Cloud Computing INFS3208/INFS7208 Re-cap – Lecture 7 • Database Background • Relational Data Bases – Revisit Relational DBs – ACID Properties * – Clustered RDBMs • Non-relational Data Bases – NoSQL concepts – CAP Theorem *** – MongoDB – Cassandra – HBase CRICOS code 00025B 2 Outline • Background of Distributed File Systems – Big […]
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Cloud Computing INFS3208 Re-cap 1. CourseOrientation 2. HistoryandDefinitionofCloudComputing 3. BusinessDriversforcreationofCloudComputing – Capacity Planning, Cost Reduction, Organisational Agility 4. TechnologiesthatimpactCloudComputing – Clustering, Grid Computing, Virtualisation 5. CloudCharacteristics – On-demand usage, Ubiquitous access, Multitenancy, Elastic, Measurable, Resilient. CRICOS code 00025B 2 Outline • CloudDeliveryModels • CloudDeployModels • Cloud-EnablingTechnologies – Broadband Networks and Internet Architecture – Virtualisation Technology
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6CCS3AIN Week 10: AI and Ethics: Tutorial Questions and Solutions Q1. In the lectures, we mention that for model-driven (or symbolic) AI systems it is usually straightforward to generate explanations of the reasoning used by the AI system in each particular case where the system is applied. An expert system which uses IF-THEN rules is
Inference by enumeration We saw that with our joint distribution table we can calculate any probability Obvious problems: 1. Worst-casetimecomplexityOpdnqwheredisthelargestarity 2. SpacecomplexityOpdnqtostorethejointdistribution 3. HowtofindthenumbersforOpdnqentries??? These problems effectively stopped the use of probability in AI until the mid 80s ⃝c -Trenn, King’s College London 2 Computational efficiency To get efficient probabilistic computations, we need two things.
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Computer Science is the science of delegation Key question: How do we delegate tasks to a machine? Hardware engineering: How to design machines able to effectively & efficiently undertake the tasks we delegate to them? Programming theory: How do we best write instructions for machines to do what we what we want them
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6CCS3AIN (Artificial Intelligence) Practical 01: Getting started with Pacman (Version 1.0) 1 Overview This practical is the first step towards the first coursework. In it you will get setup to run the Pacman game that will be the basis of the coursework for the module, and write your first code to control Pacman. Since the
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Inference by enumeration We saw that with our joint distribution table we can calculate any probability Obvious problems: 1. Worst-casetimecomplexityOpdnqwheredisthelargestarity 2. SpacecomplexityOpdnqtostorethejointdistribution 3. HowtofindthenumbersforOpdnqentries??? These problems effectively stopped the use of probability in AI until the mid 80s c -Trenn, King’s College London 2 Computational efficiency To get efficient probabilistic computations, we need two things.
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6CCS3AIN Week 10: AI and Ethics: Tutorial Questions Q1. In the lectures, we mention that for model-driven (or symbolic) AI systems it is usually straightforward to generate explanations of the reasoning used by the AI system in each particular case where the system is applied. An expert system which uses IF-THEN rules is a common
AI and Ethics Outline This week we talk about AI and Ethics Why is this topic important Regulatory and legal constraints Ethical dimensions Fairness (non-bias), transparency, explanation, rectification Features of this domain Deciding what we ourselves should do in specific situations. 2 Why is it important to consider ethics?
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Today Introduction Probabilistic Reasoning Sequential Decision Making Game Theory (A peek at) Machine Learning Argumentation AI & Ethics ⃝c -Trenn, King’s College London 2 Recap percepts Environment sensors AI Systems operate in environments ⃝c -Trenn, King’s College London 3 actions effectors Recap Environments are: ‚ Acessibleornot. ‚ Deterministic/stochastic/non-deterministic ‚ StaticorDynamic ‚ SequentialorEpisodic ‚ Discreteorcontinuous ‚