December Newsletter

Hi everyone-

December already… Happy Holidays to everyone! It certainly feels like winter is here judging by the lack of sunlight. But a December like no other, as we have a World Cup to watch – although half empty, beer-less, air-conditioned stadiums in repressive Qatar does not sit well …Perhaps time for a breather, with a wrap up of data science developments in the last month.

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. (If you are reading this on email and it is not formatting well, try viewing online at

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 2022 Newsletter

RSS Data Science Section

Committee Activities

We had an excellent turnout for our Xmas Social on December 1st- so great to see so many lively and enthusiastic data scientists!

Having successfully convened not 1, but 2 entertaining and insightful data science meetups over the last couple of months (“From Paper to Pitch” and “IP Freely, making algorithms pay – Intellectual property in Data Science and AI“) – huge thanks to Will Browne! – we have another meetup planned for December 15th, 7-830pm – “Why is AI in healthcare not working” – sign up here

The RSS is now accepting applications for the Advanced Data Science Professional certification, awarded as part of our work with the Alliance for Data Science Professionals – more details here.

The AI Standards Hub, led by committee member Florian Ostmann, will be hosting a webinar on international standards for AI transparency and explainability on December 8. The event will a published standard (IEEE 7001) as well as two standards currently under development (ISO/IEC AWI 12792 and ISO/IEC AWI TS 6254). A follow-up workshop aimed at gathering input to inform the development of ISO/IEC AWI 12792 and ISO/IEC AWI TS 6254 will take place in January.

We are very excited to announce Real World Data Science, a new data science content platform from the Royal Statistical Society. It is being built for data science students, practitioners, leaders and educators as a space to share, learn about and be inspired by real-world uses of data science. Case studies of data science applications will be a core feature of the site, as will “explainers” of the ideas, tools, and methods that make data science projects possible. The site will also host exercises and other material to
support the training and development of data science skills. Real World Data Science is online at (and on Twitter @rwdatasci). The project team has recently published a call for contributions, and those interested in contributing are invited to contact the editor, Brian Tarran.

Martin Goodson (CEO and Chief Scientist at Evolution AI) continues to run the excellent London Machine Learning meetup and is very active with events. The next event is one not to miss – December 7th when Alhussein Fawzi, Research Scientist at DeepMind, will present AlphaTensor – “Faster matrix multiplication with deep reinforcement learning“. Videos are posted on the meetup youtube channel – and future events will be posted here.

Martin has also compiled a handy list of mastodon handles as the data science and machine learning community migrates away from twitter…

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…

  • How do we assess large language models?
    • Facebook released Galactica – ‘a large language model for science’. On the face of it, this was a very exciting proposition, using the architecture and approach of the likes of GPT-3 but trained on a large scientific corpus of papers, reference material, knowledge bases and many other sources.
    • Sadly, it quickly became apparent that the output of the model could not be trusted- often it got a lot right, but it was impossible to tell right from wrong
    • Stamford’s Human-Centered AI group released a framework to try and tackle the problem of evaluating large language models and assessing their risks
"Maybe you don’t mind if GitHub Copi­lot used your open-source code with­out ask­ing.
But how will you feel if Copi­lot erases your open-source com­mu­nity?"
For a few thousand dollars a year, Social Sentinel offered schools across the country sophisticated technology to scan social media posts from students at risk of harming themselves or others. Used correctly, the tool could help save lives, the company said.

For some colleges that bought the service, it also served a different purpose — allowing campus police to surveil student protests.
The KFC promotion read, “It’s memorial day for [Kristallnacht]! Treat yourself with more tender cheese on your crispy chicken. Now at KFCheese!”
  • Some positive news however:
"AlphaFold2 revolutionized structural biology with the ability to predict protein structures with exceptionally high accuracy. Its implementation, however, lacks the code and data required to train new models. These are necessary to (i) tackle new tasks, like protein-ligand complex structure prediction, (ii) investigate the process by which the model learns, which remains poorly understood, and (iii) assess the model's generalization capacity to unseen regions of fold space. Here we report OpenFold, a fast, memory-efficient, and trainable implementation of AlphaFold2, and OpenProteinSet, the largest public database of protein multiple sequence alignments. ”

Developments in Data Science Research…

As always, lots of new developments on the research front and plenty of arXiv papers to read…

"In Emergent abilities of large language models, we defined an emergent ability as an ability that is “not present in small models but is present in large models.” Is emergence a rare phenomena, or are many tasks actually emergent?

It turns out that there are more than 100 examples of emergent abilities that already been empirically discovered by scaling language models such as GPT-3, Chinchilla, and PaLM. To facilitate further research on emergence, I have compiled a list of emergent abilities in this post. "
In this work, we pursue an ambitious goal of translating between molecules and language by proposing two new tasks: molecule captioning and text-guided de novo molecule generation. In molecule captioning, we take a molecule (e.g., as a SMILES string) and generate a caption that describes it. In text-guided molecule generation, the task is to create a molecule that matches a given natural language description 
We analyze the knowledge acquired by AlphaZero, a neural network engine that learns chess solely by playing against itself yet becomes capable of outperforming human chess players. Although the system trains without access to human games or guidance, it appears to learn concepts analogous to those used by human chess players. We provide two lines of evidence. Linear probes applied to AlphaZero’s internal state enable us to quantify when and where such concepts are represented in the network. We also describe a behavioral analysis of opening play, including qualitative commentary by a former world chess champion.
"Results show that tree-based models remain state-of-the-art on medium-sized data (10K samples) even without accounting for their superior speed. To understand this gap, we conduct an empirical investigation into the differing inductive biases of tree-based models and neural networks. This leads to a series of challenges which should guide researchers aiming to build tabular-specific neural network: 1) be robust to uninformative features, 2) preserve the orientation of the data, and 3) be able to easily learn irregular functions."

Stable-Dal-Gen oh my…

Still lots of discussion about the new breed of text-to-image models (type in a text prompt/description and an -often amazing- image is generated) with three main models available right now: DALLE2 from OpenAI, Imagen from Google and the open source Stable-Diffusion from

“Whether it’s legal or not, how do you think this artist feels now that thousands of people can now copy her style of works almost exactly?”
"It’s like a photo booth, but once the subject is captured, it can be synthesized wherever your dreams take you…"

Real world applications of Data Science

Lots of practical examples making a difference in the real world this month!

"Today, we’re announcing a breakthrough toward building AI that has mastered these skills. We’ve built an agent – CICERO – that is the first AI to achieve human-level performance in the popular strategy game Diplomacy*"

"Diplomacy has been viewed for decades as a near-impossible grand challenge in AI because it requires players to master the art of understanding other people’s motivations and perspectives; make complex plans and adjust strategies; and then use natural language to reach agreements with other people, convince them to form partnerships and alliances, and more. CICERO is so effective at using natural language to negotiate with people in Diplomacy that they often favored working with CICERO over other human participants."
  • Finally, great summary post from Jeff Dean at Google highlighting how AI is driving worldwide progress in 3 significant areas: Supporting thousands of languages; Empowering creators and artists; Addressing climate change and health challenges – well worth a read

How does that work?

Tutorials and deep dives on different approaches and techniques

"Contrastive learning is a powerful class of self-supervised visual representation learning methods that learn feature extractors by (1) minimizing the distance between the representations of positive pairs, or samples that are similar in some sense, and (2) maximizing the distance between representations of negative pairs, or samples that are different in some sense. Contrastive learning can be applied to unlabeled images by having positive pairs contain augmentations of the same image and negative pairs contain augmentations of different images."
"Enterprises are full of documents containing knowledge that isn't accessible by digital workflows. These documents can vary from letters, invoices, forms, reports, to receipts. With the improvements in text, vision, and multimodal AI, it's now possible to unlock that information. This post shows you how your teams can use open-source models to build custom solutions for free!"

Practical tips

How to drive analytics and ML into production

"By far, the most expensive, complex, and performant method is a fully realtime ML pipeline; the model runs in realtime, the features run in realtime, and the model is trained online, so it is constantly learning. Because the time, money, and resources required by a fully realtime system are so extensive, this method is infrequently utilized, even by FAANG-type companies, but we highlight it here because it is also incredible what this type of realtime implementation is capable of."
"The reason managers pursued these insane ideas is partly because they are hired despite not having any subject matter expertise in business or the company’s operations, and partly because VC firms had the strange idea that ballooning costs well in excess of revenue was “growth” and therefore good in all cases; the business equivalent of the Flat Earth Society."

Bigger picture ideas

Longer thought provoking reads – lean back and pour a drink! …

“A central goal of recommender systems is to select items according to the “preferences” of their users. “Preferences” is a complicated word that has been used across many disciplines to mean, roughly, “what people want.” In practice, most recommenders instead optimize for engagement. This has been justified by the assumption that people always choose what they want, an idea from 20th-century economics called revealed preference. However, this approach to preferences can lead to a variety of unwanted outcomes including clickbait, addiction, or algorithmic manipulation.

Doing better requires both a change in thinking and a change in approach. We’ll propose a more realistic definition of preferences, taking into account a century of interdisciplinary study, and two concrete ways to build better recommender systems: asking people what they want instead of just watching what they do, and using models that separate motives, behaviors, and outcomes.”
“For instance, as an occasional computer vision researcher, my goal is sometimes to prove that my new image classification model works well. I accomplish this by measuring its accuracy, after asking it to label images (is this image a cat or a dog or a frog or a truck or a ...) from a standardized test dataset of images. I'm not allowed to train my model on the test dataset though (that would be cheating), so I instead train the model on a proxy dataset, called the training dataset. I also can't directly target prediction accuracy during training1, so I instead target a proxy objective which is only related to accuracy. So rather than training my model on the goal I care about — classification accuracy on a test dataset — I instead train it using a proxy objective on a proxy dataset."
"But the best is yet to come. The really exciting applications will be action-driven, where the model acts like an agent choosing actions. And although academics can argue all day about the true definition of AGI, an action-driven LLM is going to look a lot like AGI."
"However, I worry that many startups in this space are focusing on the wrong things early on. Specifically, after having met and looked into numerous companies in this space, it seems that UX and product design is the predominant bottleneck holding back most applied large language model startups, not data or modeling"
"We are rapidly pursuing the industrialization of biotech. Large-scale automation now powers complex bio-foundries. Many synthetic biology companies are hellbent on scaling production volumes of new materials. A major concern is the shortage of bioreactors and fermentation capacity. While these all seem like obvious bottlenecks for the Bioeconomy, what if they aren’t? What if there is another way? Here, I’ll explore a different idea: the biologization of industry."

Fun Practical Projects and Learning Opportunities

A few fun practical projects and topics to keep you occupied/distracted:

Covid Corner

Apparently Covid is over – certainly there are very limited restrictions in the UK now

Updates from Members and Contributors

  • Mia Hatton from the ONS Data Science campus is looking for feedback from any government and public sector employees who have an interest in data science to help shape the future of the committee- check out the survey here (you can also join the mailing list here).
  • Fresh from the success of their ESSnet Web Intelligence Network webinars, the ONS Data Science campus have another excellent set of webinars coming up:
    • 24 Jan’23 – Enhancing the Quality of Statistical Business Registers with Scraped Data. This webinar will aim to inspire and equip participants keen to use web-scraped information to enhance the quality of the Statistical Business Registers. Sign up here
    • 23 Feb’23 – Methods of Processing and Analysing of Web-Scraped Tourism Data. This webinar will discuss the issues of data sources available in tourism statistics. We will present how to search for new data sources and how to analyse them. We will review and apply methods for merging and combining the web scraped data with other sources, using various programming environments. Sign up here


The Job market is a bit quiet over the summer- let us know if you have any openings you’d like to advertise

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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