Humans set Limits, Machines Crunch Numbers. From #AI to #SmartKnowledge? @turinginst @DameWendyDBE

This was another superb Turing Lecture: AI through the Looking Glass by Dame Wendy Hall DBE at the Knowledge Centre of the British Library, where the Alan Turing Institute is situated.

That’s where American Professor Lenore Blum said that girls only need more support to get into science and technology and she proved this at Carnegie Mellon University where she achieved a 50/50 balance. Women do not need their own curriculum! The support is needed against what’s now called unconscious or cognitive bias.

In that context, Dame Wendy Hall reminded us that Facebook was started as a site by boys rating girls at Harvard! She put her excellent and most interesting historic overview of AI into a ‘socio-technical’ context. Professor Nigel Shadbolt had spoken about Social Machines at the Blackett Memorial Lecture of the Operations Research Society.

Dame Wendy Hall DBE made a few mare salient appeals to our ethical and moral standards. She even wondered whether we need to press RESET and Restart!? But genies can’t be put back into bottles. Hence I can only hope that we can make a difference with our contribution of making Excel charts sexy – besides re-visualising images and time series [not yet ready]. Continue reading

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#3DMetrics for #SmartKnowledge from #Images and Multi-Dimensional Data

Our Software as a Service is not open for business yet. But we do invite people to share our excitement, especially potential technology, business and training partners who can see the value immediately.

For we now have:

These are examples for measuring visually in ‘Visual 3D‘.

Numerical comparisons give us the quantification of qualities:

We live in exciting times, thanks to digital innovations!

© Copyright Sabine K McNeill 1996 – 2017

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Algorithms with Societal Impact and Data Analytics for Human Health @RoyalSociety

The morning’s session began with Professor Karen Yeung from King’s College chairing. She is co-author of Law, Regulation and Technology: the Field, Frame and Focal Questions. Isn’t that interesting for us in the Law Special Interest Group at the British Computer Society?

John Quinn spoke about Machine learning and the humanitarian information gap. He works for UN Global Pulse and thus I learned about UNOSAT – the agency gathering satellite images.

He suggested that

  • A ‘census of buildings’ would be valuable information!
  • The types of buildings would give information about their vulnerability or resilience!

A whole new world opens for our new visualisation styles for images, multi-dimensional data and time series. That’s for sure!

The second talk was given by Professor Kobbi Nissim of Georgetown University on Differential Privacy and how it compares with Legal Standard of Privacy. He distinguished between:- Continue reading

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The Growing Ubiquity of Algorithms in Society: Implications, Impacts and Innovations @RoyalSociety

This event at the Royal Society made me really feel at home!

The first session covered Machine Learning and The Law.

It started with an introduction by Professor Sofia Olhede, one of the organisers, and a presentation by Christina Blacklaws from the Law Society who sits also on the Family Justice Council, on Transparency and Accountability.

Her presentation was followed by Marion Oswald who presented how Durham’s constabulary used forecasting to predict criminality and illustrate Algorithmic Risk Assessment Policing Models. How many people ‘outsource thinking’ and why??? She pointed to these interesting quotes from W. Twining, Preparing Lawyers for the 21st Century (1992) 3 Legal Education Review 1, 14:

Most lawyers are innumerate and most law students are terrified of figures.

It means that any number we claim for a ‘probability’ is constructed by us based on what we know.

Risk in this sense is a measure of what don’t and can’t know as much as a measure of what we can.

Continue reading

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Fifty Years of Learning from #Data Prof. Robert Calderbank #TuringLecture @TuringInst

Professor Robert Calderbank came from Duke University in North Carolina to deliver this superb overview over the past 50 years and how data science is the result of that development.

Having created the Information Initiative @ Duke, he presented the Data+ program that attracts undergraduates in an interdisciplinary way and prepares them for their careers. 

He ‘sells’ them as teams of three rather than as individuals to employers!

In this slide, he shows the three levels defining data science on the right:

  1. Marshaling;
  2. Analysing;
  3. Visualising and Communicating.

On the left he shows the ‘functionality’ or ‘technique’ used, to place projects: Continue reading

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From Computing to Visualising or from Numbers to Pixels @AlanTuringInst

Recently I had the great pleasure of listening to Prof. Lenore Blum‘s talk about Alan Turing and the Other Theory of Computation at the Knowledge Centre of the British Library.

For me, this talk was particularly significant, because it juxtaposed:-

  1. Turing with Newton;
  2. Logic / Computer Science with Numerical Analysis / Scientific Computing;
  3. computable numbers with the intrinsic size of an input word.

Professor Blum also presented

  • Computing as a Lens on the Sciences.

During her life Carnegie Mellon University, she has created fabulous opportunities for women to enter STEM, Science, Technology, Engineering and Maths. And she is now also involved in the start-up scene.

Her thinking made me realise that my software methods are moving

  • from computing numbers to visualising them.

And instead of looking at input words and their sizes, I’m displaying numbers with pixel accuracy.

I wrote Visualisation Pixels – the Link between Digital Bits and Physical Atoms some time ago.

But in future I will write ‘digital creations’ for ‘patrons’ who are willing to sponsor our Smart Knowledge Engine via my new Patreon page. In my view, this is better than crowdfunding, as the creators are DIGITAL.

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