Time flies- even with the extra day, February felt pretty short…
Anyway, here’s round 2 of the Royal Statistical Society Data Science Section monthly newsletter- 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 sign up for future updates here:
Industrial Strength Data Science March 2020 NewsletterRSS Data Science Section
Section and Member Activities
Jim Weatherall is hosting our next RSS DSS event, which is in Manchester on the 18th March. It will be an expert panel discussion focused on skills and ethics for modern data science- sign up for free tickets here
Danielle Belgrave has a busy few weeks coming up!
She is co-organising the Advances in Data Science event – more info here – in Manchester (June 22-23) where Anjali Mazumder is a keynote speaker.
In addition she is tutorial chair for NeurIPS – any tutorial proposals from the community would be very welcome.
Finally, she is giving an upcoming talk (March 12th) at an Imperial college diversity event with other women in AI including 2 other panelists and speakers from DeepMind (Marta Garnelo and Laura Weidinger). More info here.
Posts We Like
As we collectively plough on with leaving the EU, it was interesting to see the EU’s take on AI : “Prepare for socio-economic changes brought about by AI”…
On the practical applications of machine learning front, there were a couple of compelling results in the health/pharma area.
- First of all, “Powerful antibiotics discovered using AI” highlights the use of Machine Learning techniques to identify successful (and unforeseen) molecular combinations with antibiotic properties from a sizeable pool of potential candidates.
- Similarly Deep Mind continues to push the boundaries in the protein-solving problem with alpha-fold.
From a tools perspective, some useful recent releases from some of the leading data science companies.
- First of all Facebook has open-sourced their “hiplot” library which makes parallel co-ordinate plots (great for exploring multi-dimensional data) easy to produce.
- In addition Uber has released “manifold” which helps de-bug machine learning models using various easy to use interactive visualisations. This is similar to Google’s What-If tool and highlights the increasing number of out-of-the-box ‘explainability’ options for ML models (in addition to Shap, Lime etc.).
- Finally, is there now a successor for Jupyter Notebooks? DeepNote is building out an alternative with some positive reviews. Also, if managing cloud servers and repositories are an issue, Google’s AI Notebooks are increasingly powerful.
For those into Causality (and everything involved…) this was a good read– “In this post we explain a Bayesian approach to infering the impact of interventions or actions” – although you may need a quiet spot and a bit of time!
Finally, great to see unintended use cases… how about building a chess playing program using GPT2, one of the best NLP models around!
… and Probabilistic Inference in Bayesian Networks has finally entered the mainstream…
We’ve already highlighted a number of events our committee members are involved with above.
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: