Series

DLS: Scalable Intelligent Systems by 2025 (Carl Hewitt)

Venue: The Old Course Hotel (Hall of Champions)

Timetable:

9:30 Lecture 1
10:30 Break with Coffee
11:15 Lecture 2
12:15 Break for Lunch (not provided)
14:15 Lecture 3
15:15 Discussion

Lecture 1: Introduction to Scalable Intelligent Systems

Lecture 2: Foundations for Scalable Intelligent Systems

Lecture 3: Implications of Scalable Intelligent Systems

Speaker Bio:

Professor Carl Hewitt is the creator (together with his students and other colleagues) of the Actor Model of computation, which influenced the development of the Scheme programming language and the π calculus, and inspired several other systems and programming languages. The Actor Model is in widespread industrial use including eBay, Microsoft, and Twitter. For his doctoral thesis, he designed Planner, the first programming language based on pattern-invoked procedural plans.

Professor Hewitt’s recent research centers on the area of Inconsistency Robustness, i.e., system performance in the face of continual, pervasive inconsistencies (a shift from the previously dominant paradigms of inconsistency denial and inconsistency elimination, i.e., to sweep inconsistencies under the rug). ActorScript and the Actor Model on which it is based can play an important role in the implementation of more inconsistency-robust information systems. Hewitt is an advocate in the emerging campaign against mandatory installation of backdoors in the Internet of Things.

Hewitt is Board Chair of iRobust™, an international scientific society for the promotion of the field of Inconsistency Robustness. He is also Board Chair of Standard IoT™, an international standards organization for the Internet of Things, which is using the Actor Model to unify and generalize emerging standards for IoT. He has been a Visiting Professor at Stanford University and Keio University and is Emeritus in the EECS department at MIT.

Abstract:

A project to build the technology stack outlined in these lectures can bring Scalable Intelligent Systems to fruition by 2025. Scalable Intelligent Systems have the following characteristics:

  • Interactively acquire information from video, Web pages, hologlasses, online data bases, sensors, articles, human speech and gestures, etc.
  • Real-time integration of massive pervasively inconsistent information
  • Scalability in all important dimensions meaning that there are no hard barriers to continual improvement in the above areas
  • Close human collaboration with hologlasses for secure mobile interaction. Computers alone cannot implement the above capabilities
  • No closed-form algorithmic solution is possible to implement the above capabilities

Technology stack for Scalable Intelligent Systems is outlined below:

  • Experiences Hologlasses: Collaboration, Gestures, Animations, Video
  • Matrix Discourse, Rhetoric, and Narration
  • Citadels No single point of failure
  • Massive Inconsistency Robust Ontology Propositions, Goals, Plans, Descriptions, Statistics, Narratives
  • Actor Services Hardware and Software
  • Actor Many Cores Non-sequential, Every-word-tagged, Faraday cage Crypto, Stacked Carbon Nanotube

For example, pain management could greatly benefit from Scalable Intelligent Systems. Complexities of dealing with pain have led to the current opioid crisis. According to Eric Rodgers, PhD., director of the VA’s Office of Evidence Based Practice:

“The use of opioids has changed tremendously since the 1990s, when we first started formulating a plan for guidelines. The concept then was that opioid therapy was an underused strategy for helping our patients and we were trying to get our providers to use this type of therapy more. But as time went on, we became more aware of the harms of opioid therapy and the development of pill mills. The problems got worse.

It’s now become routine for providers to check the state databases to see if there’s multi-sourcing — getting prescriptions from other providers. Providers are also now supposed to use urine drug screenings and, if there are unusual results, to do a confirmation. [For every death from an opioid overdose] there are 10 people who have a problem with opioid use disorder or addiction. And for every addicted person, we have another 10 who are misusing their medication.”

Pain management requires much more than just prescribing opioids, which are often critical for short-term and less often longer-term use. [Coker 2015; Friedberg 2012; Holt 2017; Marchant 2017; McKinney 2015; Spiegel 2018; Tedesco, et. al. 2017; White 2017] Organizational aspects play an important role in pain management. [Fagerhaugh and Strauss 1977]

SRG Seminar: “Efficient Cross-architecture Hardware Virtualisation” by Tom Spink

Virtualisation is a powerful tool used for the isolation, partitioning, and sharing of physical computing resources. Employed heavily in data centres, becoming increasingly popular in industrial settings, and used by home-users for running alternative operating systems, hardware virtualisation has seen a lot of attention from hardware and software developers over the last ten?fifteen years.

From the hardware side, this takes the form of so-called hardware assisted virtualisation, and appears in technologies such as Intel-VT, AMD-V and ARM Virtualization Extensions. However, most forms of hardware virtualisation are typically same-architecture virtualisation, where virtual versions of the host physical machine are created, providing very fast isolated instances of the physical machine, in which entire operating systems can be booted. But, there is a distinct lack of hardware support for cross-architecture virtualisation, where the guest machine architecture is different to the host.

I will talk about my research in this area, and describe the cross-architecture virtualisation hypervisor Captive that can boot unmodified guest operating systems, compiled for one architecture in the virtual machine of another.

I will talk about the challenges of full system simulation (such as memory, instruction, and device emulation), our approaches to this, and how we can efficiently map guest behaviour to host behaviour.

Finally, I will discuss our plans for open-sourcing the hypervisor, the work we are currently doing and what future work we have planned.

Fable-based Learning: Seminar by Prof Jimmy Lee

CUHK + UniMelb = Fable-based Learning + A Tale of Two Cities

Prof Jimmy Lee, Chinese University of Hong Kong

This talk reports on the pedagogical innovation and experience of a joint venture by The Chinese University of Hong Kong (CUHK) and the University of Melbourne (UniMelb) in the development of MOOCs on the computer science subject of “Modeling and Solving Discrete Optimization Problems”.  In a nutshell, the MOOCs feature the Fable-based Learning approach, which is a form of problem-based learning encapsulated in a coherent story plot.  Each video lecture begins with an animation that tells a story based on the Chinese classic “Romance of the Three Kingdoms”, in which the protagonists in the novel encounter a problem requiring technical assistance from the two professors from modern time via a magical tablet bestowed upon them by a fairy god.  The new pedagogy aims at increasing learners’ motivation as well as situating the learners in a coherent learning context.  In addition to scriptwriting, animation production and situating the teaching materials in the story plot, another challenge of the project is the remote distance and potential cultural gap between the two institutions as well as the need to produce all teaching materials in both (Mandarin) Chinese and English to cater for different geographical learning needs.  The MOOCs have been running recurrently on Coursera since 2017.  Some learner statistics and feedbacks will be presented.  The experience and preliminary observations of adopting the online materials in a Flipped Classroom setting at CUHK will also be detailed.

This video at Youtube shows the trailer for the Coursera Course:

Biography:

Jimmy Lee has been on the faculty of The Chinese University of Hong Kong since 1992, where he is currently the Assistant Dean (Education) in the Faculty of Engineering and a Professor in the Department of Computer Science and Engineering.  His major research focuses on constraint satisfaction and optimization with applications in discrete optimization, but he is also involved in investigating ways of improving students’ learning experience via proper use of technologies.  Jimmy is a two-time recipient (2004 and 2015) of the Vice-Chancellor’s Exemplary Teaching Award and most recently the recipient of the University Education Award (2017) at CUHK.

Seminar: SMT, Planning and Snowmen

Professor Mateu Villaret, from Universitat de Girona is a visiting scholar with the AI group from July 1st until September 30th. Professor Villaret works on algorithms for routing and scheduling with the AI group at St Andrews.

As well as solving practical problems, he also enjoys puzzle games. That is the basis of this talk, about using Planning and SMT to solve the “Snowman” puzzle.

DHSI Seminar Series

The school of Physics & Astronomy (Room 222) are hosting our next Digital Health Seminar

12.00pm – Lunch
12.20pm – Isla Rose & Mary Barnard Ultraviolet Radiation, DNA damage, and sunscreen
12.50pm – Lewis McMillan Monte Carlo radiation transfer model of laser tissue ablation
1.20pm –   Nicole Schanche Planet candidate detection and ranking using MachineLearning
1.50pm –   General discussions

All welcome!

DHSI Flyer – Physics & Astronomy 17.8

Seminar: AI-augmented algorithms — how I learned to stop worrying and love choice

The speaker is Lars Kotthoff, previously a PhD student here, now and Assistant Professor at the University of Wyoming. All welcome.

 

Often, there is more than one way to solve a problem. It could be a different
parameter setting, a different piece of software, or an entirely different
approach. Choosing the best way is usually a difficult task, even for experts.
AI and machine learning allow to leverage performance differences of
algorithms (for a wide definition of “algorithm”) on different problems and
choose the best algorithm for a given problem automatically. In AI itself,
these techniques have redefined the state of the art in several areas and led
to innovative approaches to solving challenging problems.

In this talk, I will give examples of how AI can help to solve challenging
computational problems, what techniques have been applied, and how you can do
the same. I will argue that AI has fundamental implications for software
development, engineering, and computer science in general — stop making
decisions when coding, having more algorithmic choices is better!

 

SRG Seminar: “Application of Bayesian Nonparametric in household human activity recognition” by Lei Fang

Abstract

In this talk, I will talk about the possibility of using Bayesian nonparametric clustering, or Dirichlet Process Mixture model to solve human activity recognition problem. In particular, I will discuss how the technique can be useful when the activity labels are not annotated and/or the activity evolves over the time. This initial study is built on an existing work on using directional statistical models (von Mises-Fisher) distribution, called Hierarchical Mixture of Conditional Independent von Mises Fisher distribution (HMCIvMFs), for unknown events detection and learning. Markov chain Monte Carlo sampling based learning algorithm will be presented together with some initial experiment results.

SRG Seminar: “Introduction to Apache Mesos and the DataCenter Operating System” by Matt Jarvis

Abstract
Data processing paradigms are undergoing a paradigm shift as we move more and more towards real time processing. Emerging software models such as the SMACK stack are at the forefront of this change, focused on a pipeline processing model, but are also introducing new levels of operational complexity in running multiple complex distributed systems such as Spark, Kafka and Cassandra. In this talk, I’ll introduce both Apache Mesos and DC/OS as a solution to this growing problem, and describe the benefits are of running these new kinds of systems for emerging cloud native workloads.
 
Bio
Matt Jarvis is Senior Director of Community and Evangelism at Mesosphere, engaging with the communities around DC/OS and Mesos. Matt has spent more than 15 years building products and services around open source software, on everything from embedded devices to large scale distributed systems. Most recently he has been focused on the open cloud infrastructure space, and in emerging patterns for cloud native applications. 

SRG Seminar: “On Engineering Unikernels” by Ward Jaradat

We have explored data coordination techniques that permit distributed systems to be constructed by interconnecting services. In such systems the network latency is often a problem. For example, large data volumes might have to be transmitted across the network if computation cannot be co-located close to data sources. One solution to this problem is the ability to deploy services in appropriate geographical locations and compose them together to create distributed ecosystems. Hence we seek to be able to deploy such services rapidly and dynamically enact and orchestrate them. However, this goal is hindered by the size of the deployments. Currently, virtual machine appliances that host such services on top of monolithic kernels are very large, thus are potentially slow to deploy as they may need to be transmitted across a network.

Our principles led us to take the route of re-engineering the standard software stack to create self-contained applications that are less-bloated and consequently much smaller based on Unikernels. Unikernels are compact library operating systems that enable a single application to be statically linked against a simple kernel that manages the underlying resources presented by a hypervisor. In this talk I will present Stardust – a specialised Unikernel that aims to support the deployment of application services based on the Java programming language.

DLS: Functional Foundations for Operating Systems

Biography: Dr. Anil Madhavapeddy is a University Lecturer at the Cambridge Computer Laboratory, and a Fellow of Pembroke College where he is Director of Studies for Computer Science. He has worked in industry (NetApp, Citrix, Intel), academia (Cambridge, Imperial, UCLA) and startups (XenSource, Unikernel Systems, Docker) over the past two decades. At Cambridge, he directs the OCaml Labs research group which delves into the intersection of functional programming and systems, and is a maintainer on many open source projects such as OpenBSD, OCaml, Xen and Docker.

Timetable
9:30: Introduction by Professor Saleem Bhatti
9:35: Lecture 1
10:35: Break with tea and coffee
11:15: Lecture 2
12:15: Lunch (not provided)
14:00: Lecture 3
15:00: Close by Professor Simon Dobson

Lecture 1: Rebuilding Operating Systems with Functional Principles
The software stacks that we deploy across computing devices in the world are based on shaky foundations. Millions of lines of C code crammed into monolithic operating system kernels, mixed with layers of scheduling logic, wrapped in a hypervisor, and served with a dose of nominal security checking on the side. In this talk, I will describe an alternative approach to constructing reliable, specialised systems with a familiar developer experience. We will use modular functional programming to build several services such as a secure web server that have no reliance on conventional operating systems, and explain how to express their logic in a high level, functional fashion. By the end of it, everyone in the audience should be able to build their own so-called unikernels!

Lecture 2: The First Billion Real Deployments of Unikernels
Unikernels offer a path to a more sane basis for driving applications on hardware, but will they ever be adopted for real? For the past fifteen years, an intrepid group of adventurers have been developing the MirageOS application stack in the OCaml programming language. Along the way, it has been deployed in many unusual industrial situations that I will describe in this talk, starting with the Docker container stack, then moving onto the Xen hypervisor that drives billions of servers worldwide. I will explain the challenges of using functional programming in industry, but also the rewards of seeing successful deployments quietly working in mission-critical areas of systems software.

Lecture 3: Programming the Next Trillion Embedded Devices
The unikernel approach of compiling highly specialised applications from high-level source code is perfectly suited to programming the trillions of embedded devices that are making their way around the world. However, this raises new challenges from a programming language perspective: how can we run on a spectrum of devices from the very tiny (with just kilobytes of RAM) to specialised hardware? I will describe the new frontier of functional metaprogramming (programs which generate more programs) that we are using to compile a single application to many heterogenous devices, and a Git-like model to coordinate across thousands of nodes. I will conclude with by motivating the need for a next-generation operating system to power new exciting applications such as augmented and virtual reality in our situated environments, and remove the need for constant centralised coordination via the Internet.