The Scary World of Blogging

This is the post excerpt.

This is my first blog and the thought of doing it is absolutely terrifying! To be honest I am not even sure I am doing this correctly so chances are it will never see the light of day. So apologies for any rambling and thank you for being guinea pigs in my first ever blog.

To put my anxiety in a bit of context I shall freely admit that I only signed up to Twitter last week (and have yet to utter a Tweet – still don’t know how) and I have only had a smart phone since May. All very unusual for a library & information professional you will probably think, but that is why I am here at the marvellous CityLis – to learn things and to do things and to get out of my law library, hard copy comfort zone. Just one week in and I have already been inspired by the fabulous Lyn Robinson and my equally fabulous classmates who shine with enthusiasm and intelligence.

I do feel a bit on the back foot though having spent the days since last Monday in New Haven in the US.  I was there to meet and help settle in the new US librarian for the law firm where I work . I have never been to the US before and it was an interesting experience. What was particularly pleasing was the excitement with which my new US colleague was greeted by the US lawyers and in what high esteem the library and information professional seems to be held in the US – or in New Haven at least! It is the home of Yale University so maybe that has something to do with it. I have also never had such big meals in my entire life.

Anyway, I shall end my test blog by attempting to add a picture of the outside of Yale University library (they wouldn’t let me in sadly!)

Thanks for reading!




Bringing the data alive: measuring, analysing, and the rise of the machines.

I can’t believe that we are nearly at the end of this fascinating journey. Ten weeks have gone by so fast and I have learnt so much that it will be good to step back and process all the information. In the last few sessions, after  concentrating on the structure of information and metadata, we have moved on to how we can measure and analyse the data, and from there we have taken a leap into the world of AI.

Measuring, exploring and analysing the data is to make the data work for us. By measuring the impact of an article or piece of research, whether it is by the more traditional way of counting citations or new web based techniques such as altmetrics we can find out what impact an article has had and what people are talking about. The new techniques allow measurement also of what impact is being made across all forms of social media such as blogs and Twitter. They can also indicate new ways to share information by seeing how other people share and what formats and forums they use. As with any form of measurement we need to be aware of the pitfalls, of not looking critically at what is produced – but it is certainly a very useful starting point.

We then investigated deeper, into the tools available not just for measuring but for exploring and analyzing. Tools for getting meaning – coding with python to link things together or analyse different data sets, to be able to use the information and find out what you need to know, even if the data is not set up in such a way as to tell you! More fun things such as word clouds give a visual interpretation of the information – easy to grasp and digest and can make a big impact, even if the information is limited.

The most interesting topic, possibly because it is the headline grabbing one  is Artificial Intelligence. Whilst ethical awareness should be present in every aspect we have looked at, it is in the topic of AI that it seems most necessary and urgent. Fears range from terminator style scenarios fed by popular culture to real fears of possible job losses. A recent article from the Daily Telegraph (‘UK ‘not ready for the next industrial revolution’ and rise of the robots’ by Lauren Davidson, 28 Nov 2016 ) quotes a report produced by Deloitte which states that 35% of UK jobs at high risk of automation in the next 10 to 20 years.

There are fun things too. The music industry is one keen adopter. Streaming services such as Spotify already use data analysis to make recommendations. Quantone, a London- based start-up is using the IBM Watson engine to improve their recommendations by analysing huge amounts of data including tweets, blogs and online reviews (see FT.com article ‘Rise of the robot music industry’ by Nic Fildes, 2 Dec 2016). There is even an AI generated Xmas song this year! As reported in the Guardian on 29 November (‘It’s no Christmas No. 1 but AI-generated song brings festive cheer to researchers’ by Ian Sample) the Neural Karaoke project from the University of Toronto fed a Christmas picture into a computer program and it generated Christmassy lyrics and music as well.

But for all the fun and usefulness of music recommendations, Twitter bots managing your Twitter accounts, and handy helpers like Siri and Amazon Echo there are sinister undertones. Another article from the FT (‘Algorithmic discrimination‘ by Izabella Kaminska, 29 Nov 2016)  discusses the idea that discrimination is the single biggest problem facing the artificial intelligence field. An algorithm cannot judge exceptions and does not have the ability to ‘..respect a human’s capacity to change, better himself or hold contradictory view points at the same time’. This danger is further highlighted by reports that researchers at Shanghai Jiao Tong University have created a machine that can identify criminals by assessing their eyes, nose and mouth, supporting the dangerous view that criminals have certain facial features (‘Minority Report-style AI learns to predict if people are criminals from their facial features‘ by Cara McGoogan, 24 Nov 2016) This all sounds worrying, and in parts of the World where surveillance is everywhere and freedoms are restricted it is worrying and a real cause for concern for human rights watchers. But the AI isn’t creating the problems, true that it is feeding them and enhancing them but it is still human beings that are creating the tools.

The fears aren’t new and the problems aren’t really new either. Any problems or fears come down to the questions of what can we do, how can we do it and should we do it and also what we allow ourselves to do and how we treat our fellow human beings. AI is just another tool, albeit an exciting one. An understanding of ethics and and ethical thinking and the need to discuss and be open about ethical problems needs to be at the starting point of everything. It needs to be part of our education, the need is to instil ethical thinking and discussion at school age, before over enthusiasm, economics and, potentially, greed kick in.



Sources: Financial Times, 23 November 2016; Telegraph.co.uk, 24 November 2016; The Observer, 27 November 2016; Daily Telegraph, 28 November 2016; The Guardian, 29 November 2016; FT.com, 29 November 2016; FT.com, 2 December 2016; The Guardian, 2 December 2016.


The Big Bang of Big Data – Reflections on the first two weeks of DITA


The first two weeks have been fascinating and I feel that my brain has already expanded with all the information. Big data, how big it is, the speed it is being created and how we can engage with it and use it and can be used by it.

Finding the I in data – the information and the individual. The amount of data we now produce, especially unstructured data, is staggering and more than slightly scary. Ethical issues abound, from who owns the data to who can access it. Is all data up for grabs or should some data be private? How can it even be monitored and should be monitored? The Google ‘right to be forgotten’ case  from 2014 shows that it is definitely a sensitive issue but the ECJ found for privacy.This is just one issue among many, some of which we haven’t even thought about yet.

Also whilst we may not be the ones who are analysing data, the ones to turn it into information, as users we still need to be aware of its ambiguities, and our own potential misconceptions and prejudices too.

What does it mean for us as library and information professionals? In the day to day role we can use data to provide a better service for users. Where I work we  gather statistics to track database usage for both commercial reasons, to use in renewal negotiations with suppliers, but also to identify training needs and so help users better find the information they require. A more hopeful answer is that we facilitate the step from information to knowledge for our users and for ourselves.

In the second session I did find it very helpful to have a whizz through the development of computers from Ada Lovelace, to Alan Turing, to the Manchester Baby. To see pictures of large physical machines somehow made the small devices we have today make more sense. On looking at the additional resources I was intrigued to find out  that the first commercial computer  was developed by J. Lyons & Co. in 1951, cake makers and owners of a nationwide chain of tea shops. Lyons Electronic Office ended up being used to calculate the payroll for the firm.

What I found uplifting and positive was the collaborative nature of the early days of the internet. How there was sharing of knowledge and cooperation between all the different agencies, military, scientific and educational. The true knowledge sharing spirit of what internet should be before commercial concerns get in the mix.

I look forward to the next sessions.

Sources: https://www.theguardian.com/technology/2014/may/14/explainer-right-to-be-forgotten-the-newest-cultural-shibboleth