'best-fit' for a given problem. This speaks to a future in which classical machines are used in concert with hybrid or highly optimised specialist platforms, universal quantum computers, non von Neumann architectures, approximate and stochastic computing paradigms.

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Human-in-the-loop analytics and machine learning

Leigh Chase

Leigh is a Computer Scientist and Technical Leader within the Emerging Technology group of IBM Research. Based at the Hursley laboratories (UK) his specialisms are information security, artificial intelligence / machine learning and scientific computing. Much of Leigh's current work relates to experimental computer science; specifically the design and creation cognitive agents that bolster existing network defence technologies, working alongside human analysts to improve visibility and understanding of complex systems.

SkillsCast

In this talk we will discuss the role of human-in-the-loop analytics that incorporate machine learning or reasoning capabilities. Our emphasis is on how machine learning can enhance and engage the experience of analysts working in domains such as cyber security and support complex decision-making / -planning. We shall outline how the increasingly significant challenges of data volume, variety and veracity impede progress within these fields and how practical, well-designed AI techniques can make a positive difference and these problems more tractable. Relativistic anomaly scoring systems based on tensors, bespoke unsupervised clustering techniques and manifolds in high-dimension datasets; Use of container-based machine reasoning in scenario planning, using controlled natural language that is human intuitive and machine readable; and Application of machine learning in generative signature models applied to advanced cyber security and self-healing networks. In parallel with these examples we shall describe a model of computing in which runtimes are called and applied based on assessing their 'best-fit' for a given problem. This speaks to a future in which classical machines are used in concert with hybrid or highly optimised specialist platforms, universal quantum computers, non von Neumann architectures, approximate and stochastic computing paradigms.

YOU MAY ALSO LIKE:

Thanks to our sponsors

About the Speaker

Human-in-the-loop analytics and machine learning

Leigh Chase

Leigh is a Computer Scientist and Technical Leader within the Emerging Technology group of IBM Research. Based at the Hursley laboratories (UK) his specialisms are information security, artificial intelligence / machine learning and scientific computing. Much of Leigh's current work relates to experimental computer science; specifically the design and creation cognitive agents that bolster existing network defence technologies, working alongside human analysts to improve visibility and understanding of complex systems.