December Newsletter

Hi everyone-

Properly dark and cold now in the UK, and even some initial sightings of Christmas trees so it must be getting to the end of year… perhaps time for some satisfying data science reading materials while pondering what present to buy for your long lost auntie!

Following is the December edition of our Royal Statistical Society Data Science and AI Section newsletter. Hopefully some interesting topics and titbits to feed your data science curiosity.

As always- any and all feedback most welcome! If you like these, do please send on to your friends- we are looking to build a strong community of data science practitioners. And if you are not signed up to receive these automatically you can do so here.

Industrial Strength Data Science December 2021 Newsletter

RSS Data Science Section

Committee Activities

We are all conscious that times are incredibly hard for many people and are keen to help however we can- if there is anything we can do to help those who have been laid-off (networking and introductions help, advice on development etc.) don’t hesitate to drop us a line.

On Tuesday 23rd November we hosted our latest event “The National AI Strategy – boom or bust to your career in data science?” and it was another great success with a strong turnout.

  • First of all Seb Krier, Senior Technology Policy Researcher at the Stanford University Cyber Policy Centre, gave an excellent overview of the published National AI strategy using his extensive experience to provide insight into the strengths and weaknesses of the different focus areas, and how it compares to different approaches around the world.
  • Next, Adam Davison and Martin Goodson talked through the results of our recent data science practitioner survey on the government strategy proposals, highlighting areas of discrepancy and omission.
  • We then finished with a lively round-table discussion, additionally including Stian Westlake, Chief Executive of the RSS and Janet Bastiman, Chief Data Scientist at Napier AI.

We will publish a more detailed review and video in the coming weeks for those who missed out.

If anyone is interested in getting more involved in this discussion, we are collaborating with the UK Government’s Office for AI to host a roundtable event on AI Governance and Regulation which is one of the 3 main pillars of the UK AI Strategy. We are seeking Data Science and AI experts and practitioners to participate – please express any interest by emailing weatheralljames@hotmail.com.

Many congratulations to DSS section committee’s Rich Pugh who has been elected to the RSS Council – joining the DSS’s Anjali Mazumder and Jim Weatherall… all part of our cunning plan for global domination!

Martin Goodson continues to run the excellent London Machine Learning meetup and is very active in with events. The last talk was on October 27th where Anees Kazi, senior research scientist at the chair of Computer Aided Medical Procedure and Augmented Reality (CAMPAR) at Technical University of Munich, discussed “Graph Convolutional Networks for Disease Prediction“. Videos are posted on the meetup youtube channel – and future events will be posted here.

This Month in Data Science

Lots of exciting data science going on, as always!

Ethics and more ethics…
Bias, ethics and diversity continue to be hot topics in data science…

"This change will represent one of the largest shifts in facial recognition usage in the technology’s history. More than a third of Facebook’s daily active users have opted in to our Face Recognition setting and are able to be recognized, and its removal will result in the deletion of more than a billion people’s individual facial recognition templates."
For example, asking AI to cure cancer as quickly as possible could be dangerous. “It would probably find ways of inducing tumours in the whole human population, so that it could run millions of experiments in parallel, using all of us as guinea pigs,” said Russell. “And that’s because that’s the solution to the objective we gave it; we just forgot to specify that you can’t use humans as guinea pigs and you can’t use up the whole GDP of the world to run your experiments and you can’t do this and you can’t do that.”

Developments in Data Science…
As always, lots of new developments…

“The brain is able to use information coming from the skin as if it were coming from the eyes. We don’t see with the eyes or hear with the ears, these are just the receptors, seeing and hearing in fact goes on in the brain.”
"This trend of massive investments of dozens of millions of dollars going into training ever more massive AI models appears to be here to stay, at least for now. Given these models are incredibly powerful this is very exciting, but the fact that primarily corporations with large monetary resources can create these models is worrying"

Real world applications of Data Science
Lots of practical examples making a difference in the real world this month!

“Biology is likely far too complex and messy to ever be encapsulated as a simple set of neat mathematical equations. But just as mathematics turned out to be the right description language for physics, biology may turn out to be the perfect type of regime for the application of AI.”
“There was no problem with the algorithm as long as they stay within the boundaries of the business model and buy cookie-cutter homes that are easier to sell. There are a lot of things that affect the valuation of homes that even very sophisticated algorithms cannot catch"

How does that work?
A new section on understanding different approaches and techniques

"Before we start, just a heads-up. We're going to be talking a lot about matrix multiplications and touching on backpropagation (the algorithm for training the model), but you don't need to know any of it beforehand. We'll add the concepts we need one at a time, with explanation.."
For example, speech recognition systems need to disambiguate between phonetically similar phrases like “recognize speech” and “wreck a nice beach”, and a language model can help pick the one that sounds the most natural in a given context. For instance, a speech recognition system transcribing a lecture on audio systems should likely prefer "recognize speech", whereas a news flash about an extraterrestrial invasion of Miami should likely prefer "wreck a nice beach".
"But I am going to define this stuff three times. Once for mum, once for dad, and once for the country."

Practical tips
How to drive analytics and ML into production

  • We’ve previously highlighted the importance of MLOps and the standardisation of processes for updating and monitoring ML models in production. Another good podcast on the ‘The Data Exchange’ this time about ML Ops Anti-Patterns (the underlying research paper is here)
  • Speaking of MLOps – excellent summary of the platforms used across the big players, highlighting how much is still ‘home grown’ (labeled ‘IH’ below)
"Machine learning systems are extremely complex, and have a frustrating ability to erode abstractions between software components. This presents a wide array of challenges to the kind of iterative development that is essential for ML success.”

Bigger picture ideas
Longer thought provoking reads – a few more than normal, lean back and pour a drink!

"Abundant evidence and decades of sustained research suggest that the brain cannot simply be assembling sensory information, as though it were putting together a jigsaw puzzle, to perceive its surroundings. This is borne out by the fact that the brain can construct a scene based on the light entering our eyes, even when the incoming information is noisy and ambiguous."
"I would love to incorporate deep learning into the design, manufacturing, and operations of our aircraft. But I need some guarantees."

Practical Projects and Learning Opportunities
As always here are a few potential practical projects to keep you busy:

Covid Corner

As we head into winter, we continue to experience the conflicting emotions of relaxing regulations and behaviour with increasing Covid prevalence and hospitals at breaking point. And now there is a news of a new variant…

"Whatever the reason, by half-term, only around 16 per cent of vaccinations in the cohort had been achieved. Meanwhile, school-age kids had caught Covid by the truckload. Over 7 per cent of the entire Year 7 to Year 11 cohort was infected on any day in the last week of October alone. Maybe that was the unspoken plan. Certainly the JCVI’s minutes – released at the end of October after lengthy delays – make grim reading in this respect. The idea, already noted, that “natural infection” might be better than vaccination for young people was under discussion even here. Somehow, catching Covid was proffered as a better way of not getting ill with Covid than preventing its worst effects with a proven vaccine."

Updates from Members and Contributors

  • Professor Harin Sellahewa reports that nearly 50 of the University of Buckingham’s first ever master’s level data science apprentices have graduated. The Integrated Master’s level Degree Apprenticeship course was set up two years ago to help address an urgent shortage of people with advanced digital skills and to produce expert data scientists by giving them the technological and business skills to transform their workplace. The graduates receive the MSc in Applied Data Science from Buckingham as well as the Level 7 Digital and Technology Solutions Specialist degree apprenticeship certificate from ESFA. The apprenticeship is provided in partnership with AVADO who work with businesses to train staff to develop the skills needed to compete in a digital world. Industry partners such as IBM, Tableau, TigerGraph and Zizo conducted practical workshops for the learners.

Again, hope you found this useful. Please do send on to your friends- we are looking to build a strong community of data science practitioners- and sign up for future updates here.

– Piers

The views expressed are our own and do not necessarily represent those of the RSS

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