DataCafé

Do you have multiple devices connected to your internet fighting for your bandwidth? Are you asking your children (or even neighbours!) to get off the network so you can finish an important call? Recent lockdowns caused huge network contention as everyone moved to online meetings and virtual classrooms. This is an optimisation challenge that requires advanced modelling and simulation to tackle. How can a network provider know how much bandwidth to provision to a town or a city to cope with peak demands? That's where agent-based simulations come in - to allow network designers to anticipate and then plan for high-demand events, applications and trends.
In this episode of the DataCafé we hear from Dr. Lucy Gullon, AI and Optimisation Research Specialist at Applied Research, BT. She tells us about the efforts underway to assess the need for bandwidth across different households and locations, and the work they lead to model, simulate, and optimise the provision of that bandwidth across the network of the UK. We hear how planning for peak use, where, say, the nation is streaming a football match is an important consideration. At the same time, reacting to times of low throughput can help to switch off unused circuits and equipment and save a lot of energy.
Interview Guest: Dr. Lucy Gullon, AI and Optimisation Research Specialist from Applied Research, BT.
Further reading:

• BT Research and Development (https://www.bt.com/about/bt/research-and-development)
• Anylogic agent-based simulator (https://www.anylogic.com/use-of-simulation/agent-based-modeling/)
• Article: Agent-based modelling (via Wikipedia)
• Article:Prisoner's Dilemma (via Wikipedia)
• Article: Crowd Simulation (via Wikipedia)
• Book: Science and the City (via Bloomsbury)
• Research group: Traffic Modelling (via mit.edu)

Some links above may require payment or login. We are not endorsing them or receiving any payment for mentioning them. They are provided as is. Often free versions of papers are available and we would encourage you to investigate.

Recording date: 5 May 2022
Interview date: 27 Apr 2022

Thanks for joining us in the DataCafé. You can follow us on twitter @DataCafePodcast and feel free to contact us about anything you've heard here or think would be an interesting topic in the future.

Do you ever find yourself wondering what the data was you used in a project? When was it obtained and where is it stored? Or even just the way to run a piece of code that produced a previous output and needs to be revisited?
Chances are the answer is yes. And it’s likely you have been frustrated by not knowing how to reproduce an output or rerun a codebase or even who to talk to to obtain a refresh of the data - in some way, shape, or form.
The problem that a lot of project teams face, and data scientists in particular, is the agreement and effort to document their work in a robust and reliable fashion. Documentation is a broad term and can refer to all manner of project details, from the actions captured in a team meeting to the technical guides for executing an algorithm.
In this bite episode of DataCafé we discuss the challenges around documentation in data science projects (though it applies more broadly). We motivate the need for good documentation through agreement of the responsibilities, expectations, and methods of capturing notes and guides. This can be everything from a summary of the data sources and how to preprocess input data, to project plans and meeting minutes, through to technical details on the dependencies and setups for running codes.

Thanks for joining us in the DataCafé. You can follow us on twitter @DataCafePodcast and feel free to contact us about anything you've heard here or think would be an interesting topic in the future.

Are people resistant to change? And if so, how do you manage that when trying to introduce and deliver innovation through Data Science?
In this episode of the DataCafé we discuss the challenges faced when trying to land a data science project. There are a number of potential barriers to success that need to be carefully managed. We talk about "change management" and aspects of employee behaviours and stakeholder management that influence the chances of landing a project. This is especially important for embedding innovation in your company or organisation, and implementing a plan to sustain the changes needed to deliver long-term value.
Further reading & references

• Kotter's 8 Step Change Plan
• Armenakis, Achilles & Harris, Stanley & Mossholder, Kevin. (1993). Creating Readiness for Organizational Change. Human Relations. 46. 681-704. 10.1177/001872679304600601.
• Lewin, K (1944a) Constructs in Field Theory. In D Cartwright(Ed):(1952) Field Theory in Social Science: Selected Theoretical Papers by Kurt Lewin. London: Social Science Paperbacks. pp30-42
• Lewin, K. (1947) ‘Frontiers in Group Dynamics: Concept, Method and Reality in Social Science; Social Equilibria and Social Change’, Human Relations, 1(1), pp. 5–41. doi: 10.1177/001872674700100103.
  

Some links above may require payment or login. We are not endorsing them or receiving any payment for mentioning them. They are provided as is. Often free versions of papers are available and we would encourage you to investigate.
Recording date: 10 February 2022

Thanks for joining us in the DataCafé. You can follow us on twitter @DataCafePodcast and feel free to contact us about anything you've heard here or think would be an interesting topic in the future.

Data scientists usually have to write code to prototype software, be it to preprocess and clean data, engineer features, build a model, or deploy a codebase into a production environment or other use case. The evolution of a codebase is important for a number of reasons which is where version control can help, such as:

• collaborating with other code developers (due diligence in coordination and delegation)
• generating backups
• recording versions
• tracking changes
• experimenting and testing
• and working with agility.

In this bite episode of the DataCafé we talk about these motivators for version control and how it can strengthen your code development and teamwork in building a data science model, pipeline or product.
Further reading:

• Version control via Wikipedia https://en.wikipedia.org/wiki/Version_control
• git-scm via https://git-scm.com/
• "Version Control & Git" by Jason Byrne via Slideshare https://www.slideshare.net/JasonByrne6/version-control-git-86928367
• "Learn git" via codecademy https://www.codecademy.com/learn/learn-git
• "Become a git guru" via Atlassian https://www.atlassian.com/git/tutorials
• Gitflow workflow via Atlassian https://www.atlassian.com/git/tutorials/comparing-workflows/gitflow-workflow
• "A successful git branching model" by Vincent Dressian https://nvie.com/posts/a-successful-git-branching-model/
• Branching strategies via GitVersion https://gitversion.net/docs/learn/branching-strategies/

Recording date: 21 April 2022

Thanks for joining us in the DataCafé. You can follow us on twitter @DataCafePodcast and feel free to contact us about anything you've heard here or think would be an interesting topic in the future.

We explore one of the key issues around Deep Learning Neural Networks - how can you prove that your neural network will perform correctly? Especially if the neural network in question is at the heart of a mission-critical application, such as making a real-time control decision in an autonomous car. Similarly, how can you establish if you've trained your neural network at the heart of a loan decision agent with a prebuilt bias? How can you be sure that your black box is going to adapt to critical new situations?
We speak with Prof. Alessio Lomuscio about how Mixed Integer Linear Programs (MILPs) and Symbolic Interval Propagation can be used to capture and solve verification problems in large Neural Networks. Prof. Lomuscio leads the Verification of Autonomous Systems Group in the Dept. of Computing at Imperial College; their results have shown that verification is feasible for models in the millions of tunable parameters, which was previously not possible. Tools like VENUS and VeriNet, developed in their lab, can verify key operational properties in Deep Learning Networks and this has a particular relevance for safety-critical applications in e.g. the aviation industry, medical imaging and autonomous transportation. Particularly importantly, given that neural networks are only as good as the training data that they have learned from, it is also possible to prove that a particular defined bias does or does not exist for a given network. This latter case is, of course, important for many social or industrial applications: being able to show that a decisioning tool treats people of all genders, ethnicities and abilities equitably.
Interview Guest

Our interview guest Alessio Lomuscio is Professor of Safe Artificial Intelligence in the Department of Computing at Imperial College London. Anyone wishing to contact Alessio about his team's verification technology can do so via his Imperial College website, or via the Imperial College London spin-off Safe Intelligence that will be commercialising the AI verification technology in the future.
Further Reading

• Publication list for Prof. Alessio Lomuscio (via Imperial College London)
• Paper on Formal Analysis of Neural Network-based Systems in the Aircraft Domain using the VENUS tool (via Imperial College London)
• Paper on Scalable Complete Verification of ReLU Neural Networks via Dependency-based Branching (via IJCAI.org)
• Paper on DEEPSPLIT: An Efficient Splitting Method for Neural Network Verification via Indirect Effect Analysis (via IJCAI.org)
• Team: Verification of Autonomous Systems Group, Department of Computing, Imperial College London
• Tools: VENUS and VeriNet

Some links above may require payment or login. We are not endorsing them or receiving any payment for mentioning them. They are provided as is. Often free versions of papers are available and we would encourage you to inve

Thanks for joining us in the DataCafé. You can follow us on twitter @DataCafePodcast and feel free to contact us about anything you've heard here or think would be an interesting topic in the future.